From caf514b972460e103dc69bbb561152b161aced81 Mon Sep 17 00:00:00 2001
From: William Qi <wqi@argo.ai>
Date: Thu, 17 Mar 2022 02:16:54 -0400
Subject: [PATCH] AV2 API v0.1.0 release

---
 .darglint                                     |     3 +
 .github/pull_request_template.md              |    21 +
 .github/workflows/ci.yml                      |    46 +
 .gitignore                                    |   158 +
 .pre-commit-config.yaml                       |    14 +
 CITATION.cff                                  |    65 +
 DOWNLOAD.md                                   |    38 +
 LICENSE                                       |    21 +
 NOTICE                                        |    60 +
 README.md                                     |   124 +
 conda/environment.yml                         |    17 +
 conda/install.sh                              |    13 +
 noxfile.py                                    |   140 +
 pyproject.toml                                |    24 +
 setup.cfg                                     |    39 +
 src/av2/__init__.py                           |     5 +
 src/av2/datasets/__init__.py                  |     3 +
 src/av2/datasets/lidar/README.md              |    73 +
 src/av2/datasets/lidar/__init__.py            |     3 +
 src/av2/datasets/lidar/splits.py              | 20014 ++++++++++++++++
 src/av2/datasets/motion_forecasting/README.md |    68 +
 .../datasets/motion_forecasting/__init__.py   |     3 +
 .../motion_forecasting/data_schema.py         |   124 +
 .../motion_forecasting/eval/__init__.py       |     3 +
 .../motion_forecasting/eval/metrics.py        |    58 +
 .../scenario_serialization.py                 |   176 +
 .../motion_forecasting/viz/__init__.py        |     3 +
 .../viz/scenario_visualization.py             |   249 +
 src/av2/datasets/sensor/README.md             |   239 +
 src/av2/datasets/sensor/__init__.py           |     3 +
 .../datasets/sensor/av2_sensor_dataloader.py  |   410 +
 src/av2/datasets/sensor/constants.py          |    70 +
 src/av2/datasets/sensor/sensor_dataloader.py  |   516 +
 src/av2/datasets/sensor/splits.py             |  1012 +
 src/av2/datasets/sensor/utils.py              |    29 +
 src/av2/datasets/tbv/README.md                |   139 +
 src/av2/datasets/tbv/splits.py                |     4 +
 src/av2/geometry/__init__.py                  |     3 +
 src/av2/geometry/camera/__init__.py           |     3 +
 src/av2/geometry/camera/pinhole_camera.py     |   427 +
 src/av2/geometry/geometry.py                  |   295 +
 src/av2/geometry/infinity_norm_utils.py       |    42 +
 src/av2/geometry/interpolate.py               |   241 +
 src/av2/geometry/mesh_grid.py                 |    45 +
 src/av2/geometry/polyline_utils.py            |   211 +
 src/av2/geometry/se3.py                       |    99 +
 src/av2/geometry/sim2.py                      |   209 +
 src/av2/map/README.md                         |   146 +
 src/av2/map/__init__.py                       |     3 +
 src/av2/map/drivable_area.py                  |    40 +
 src/av2/map/lane_segment.py                   |   177 +
 src/av2/map/map_api.py                        |   680 +
 src/av2/map/map_primitives.py                 |    83 +
 src/av2/map/pedestrian_crossing.py            |    66 +
 src/av2/rendering/__init__.py                 |     3 +
 src/av2/rendering/color.py                    |    47 +
 src/av2/rendering/map.py                      |   267 +
 src/av2/rendering/ops/__init__.py             |     3 +
 src/av2/rendering/ops/draw.py                 |   197 +
 src/av2/rendering/rasterize.py                |   156 +
 src/av2/rendering/vector.py                   |   136 +
 src/av2/rendering/video.py                    |   137 +
 src/av2/structures/__init__.py                |     3 +
 src/av2/structures/cuboid.py                  |   382 +
 src/av2/structures/ndgrid.py                  |   157 +
 src/av2/structures/sweep.py                   |    91 +
 src/av2/structures/timestamped_image.py       |    24 +
 src/av2/utils/__init__.py                     |     2 +
 src/av2/utils/constants.py                    |    19 +
 src/av2/utils/dataclass.py                    |    72 +
 src/av2/utils/dense_grid_interpolation.py     |    64 +
 src/av2/utils/depth_map_utils.py              |    71 +
 src/av2/utils/dilation_utils.py               |    41 +
 src/av2/utils/helpers.py                      |    24 +
 src/av2/utils/io.py                           |   233 +
 src/av2/utils/metric_time.py                  |    31 +
 src/av2/utils/raster.py                       |    49 +
 src/av2/utils/synchronization_database.py     |   224 +
 src/av2/utils/typing.py                       |    16 +
 tests/conftest.py                             |    13 +
 .../motion_forecasting/eval/test_metrics.py   |   116 +
 .../test_scenario_serialization.py            |    68 +
 .../sensor/test_av2_sensor_dataloader.py      |    54 +
 .../datasets/sensor/test_sensor_dataloader.py |    94 +
 .../annotations.feather                       |   Bin 0 -> 437218 bytes
 tests/geometry/test_geometry.py               |   524 +
 tests/geometry/test_interpolate.py            |   623 +
 tests/geometry/test_pinhole_camera.py         |   599 +
 tests/map/test_drivable_area.py               |    32 +
 tests/map/test_lane_segment.py                |    55 +
 tests/map/test_map_api.py                     |   348 +
 tests/rendering/ops/test_draw.py              |   146 +
 tests/rendering/test_color.py                 |    16 +
 tests/rendering/test_map.py                   |    50 +
 tests/rendering/test_video.py                 |    91 +
 tests/structures/test_cuboid.py               |   252 +
 tests/structures/test_ndgrid.py               |    89 +
 tests/structures/test_sweep.py                |    33 +
 tests/test_data/a_Sim2_b.json                 |     1 +
 tests/test_data/a_Sim2_b___invalid.json       |     1 +
 ..._0a1e6f0a-1817-4a98-b02e-db8c9327d151.json |     1 +
 ...1e6f0a-1817-4a98-b02e-db8c9327d151.parquet |   Bin 0 -> 123374 bytes
 .../city_SE3_egovehicle.feather               |   Bin 0 -> 161634 bytes
 .../sensors/lidar/315973157959879000.feather  |   Bin 0 -> 1048066 bytes
 .../calibration/egovehicle_SE3_sensor.feather |   Bin 0 -> 4586 bytes
 .../sensors/lidar/315968663259918000.feather  |   Bin 0 -> 3106 bytes
 .../calibration/egovehicle_SE3_sensor.feather |   Bin 0 -> 4586 bytes
 .../test_log/calibration/intrinsics.feather   |   Bin 0 -> 5330 bytes
 ...P__2020-07-21-Z1F0076___img_Sim2_city.json |     1 +
 ...1-Z1F0076_ground_height_surface____MIA.npy |   Bin 0 -> 3345800 bytes
 ...t5aN5rSYjQidJXvGP__2020-07-21-Z1F0076.json |   237 +
 tests/utils/test_dense_grid_interpolation.py  |    96 +
 tests/utils/test_depth_map_utils.py           |    31 +
 tests/utils/test_dilate_utils_unit.py         |    83 +
 tests/utils/test_infinity_norm_utils.py       |    57 +
 tests/utils/test_io.py                        |    40 +
 tests/utils/test_mesh_grid.py                 |   102 +
 tests/utils/test_metric_time.py               |    37 +
 tests/utils/test_polyline_utils.py            |   100 +
 tests/utils/test_raster.py                    |   192 +
 tests/utils/test_se3.py                       |   210 +
 tests/utils/test_sim2.py                      |   314 +
 tutorials/generate_egoview_overlaid_lidar.py  |   198 +
 .../generate_egoview_overlaid_vector_map.py   |   343 +
 ...ate_forecasting_scenario_visualizations.py |   122 +
 tutorials/generate_per_camera_videos.py       |   113 +
 tutorials/map_teaser_notebook.py              |   372 +
 tutorials/map_tutorial.ipynb                  |   348 +
 tutorials/untar_tbv.py                        |    89 +
 129 files changed, 35497 insertions(+)
 create mode 100644 .darglint
 create mode 100644 .github/pull_request_template.md
 create mode 100644 .github/workflows/ci.yml
 create mode 100644 .gitignore
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 CITATION.cff
 create mode 100644 DOWNLOAD.md
 create mode 100644 LICENSE
 create mode 100644 NOTICE
 create mode 100644 README.md
 create mode 100644 conda/environment.yml
 create mode 100644 conda/install.sh
 create mode 100644 noxfile.py
 create mode 100644 pyproject.toml
 create mode 100644 setup.cfg
 create mode 100644 src/av2/__init__.py
 create mode 100644 src/av2/datasets/__init__.py
 create mode 100644 src/av2/datasets/lidar/README.md
 create mode 100644 src/av2/datasets/lidar/__init__.py
 create mode 100644 src/av2/datasets/lidar/splits.py
 create mode 100644 src/av2/datasets/motion_forecasting/README.md
 create mode 100644 src/av2/datasets/motion_forecasting/__init__.py
 create mode 100644 src/av2/datasets/motion_forecasting/data_schema.py
 create mode 100644 src/av2/datasets/motion_forecasting/eval/__init__.py
 create mode 100644 src/av2/datasets/motion_forecasting/eval/metrics.py
 create mode 100644 src/av2/datasets/motion_forecasting/scenario_serialization.py
 create mode 100644 src/av2/datasets/motion_forecasting/viz/__init__.py
 create mode 100644 src/av2/datasets/motion_forecasting/viz/scenario_visualization.py
 create mode 100644 src/av2/datasets/sensor/README.md
 create mode 100644 src/av2/datasets/sensor/__init__.py
 create mode 100644 src/av2/datasets/sensor/av2_sensor_dataloader.py
 create mode 100644 src/av2/datasets/sensor/constants.py
 create mode 100644 src/av2/datasets/sensor/sensor_dataloader.py
 create mode 100644 src/av2/datasets/sensor/splits.py
 create mode 100644 src/av2/datasets/sensor/utils.py
 create mode 100644 src/av2/datasets/tbv/README.md
 create mode 100644 src/av2/datasets/tbv/splits.py
 create mode 100644 src/av2/geometry/__init__.py
 create mode 100644 src/av2/geometry/camera/__init__.py
 create mode 100644 src/av2/geometry/camera/pinhole_camera.py
 create mode 100644 src/av2/geometry/geometry.py
 create mode 100644 src/av2/geometry/infinity_norm_utils.py
 create mode 100644 src/av2/geometry/interpolate.py
 create mode 100644 src/av2/geometry/mesh_grid.py
 create mode 100644 src/av2/geometry/polyline_utils.py
 create mode 100644 src/av2/geometry/se3.py
 create mode 100644 src/av2/geometry/sim2.py
 create mode 100644 src/av2/map/README.md
 create mode 100644 src/av2/map/__init__.py
 create mode 100644 src/av2/map/drivable_area.py
 create mode 100644 src/av2/map/lane_segment.py
 create mode 100644 src/av2/map/map_api.py
 create mode 100644 src/av2/map/map_primitives.py
 create mode 100644 src/av2/map/pedestrian_crossing.py
 create mode 100644 src/av2/rendering/__init__.py
 create mode 100644 src/av2/rendering/color.py
 create mode 100644 src/av2/rendering/map.py
 create mode 100644 src/av2/rendering/ops/__init__.py
 create mode 100644 src/av2/rendering/ops/draw.py
 create mode 100644 src/av2/rendering/rasterize.py
 create mode 100644 src/av2/rendering/vector.py
 create mode 100644 src/av2/rendering/video.py
 create mode 100644 src/av2/structures/__init__.py
 create mode 100644 src/av2/structures/cuboid.py
 create mode 100644 src/av2/structures/ndgrid.py
 create mode 100644 src/av2/structures/sweep.py
 create mode 100644 src/av2/structures/timestamped_image.py
 create mode 100644 src/av2/utils/__init__.py
 create mode 100644 src/av2/utils/constants.py
 create mode 100644 src/av2/utils/dataclass.py
 create mode 100644 src/av2/utils/dense_grid_interpolation.py
 create mode 100644 src/av2/utils/depth_map_utils.py
 create mode 100644 src/av2/utils/dilation_utils.py
 create mode 100644 src/av2/utils/helpers.py
 create mode 100644 src/av2/utils/io.py
 create mode 100644 src/av2/utils/metric_time.py
 create mode 100644 src/av2/utils/raster.py
 create mode 100644 src/av2/utils/synchronization_database.py
 create mode 100644 src/av2/utils/typing.py
 create mode 100644 tests/conftest.py
 create mode 100644 tests/datasets/motion_forecasting/eval/test_metrics.py
 create mode 100644 tests/datasets/motion_forecasting/test_scenario_serialization.py
 create mode 100644 tests/datasets/sensor/test_av2_sensor_dataloader.py
 create mode 100644 tests/datasets/sensor/test_sensor_dataloader.py
 create mode 100644 tests/geometry/data/b87683ae-14c5-321f-8af3-623e7bafc3a7/annotations.feather
 create mode 100644 tests/geometry/test_geometry.py
 create mode 100644 tests/geometry/test_interpolate.py
 create mode 100644 tests/geometry/test_pinhole_camera.py
 create mode 100644 tests/map/test_drivable_area.py
 create mode 100644 tests/map/test_lane_segment.py
 create mode 100644 tests/map/test_map_api.py
 create mode 100644 tests/rendering/ops/test_draw.py
 create mode 100644 tests/rendering/test_color.py
 create mode 100644 tests/rendering/test_map.py
 create mode 100644 tests/rendering/test_video.py
 create mode 100644 tests/structures/test_cuboid.py
 create mode 100644 tests/structures/test_ndgrid.py
 create mode 100644 tests/structures/test_sweep.py
 create mode 100644 tests/test_data/a_Sim2_b.json
 create mode 100644 tests/test_data/a_Sim2_b___invalid.json
 create mode 100644 tests/test_data/forecasting_scenarios/0a1e6f0a-1817-4a98-b02e-db8c9327d151/log_map_archive_0a1e6f0a-1817-4a98-b02e-db8c9327d151.json
 create mode 100644 tests/test_data/forecasting_scenarios/0a1e6f0a-1817-4a98-b02e-db8c9327d151/scenario_0a1e6f0a-1817-4a98-b02e-db8c9327d151.parquet
 create mode 100644 tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/city_SE3_egovehicle.feather
 create mode 100644 tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/sensors/lidar/315973157959879000.feather
 create mode 100644 tests/test_data/sensor_dataset_logs/dummy/calibration/egovehicle_SE3_sensor.feather
 create mode 100644 tests/test_data/sensor_dataset_logs/dummy/sensors/lidar/315968663259918000.feather
 create mode 100644 tests/test_data/sensor_dataset_logs/test_log/calibration/egovehicle_SE3_sensor.feather
 create mode 100644 tests/test_data/sensor_dataset_logs/test_log/calibration/intrinsics.feather
 create mode 100644 tests/test_data/static_maps/dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076/gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076___img_Sim2_city.json
 create mode 100644 tests/test_data/static_maps/dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076/gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076_ground_height_surface____MIA.npy
 create mode 100644 tests/test_data/static_maps/dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076/log_map_archive_dummy_log_map_v2_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076.json
 create mode 100644 tests/utils/test_dense_grid_interpolation.py
 create mode 100644 tests/utils/test_depth_map_utils.py
 create mode 100644 tests/utils/test_dilate_utils_unit.py
 create mode 100644 tests/utils/test_infinity_norm_utils.py
 create mode 100644 tests/utils/test_io.py
 create mode 100644 tests/utils/test_mesh_grid.py
 create mode 100644 tests/utils/test_metric_time.py
 create mode 100644 tests/utils/test_polyline_utils.py
 create mode 100644 tests/utils/test_raster.py
 create mode 100644 tests/utils/test_se3.py
 create mode 100644 tests/utils/test_sim2.py
 create mode 100644 tutorials/generate_egoview_overlaid_lidar.py
 create mode 100644 tutorials/generate_egoview_overlaid_vector_map.py
 create mode 100644 tutorials/generate_forecasting_scenario_visualizations.py
 create mode 100644 tutorials/generate_per_camera_videos.py
 create mode 100644 tutorials/map_teaser_notebook.py
 create mode 100644 tutorials/map_tutorial.ipynb
 create mode 100644 tutorials/untar_tbv.py

diff --git a/.darglint b/.darglint
new file mode 100644
index 00000000..01c1eab9
--- /dev/null
+++ b/.darglint
@@ -0,0 +1,3 @@
+# .darglint
+[darglint]
+strictness = short
\ No newline at end of file
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 00000000..b8f879e0
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,21 @@
+## PR Summary
+<!-- Authors: Add a description immediately below this line, explaining what was done and why it was done, and check the applicable boxes below. -->
+
+## Testing
+<!-- Authors: Add testing details here, explaining your overall strategy, and check the applicable boxes below. -->
+
+In order to ensure this PR works as intended, it is:
+
+* [ ] unit tested.
+* [ ] other or not applicable (*additional detail/rationale required*)
+
+## Compliance with Standards
+<!-- Authors: Check each item below to certify that this PR is ready for review. -->
+
+As the author, I certify that this PR conforms to the following standards:
+
+* [ ] Code changes conform to [PEP8](https://www.python.org/dev/peps/pep-0008) and docstrings conform to the Google Python [style guide](https://google.github.io/styleguide/pyguide.html?showone=Comments#38-comments-and-docstrings).
+* [ ] A well-written summary explains what was done and why it was done.
+* [ ] The PR is adequately tested and the testing details and links to external results are included.
+
+<!-- Authors: If this PR is not ready for review, please create a "Draft Pull Request" using the dropdown below. -->
\ No newline at end of file
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 00000000..b2daa9f2
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,46 @@
+name: CI
+
+on: [push, pull_request]
+
+jobs:
+  test_and_build:
+    name: ${{ matrix.os }}_${{ matrix.venv_backend }}_py${{ matrix.python_version }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [macos-latest, ubuntu-latest, windows-latest]
+        python_version:
+          ["3.8", "3.9", "3.10"]
+        venv_backend:
+          ["virtualenv", "mamba"]  # Testing two different resolvers (pip, mamba).
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - uses: actions/checkout@v2
+      - uses: conda-incubator/setup-miniconda@v2
+        with:
+          mamba-version: "*"
+          miniforge-version: latest
+          python-version: ${{ matrix.python_version }}
+
+      - name: Install prerequisites.
+        run: |
+          mamba install -y nox pip pyyaml
+
+      - name: Run nox with ${{ matrix.venv_backend }}.
+        run: |  # Cache the environments (-r), fail on missing python interpreters (--error-on-missing-interpreters), set dependency resolver backend.
+          python -m nox -r --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }}
+
+      - name: Build wheels.
+        if: ${{ matrix.python_version }} == "virtualenv"
+        run: |
+          python -m pip wheel --wheel-dir=/tmp/wheelhouse .
+          
+      - name: Publish package to PyPI.
+        if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..be38ce6a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,158 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+*.pkl
+
+.vscode/
+outputs/
+condaenv*
+
+/data/
+
+*.jpg
+*.png
+*.svg
+
+.DS_Store
+*/**/.DS_Store
+experiments
+
+*.parquet
+*.pt
+*.mp4
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..97f02ea2
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,14 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: local
+    hooks:
+    -   id: nox
+        name: Run nox.
+        language: python
+        entry: nox -rs black isort mypy
+        pass_filenames: false
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 00000000..97d464b2
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,65 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Wilson"
+  given-names: "Benjamin"
+- family-names: "Qi"
+  given-names: "William"
+- family-names: "Agarwal"
+  given-names: "Tanmay"
+- family-names: "Lambert"
+  given-names: "John"
+- family-names: "Singh"
+  given-names: "Jagjeet"
+- family-names: "Khandelwal"
+  given-names: "Siddhesh"
+- family-names: "Pan"
+  given-names: "Bowen"
+- family-names: "Kumar"
+  given-names: "Ratnesh"
+- family-names: "Hartnett"
+  given-names: "Andrew"
+- family-names: "Pontes"
+  given-names: "Jhony Kaesemodel"
+- family-names: "Ramanan"
+  given-names: "Deva"
+- family-names: "Carr"
+  given-names: "Peter"
+- family-names: "Hays"
+  given-names: "James"
+title: "Argoverse 2 API"
+version: 1.0.0
+date-released: 2022-03-15
+url: "https://github.com/argoai/argoverse2-api/"
+preferred-citation:
+  type: conference-paper
+  authors:
+  - family-names: "Wilson"
+    given-names: "Benjamin"
+  - family-names: "Qi"
+    given-names: "William"
+  - family-names: "Agarwal"
+    given-names: "Tanmay"
+  - family-names: "Lambert"
+    given-names: "John"
+  - family-names: "Singh"
+    given-names: "Jagjeet"
+  - family-names: "Khandelwal"
+    given-names: "Siddhesh"
+  - family-names: "Pan"
+    given-names: "Bowen"
+  - family-names: "Kumar"
+    given-names: "Ratnesh"
+  - family-names: "Hartnett"
+    given-names: "Andrew"
+  - family-names: "Pontes"
+    given-names: "Jhony Kaesemodel"
+  - family-names: "Ramanan"
+    given-names: "Deva"
+  - family-names: "Carr"
+    given-names: "Peter"
+  - family-names: "Hays"
+    given-names: "James"
+  conference:
+    name: "NeurIPS 2021"
+  title: "Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting"
\ No newline at end of file
diff --git a/DOWNLOAD.md b/DOWNLOAD.md
new file mode 100644
index 00000000..94432650
--- /dev/null
+++ b/DOWNLOAD.md
@@ -0,0 +1,38 @@
+# Downloading the Argoverse 2 Datasets
+Our datasets are available for download from [AWS S3](https://aws.amazon.com/s3/). For the best experience, we highly recommend using the open-source [s5cmd](https://github.com/peak/s5cmd) tool to transfer the data to your local filesystem (additional info available [here](https://aws.amazon.com/blogs/opensource/parallelizing-s3-workloads-s5cmd/)). Please note that an AWS account is not required to download the datasets.
+
+### Installing `s5cmd`
+
+`s5cmd` can be easily installed with the following script:
+
+```bash
+#!/usr/bin/env bash
+
+export INSTALL_DIR=$HOME/.local/bin
+export PATH=$PATH:$INSTALL_DIR
+export S5CMD_URI=https://github.com/peak/s5cmd/releases/download/v1.4.0/s5cmd_1.4.0_$(uname | sed 's/Darwin/macOS/g')-64bit.tar.gz
+
+mkdir -p $INSTALL_DIR
+curl -sL $S5CMD_URI | tar -C $INSTALL_DIR -xvzf - s5cmd
+```
+
+Note that it will install `s5cmd` in your local bin directory. You can always change the path if you prefer installing it in another directory.
+
+### Downloading Datasets
+Once `s5cmd` is installed installed, downloading a dataset is as easy as running the following (using the sensor dataset as an example):
+
+```bash
+s5cmd --no-sign-request cp s3://argoai-argoverse/av2/sensor/* target-directory
+```
+
+The command will download all S3 objects to the target directory (for example, `target-directory` can be `/home/av2/sensors/`). Given the size of the dataset, it might take a couple of hours depending on the network connectivity.
+
+When the download is finished, the dataset is ready to use!
+
+### Dataset S3 Locations
+```bash
+s3://argoai-argoverse/av2/sensor/
+s3://argoai-argoverse/av2/lidar/
+s3://argoai-argoverse/av2/motion-forecasting/
+s3://argoai-argoverse/av2/tbv/
+```
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 00000000..447d74fc
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Argo AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/NOTICE b/NOTICE
new file mode 100644
index 00000000..c217fe71
--- /dev/null
+++ b/NOTICE
@@ -0,0 +1,60 @@
+Terms of Use 
+ 
+Argo AI, LLC, and its affiliates (“Argo”) strive to promote future development in the field of self-driving cars.
+
+Using its autonomous vehicles, Argo collected, annotated and organized videos, maps and lidar data in a dataset
+(the “Argoverse”). By using or downloading the Argoverse, you are agreeing to comply with the terms of this page and
+any licensing terms referenced below.
+
+License - Data and Documentation.
+Argoverse and any associated data or documentation are provided free of charge under the terms of the Creative Commons
+Attribution-NonCommercial-ShareAlike 4.0 International Public License (“CC BY-NC-SA 4.0”). The full text of the license
+is accessible at https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.  
+
+License - Code and Documentation.
+Argoverse code and APIs are licensed under the MIT license, the full text of which can be found at
+https://opensource.org/licenses/MIT.
+
+Using Argoverse Non-Commercially.
+We license Argoverse data and documents for non-commercial use only.  Under the terms of the CC BY-NC-SA 4.0
+“non-commercial” means “not primarily intended for or directed towards commercial advantage or monetary compensation.”
+To assist you in determining whether your contemplated use is non-commercial, we make available the following examples:
+
+Example 1, Acceptable Use: 
+You are a researcher at an academic institution, working on computer vision and prediction. You use the Argoverse
+dataset to further your research and publish results based on the Argoverse benchmark. Your published paper includes
+imagery and figures derived from the Argoverse dataset. The imagery and figures are attributed to Argo and a hyperlink
+to the text of the CC-BY-NC-SA 4.0 license is provided.
+
+Example 2, Acceptable Use:
+You are a researcher at an AV company. You are submitting your image-based pose estimation algorithm to a major
+conference for public review. You use the Argoverse dataset to train and evaluate your method. You publish the results
+of the comparison publicly. The Argoverse data in your paper is attributed in accordance with the CC-BY-NC-SA 4.0
+license.
+
+Example 3, Unacceptable Use:
+You are an engineer at an AV company. You use the Argoverse dataset to train a prototype detection model for use on
+your company’s AV at a test track. You use this prototype detection model as a placeholder until you build a large
+enough internal dataset to train your model against.
+
+Example 4, Unacceptable Use:
+You are an engineer at a company that produces a robotics platform for sale. You train the prediction and detection
+machine learning model in your company’s product on data from the Argoverse dataset. Your company releases the product
+for sale with the model you trained against Argoverse data.
+
+Suggestions:
+If you use Argoverse and identify areas that could benefit from changes, we would like to hear from you. Please submit
+any comments on the issues page of our GitHub repository. If you submit your changes, you may be required to confirm
+that your changes are subject to CC BY-NC-SA 4.0.
+
+Attribution.
+Please follow the attribution guidelines provided in section 3.A. of CC-BY-NC-SA 4.0 license with the copyright notice
+being “© 2022 Argo AI, LLC”. APIs attribution shall comply with the MIT license.
+
+Privacy.
+Argo takes steps to protect the privacy of individuals in Argoverse imagery by blurring faces and license plates.
+If you notice that your face or licence plate is still identifiable, or if you have any privacy concerns pertaining to
+Argoverse, please submit a request here: https://www.argoverse.org/about.html#privacy.  
+
+Marketing.
+You may not use Argo’s trademark, logo, or name without Argo’s express written permission in connection with Argoverse.
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 00000000..4858de13
--- /dev/null
+++ b/README.md
@@ -0,0 +1,124 @@
+[![PyPI Versions](https://img.shields.io/pypi/pyversions/av2)](https://pypi.org/project/av2/)
+![CI Status](https://github.com/argoai/argoverse2-api/actions/workflows/ci.yml/badge.svg)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)
+
+# Argoverse 2 API
+
+Official GitHub repository for the [Argoverse 2](https://www.argoverse.org) family of datasets.
+
+If you have any questions or run into any problems with either the data or API, please feel free to open a [GitHub issue](https://github.com/argoai/argoverse2-api/issues)!
+
+## Overview
+
+- [Setup](#setup)
+- [Usage](#usage)
+- [Testing](#testing-automation)
+- [Contributing](#contributing)
+- [Citing](#citing)
+- [License](#license)
+
+## Getting Started
+
+### Setup
+
+The easiest way to install the API is via [pip](https://pypi.org/project/av2/) by running the following command:
+
+```bash
+pip install av2
+```
+
+### Downloading the datasets
+Please see [the Download README](DOWNLOAD.md) for detailed instructions on how to download each dataset.
+
+### Argoverse 2 Sensor Dataset
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/158742778-557f31a4-569d-44aa-a032-99836094dc97.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158742776-069501c4-8dd4-4f9d-ac8c-f0421f855607.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158739736-fe876299-23da-46ed-98ce-173f938d1702.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158739767-886e1c2f-4613-495d-9204-a7b4813af16d.gif" height="150">
+</p>
+
+Please refer to the [sensor dataset README](src/av2/datasets/sensor/README.md) for additional details.
+
+### Argoverse 2 Lidar Dataset
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/158715494-472339d1-a5d5-4d33-8fcf-3455c0d78d27.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158715496-f439ccad-71af-4880-8b43-ade7b6c8f333.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158715498-23d7a11f-12a1-4aeb-b9af-dbced217b340.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158715497-d1603423-c32f-4cf0-ab1e-6bbc9c458535.gif" height="150">
+</p>
+
+Please refer to the [lidar dataset README](src/av2/datasets/lidar/README.md) for additional details.
+
+### Argoverse 2 Motion Forecasting Dataset
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/158486284-1a0df794-ee0a-4ae6-a320-0dd0d1daad06.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158486286-e734e654-b879-4994-a129-9957cc591af4.gif" height="150">
+  <img src="https://user-images.githubusercontent.com/29715011/158486288-5e7c0971-de0c-4ff5-bea7-76f7922dd1e0.gif" height="150">
+</p>
+
+
+Please refer to the [motion forecasting dataset README](src/av2/datasets/motion_forecasting/README.md) for additional details.
+
+### Argoverse 2 Map Change Dataset
+
+Please refer to the map change dataset (known as the **Trust, but Verify Dataset**) [README](src/av2/datasets/tbv/README.md) for additional details.
+
+### Map API
+
+Please refer to the [map README](src/av2/map/README.md) for additional details about the common format for vector and
+raster maps that we employ across all AV2 datasets.
+
+## Compatibility Matrix
+
+| `Python Version`   |     `linux`        |     `macOS`        |    `windows`       |
+| -------------      | -----------------  | ------------------ | ------------------ |
+| `3.8`              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| `3.9`              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| `3.10`             | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+
+## Testing
+
+All incoming pull requests are tested using [nox](https://nox.thea.codes/en/stable/) as
+part of the CI process. This ensures that the latest version of the API is always stable on all supported platforms. You
+can run the full suite of automated checks and tests locally using the following command:
+
+```bash
+nox -r
+```
+
+## Contributing
+
+Have a cool feature you'd like to add? Found an unhandled corner case? The Argoverse team welcomes contributions from
+the open source community - please open a PR using the following [template](.github/pull_request_template.md)!
+
+## Citing
+
+Please use the following citation when referencing the [Argoverse 2](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/4734ba6f3de83d861c3176a6273cac6d-Paper-round2.pdf) Sensor, Lidar, or Motion Forecasting Datasets:
+
+```BibTeX
+@INPROCEEDINGS { Argoverse2,
+  author = {Benjamin Wilson and William Qi and Tanmay Agarwal and John Lambert and Jagjeet Singh and Siddhesh Khandelwal and Bowen Pan and Ratnesh Kumar and Andrew Hartnett and Jhony Kaesemodel Pontes and Deva Ramanan and Peter Carr and James Hays},
+  title = {Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting},
+  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
+  year = {2021}
+}
+```
+
+Use the following citation when referencing the [Argoverse 2](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/6f4922f45568161a8cdf4ad2299f6d23-Paper-round2.pdf) Map Change Dataset:
+```BibTeX
+@INPROCEEDINGS { TrustButVerify,
+  author = {John Lambert and James Hays},
+  title = {Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection},
+  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
+  year = {2021}
+}
+```
+
+## License
+
+All code provided within this repository is released under the MIT license and bound by the Argoverse terms of use,
+please see [LICENSE](LICENSE) and [NOTICE](NOTICE) for additional details.
diff --git a/conda/environment.yml b/conda/environment.yml
new file mode 100644
index 00000000..8943eb38
--- /dev/null
+++ b/conda/environment.yml
@@ -0,0 +1,17 @@
+name: av2
+channels:
+  - conda-forge
+dependencies:
+  - av
+  - click
+  - joblib
+  - matplotlib-base
+  - nox
+  - numba
+  - numpy>=1.21.5
+  - opencv
+  - pandas
+  - pip
+  - pyarrow
+  - rich
+  - scipy
diff --git a/conda/install.sh b/conda/install.sh
new file mode 100644
index 00000000..730c393c
--- /dev/null
+++ b/conda/install.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+ENVIRONMENT_FILE=environment.yml
+
+# Ensure mamba is installed.
+conda install -y mamba -c conda-forge
+
+# Create library environment.
+mamba env create -f ${SCRIPT_DIR}/${ENVIRONMENT_FILE} \
+&& eval "$(conda shell.bash hook)" \
+&& conda activate av2 \
+&& python -m pip install -e $SCRIPT_DIR/..
diff --git a/noxfile.py b/noxfile.py
new file mode 100644
index 00000000..2285f87c
--- /dev/null
+++ b/noxfile.py
@@ -0,0 +1,140 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Test automation using `nox`."""
+
+from pathlib import Path
+from sys import platform
+from typing import Dict, Final, List, Union
+
+import nox
+import yaml
+from nox import Session
+from nox.virtualenv import CondaEnv
+
+PYTHON: Final[List[str]] = ["3.8", "3.9", "3.10"]
+
+nox.options.sessions = ["black", "isort", "lint", "mypy", "pytest"]
+if platform != "win32":  # pytype isn't supported on windows.
+    nox.options.sessions.append("pytype")
+
+
+def _setup(session: Session) -> None:
+    """Install `av2` into a virtual environment.
+
+    Args:
+        session: `nox` session.
+    """
+    if isinstance(session.virtualenv, CondaEnv):
+
+        # Load environment.yml file and install conda dependencies.
+        env = yaml.safe_load(Path("conda/environment.yml").read_text())
+
+        conda_pkgs: List[str] = []
+        reqs: List[str] = []
+        pkgs: List[Union[str, Dict[str, str]]] = env["dependencies"]
+        for pkg in pkgs:
+            if isinstance(pkg, dict):
+                if "pip" in pkg:
+                    reqs += pkg["pip"]
+            else:
+                conda_pkgs.append(pkg)
+        session.conda_install(*conda_pkgs)
+
+        # Install pip dependencies if they exist.
+        if len(reqs) > 0:
+            session.install(*reqs, "--no-deps")
+
+    # Install package.
+    session.install("-e", ".")
+
+
+@nox.session(python=PYTHON)
+def black(session: Session) -> None:
+    """Run `black` against `av2`.
+
+    Args:
+        session: `nox` session.
+    """
+    env = ["black[jupyter]"]
+    _setup(session)
+    session.install(*env)
+    session.run("black", ".")
+
+
+@nox.session(python=PYTHON)
+def isort(session: Session) -> None:
+    """Run `isort` against `av2`.
+
+    Args:
+        session: `nox` session.
+    """
+    env = ["isort"]
+    _setup(session)
+    session.install(*env)
+    session.run("isort", ".")
+
+
+@nox.session(python=PYTHON)
+def lint(session: Session) -> None:
+    """Lint using flake8."""
+    env = [
+        "flake8",
+        "flake8-annotations",
+        "flake8-black",
+        "flake8-bugbear",
+        "flake8-docstrings",
+        "flake8-import-order",
+        "darglint",
+    ]
+    _setup(session)
+    session.install(*env)
+    session.run("flake8", ".")
+
+
+@nox.session(python=PYTHON)
+def mypy(session: Session) -> None:
+    """Run `mypy` against `av2`.
+
+    Args:
+        session: `nox` session.
+    """
+    env = [
+        "mypy",
+        "types-pyyaml",
+    ]
+    _setup(session)
+    session.install(*env)
+    session.run("mypy", ".")
+
+
+@nox.session(python=["3.8", "3.9"])  # pytype doesn't support 3.10.
+def pytype(session: Session) -> None:
+    """Run `pytype` against `av2`.
+
+    Args:
+        session: `nox` session.
+    """
+    env = [
+        "git+https://github.com/google/pytype@fc08da16d4eab1deace406c52130457a7da66020",  # Fixes typing.Final issue.
+        "pytest",  # Required for analysis in the test directory.
+    ]
+    _setup(session)
+    session.install(*env)
+    session.run("pytype", ".")
+
+
+@nox.session(python=PYTHON)
+def pytest(session: Session) -> None:
+    """Run `pytest` against `av2`.
+
+    Args:
+        session: `nox` session.
+    """
+    env = [
+        "pytest",
+        "pytest-benchmark",
+        "pytest-cov",
+    ]
+    _setup(session)
+    session.install(*env)
+    session.run("pytest", "tests", "--cov", "src/av2")
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..6e1524f7
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,24 @@
+[build-system]
+requires = [
+    "setuptools>=42",
+    "wheel"
+]
+build-backend = "setuptools.build_meta"
+
+[tool.black]
+line-length = 120
+
+[tool.isort]
+known_first_party = "av2"
+known_third_party = ""
+
+line_length = 120
+multi_line_output = 3
+profile = "black"
+
+[tool.mypy]
+exclude = "build"
+ignore_missing_imports = true
+disallow_untyped_decorators = false
+plugins = "numpy.typing.mypy_plugin"
+strict = true
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 00000000..46732c55
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,39 @@
+[metadata]
+author = Argo AI
+author_email = argoverse-api@argo.ai
+description_file = README.md
+license = MIT
+name = av2
+url = https://www.argoverse.org
+version = attr: av2.__version__
+
+[options]
+zip_safe = False
+include_package_data = True
+python_requires = >= 3.8
+install_requires =
+    av
+    click
+    joblib
+    matplotlib
+    nox
+    numba
+    numpy>=1.21.5
+    opencv-python
+    pandas
+    pyarrow
+    rich
+    scipy
+
+package_dir=
+    =src
+packages=find:
+
+[options.packages.find]
+where=src
+
+[flake8]
+enable_extensions = G
+exclude = build,.nox,.pytype
+ignore = ANN101,ANN102,E201,E203,E241,E704,E711,E722,E741,W291,W293,W391,W503,F821,F401,F811,F841,P101,G004,G002,I201,I100,I101
+max_line_length = 120
diff --git a/src/av2/__init__.py b/src/av2/__init__.py
new file mode 100644
index 00000000..87cc154c
--- /dev/null
+++ b/src/av2/__init__.py
@@ -0,0 +1,5 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Argoverse 2 API."""
+
+__version__ = "0.1.0"
diff --git a/src/av2/datasets/__init__.py b/src/av2/datasets/__init__.py
new file mode 100644
index 00000000..d87765e1
--- /dev/null
+++ b/src/av2/datasets/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Datasets subpackage."""
diff --git a/src/av2/datasets/lidar/README.md b/src/av2/datasets/lidar/README.md
new file mode 100644
index 00000000..3e590585
--- /dev/null
+++ b/src/av2/datasets/lidar/README.md
@@ -0,0 +1,73 @@
+# Argoverse 2 Lidar Dataset Overview
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/158715494-472339d1-a5d5-4d33-8fcf-3455c0d78d27.gif" height="215">
+  <img src="https://user-images.githubusercontent.com/29715011/158715496-f439ccad-71af-4880-8b43-ade7b6c8f333.gif" height="215">
+  <img src="https://user-images.githubusercontent.com/29715011/158715498-23d7a11f-12a1-4aeb-b9af-dbced217b340.gif" height="215">
+  <img src="https://user-images.githubusercontent.com/29715011/158715497-d1603423-c32f-4cf0-ab1e-6bbc9c458535.gif" height="215">
+</p>
+
+
+The Argoverse 2 Lidar Dataset is intended to support research into self-supervised learning in the lidar domain as well as point cloud forecasting. The AV2 Lidar Dataset is mined with the same criteria as the Forecasting Dataset to ensure that each scene is interesting. While the Lidar Dataset does not have 3D object annotations, each scenario carries an HD map with rich, 3D information about the scene.
+
+## Dataset Size
+
+Our dataset is the largest such collection to date with 20,000 thirty second sequences.
+
+## Sensor Suite
+
+Lidar sweeps are collected at 10 Hz. In addition, 6-DOF ego-vehicle pose in a global coordinate system are provided. Lidar returns are captured by two 32-beam lidars, spinning at 10 Hz in the same direction, but separated in orientation by 180°.
+
+We aggregate all returns from the two stacked 32-beam sensors into a single sweep. These sensors each have different, overlapping fields-of-view. Both lidars have their own reference frame, and we refer to them as `up_lidar` and `down_lidar`, respectively. We have egomotion-compensated the lidar sensor data to the egovehicle reference nanosecond timestamp. **All lidar returns are provided in the egovehicle reference frame, not the individual lidar reference frame**.
+
+## Dataset Structure Format
+
+Tabular data (lidar sweeps, poses) are provided as [Apache Feather Files](https://arrow.apache.org/docs/python/feather.html) with the file extension `.feather`.
+
+**Maps:** A local vector map is provided per log, please refer to the [Map README](../../map/README.md) for additional details.
+
+Directory structure:
+```
+av2
+└───lidar
+    └───train
+    |   └───LyIXwbWeHWPHYUZjD1JPdXcvvtYumCWG
+    |       └───sensors
+    |       |   └───lidar
+    |       |       └───15970913559644000.feather
+    |       |                      .
+    |       |                      .
+    |       |                      .
+    |       └───calibration
+    |       |   └───egovehicle_SE3_sensor.feather
+    |       └───map
+    |       |   └───log_map_archive_LyIXwbWeHWPHYUZjD1JPdXcvvtYumCWG__Summer____PIT_city_77257.json
+    |       └───city_SE3_egovehicle.feather
+    └───val
+    └───test
+```
+
+An example sweep `sensors/lidar/15970913559644000.feather`, meaning a reference timestamp of 15970913559644000 nanoseconds:
+```python
+               x          y         z  intensity  laser_number  offset_ns
+0      -1.291016   2.992188 -0.229370         24            31    3318000
+1     -25.921875  25.171875  0.992188          5            14    3318000
+2     -15.500000  18.937500  0.901855         34            16    3320303
+3      -3.140625   4.593750 -0.163696         12            30    3320303
+4      -4.445312   6.535156 -0.109802         14            29    3322607
+...          ...        ...       ...        ...           ...        ...
+98231  18.312500 -38.187500  3.279297         26            50  106985185
+98232  23.109375 -34.437500  3.003906         20            49  106987490
+98233   4.941406  -5.777344 -0.162720         12            32  106987490
+98234   6.640625  -8.257812 -0.157593          6            33  106989794
+98235  20.015625 -37.062500  2.550781         12            47  106989794
+
+[98236 rows x 6 columns]
+```
+
+## Lidar Dataset splits
+We randomly partition the dataset into the following splits:
+
+- Train (16,000 logs)
+- Validation (2,000 logs)
+- Test (2,000 logs)
diff --git a/src/av2/datasets/lidar/__init__.py b/src/av2/datasets/lidar/__init__.py
new file mode 100644
index 00000000..22a65af9
--- /dev/null
+++ b/src/av2/datasets/lidar/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Lidar dataset subpackage."""
diff --git a/src/av2/datasets/lidar/splits.py b/src/av2/datasets/lidar/splits.py
new file mode 100644
index 00000000..4a64211e
--- /dev/null
+++ b/src/av2/datasets/lidar/splits.py
@@ -0,0 +1,20014 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Splits for the Argoverse 2 lidar dataset."""
+
+from typing import Final, Tuple
+
+TRAIN: Final[Tuple[str, ...]] = (
+    "000VFSWWAAkobywItdrErpC6fedKDWg4",
+    "00AdM6sOm464W4gr3GAjS1RQICs6kfDi",
+    "00NWuHDf7JlFNNuHVoCpzH0BRtpTXOM1",
+    "00SoqQ8cogQZown9vgaOPYN2MTCteHn1",
+    "00d3V9J9YqrSY6GvqmyPniFzusck1SS2",
+    "00evDt4kTBjibQk3CWDLAMJv4M3CnF9d",
+    "00iDTNgCO7PomBQXKajGzw3ltaN6iZl4",
+    "01HFHbP5Nki7nEkVNk3LoCRtnpplLV76",
+    "01a00LOmtnMBmV38GCEcG2VyJxLEiAvG",
+    "01eqxqeCpglInSM24lmPmPpWcHzH4edC",
+    "01zycuP8JyadKUWScacThS3QilmtEL3F",
+    "02AsEHFLJ7pATIQPBYPQLIQrozQhO3VD",
+    "02ZtLCghww5ojl6pcXc9blN2M2d4mhA7",
+    "02ePB7X3J2MxiZxIBbvWVcXoTeQ5d17N",
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+    "036oFkwe5HKgqqM2adiEdc8G4imDsYJ0",
+    "03WxMyW7IP2BG9JnjbsDwoUadXd224mv",
+    "03ew0C4LcrVtGvD5aQRyu5ZeiNpDKSJQ",
+    "03hqFiavewn9rgLxiB1iQGJXhMYoWitL",
+    "03yQHAhXQcIUVo6ReNaWn7ppUBBegUcQ",
+    "046TGB543BJ1EuO59vABj16VwT1RPof3",
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+)
+
+VAL: Final[Tuple[str, ...]] = (
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+    "EmPaKPEmLFoPJytVd3qwXmweftlwWINd",
+    "OiULk1XOPduDnSi096RQZb6Z383zdBDl",
+    "mfHvSWoY6k3PWpoZT3TBRuEbWShDFCK2",
+    "wwnLayJdDRcfX1fuivxgHzcnLRSroMsf",
+    "TUorVOjNxyv5WFd7WhvhfZQid27W8f0I",
+    "T2chYUBEFFw7uIBEHp1iDC6tlSLG3xyQ",
+    "isRPnOfbYcc4s9KepZMMQfMQdEOFW5zm",
+    "E6FZpwq2e9RYCu27gyxC2HHLLqSmsVcJ",
+    "mUrfpObCS74WLocZRxiSWqF35dw1f24y",
+    "Q5grVQXFQAyA1aATAa1LdFcN4in2bhm3",
+    "KB8hqBYQ3dXSpDHVdCdD15L7xOcHK82f",
+    "YsqmCFJtxpNQFN1e9lWu2MsCANm6YfPe",
+    "pl4SHhOfsPHsdSLvLVZ4HOU48co8iw7v",
+    "242SUFtd9uUsMupflqoEZ5Vlu0df8JGU",
+    "QXmGK7X5ydlFySejyltuOJdYXNRQrz1M",
+    "G1AInX4QCGyxN8Y0qqfwtoMfMKPJ2DBt",
+    "r7yDfv33o1BA1F1X9whKDM69VjFyzyhR",
+    "8sp0EeVyp4pZY3bk4K2VRcKTgryu7ORm",
+    "flE7GzFGfLpI57VY2GZOWRbrXh85j3Cw",
+    "4IkzKYzFAcvOzOzUVEWIYqCsg0oKLcWI",
+    "hL8PI8kceZ9uRsysZ3WWQdFtpnFeRyBy",
+    "hHiUPy1byFTr48h22Fzui5HcSgTUtpBD",
+    "4A89MEDraGl3CXaEq5fYtUKgVYKwdxf0",
+    "rRfanmGnkb7KZnBSFvTikfdWs8LUJuIi",
+    "595ErHqbojO1O8lWeEMXDK9CG73iQCtK",
+    "TsUs0zM6HGfmssgymRWwx8Bz0xY3BkfP",
+    "eqfnLbP1CVVdKYvn69sNjQ8yQqks0RrW",
+    "wwzG13PceW4L6NpnThEDm7ZsxKJ3gutm",
+    "cU50h3KMBI9dcizI1N3Fcas3Mu8Z5ggl",
+    "NAQp4zY7FHwP67qJYkaCVOOlVyoke8Xu",
+    "Vv85ADVKcacXKXmpc3T9ukYKSlPL5KWT",
+    "qpKJbOiLM1A3sYCQSB18b7RPwspu3iSE",
+    "0zqLpGOken7vy8be1UxJMYJqc9I7gDfS",
+    "jXJLSFaeFqvNzwb8HL0q7eotkfu6OvAI",
+    "ctY1JzFxBdijXXu2Cp2IbkmBcvZTk26u",
+    "6PfcGIC1V8sDP7Ali4PUJK1T7ED9HtMQ",
+    "BjOBQUoTV9IhQe9yOH0mjD20jJJWakHw",
+    "dmwIwPHf5dlLB7fS9XyGPI3If1pnNwyZ",
+    "k7vurywnRtkiTZvhpN5olP8cCXllOOEi",
+    "XnqO6V3kgBF3iZpoeXvDjAyjCTLxzjNO",
+    "xbDHz2ubznVxP31BRcV9YTXsHXFRycWR",
+    "2R7eRaxjeKPlOxi1SvsB0PQZVWlKnNP3",
+    "YjcYkXGiUZv4PTYgHrL1AkNsIeEbSwZg",
+    "D86lHjBdveocxJAwGXoS0uJnpWhgUxz5",
+    "A2m8UsVvMLxaDJ1jlKEIhqOfOC6PMjLI",
+    "3coOq9KG2Pn3j5pLZ37BpjArWdqoxhGP",
+    "pNauPELiusqQjhl2XEQ872MdDN5p4J9t",
+    "MZAXGBSWg5xxL2z1vYtvQNbVR9D9TvMg",
+    "LXSujPvTtZqDAlxoBi5caGg6amaWZyht",
+    "rk63V55Gq0wxlOawggFWPxORQDqMieBZ",
+    "P15CfNGRDTjKEBVfFwums88et7vkMbsE",
+    "s77Qadgz4fLntkJmCvrpeXVrpYrnG3uH",
+    "dHwFFxKOpDrerOoQJfmi537BTyIyZfdA",
+    "ijJ7AOcAaQnkESx2rr3RMR9V93U7fhEm",
+    "mdnSK4Ze7n6ELmdTFVNSdUIwm2Nf9EtM",
+    "Uc52zcOu54oM3Kt2WYdeg65sDgdzqwcS",
+    "pT238kTySCF20djaAHSwvhMf36Xitubm",
+    "Ev0v5BmCwqvBZvDP2QiUSNX8YpzU24wr",
+    "kahcUcNwxxZQcZXvPrBWDL8VgbOT3EB4",
+    "junJWL4apYPpd5XYTJ3NXR2Er8MrEImy",
+    "JmCBE6v9hEmAwWJXnOPkH0NRyzRahFBv",
+    "iWk9RO4l5b3QvAfi3yhuiHpAmWunvHpb",
+    "qHBcq5ibiI3fuMXWmo3bbHvDdIMLiPJa",
+    "9wNU5xGdYha4rOtIJOL9qDcGHn5fxh8o",
+    "mwLYEj685OyLzYC12LcCwXeZlVWWyR7J",
+    "UroSdnrXMAAK7gErPLL9FpDScQJjYPrV",
+    "gkYOBTCUm4RO6DeDJfWsoOl0N2oPg8Om",
+    "ZVdRyzyLV8sRZAFl6lZSehzDETt6E0OJ",
+    "hMAImEhsyXdiITkjz8IzCHZLOs2p3mT6",
+    "hfI3UoBbLzDWYKqA9AH799Se9WGgBZJv",
+    "FHL1wDGzaMyrQt4LCPZDpsMkQNYLTbBY",
+    "ZhvReQo0eEKj1eJE11MeWUdlP2LTMBqb",
+    "x68mMtUnUAqFgEUfxjh36XPt1iL33b0q",
+    "MzfUG0LyufBj3t3JlUXSZ5Py5FT7n59J",
+    "7OPjaYYeDt5fWsCDszUkWldiYEEL48ZE",
+    "PdvAgzEFGhP6hLABlKnLf0RckJjdYA39",
+    "FYHpqd4wXIeF9sYysUrh1FbnMgo4UfVS",
+    "ADcmZYTCjYOr9OsTMUsao9qOBmMCyGBi",
+    "blaEyNsB318oBQZsOCBPN5u08NNieL2y",
+    "xAuzzN8MtY0rinrpxUcjRRlAQ3QsPFsS",
+    "4G8Sij4YWmR8xZIY3Wi99ehTbGtqqmvS",
+    "uyvIuCISJTpqLFUvFSCz3GobDp4uCsvS",
+    "AQJ9KdxgvjijbxCS2MF7kVSeJ4J6UhsA",
+    "0KVuwn20PpOSEuUH0OFM1T2G4UqpeYeL",
+    "64ahcUG0pxMDHApfSo8lMjVEnnFQzdNV",
+    "dtBSiJnI7db5fRGQvV0YFpQg45Jbeqh1",
+    "lEOg1f3SZjMQD1CEbGJ6FXfXXSm2CcRF",
+    "NqBqr85TBTRS5ImaE7TO6hYFNxcXDI4J",
+    "6HKTsviOZCKG3Fi6ZNIGvsBfUgaAfzQt",
+    "5rnnFH5EP5rLuJDzyfJkk6AECF41uZbO",
+    "x39IcmYd9dXTtXVrU7UuaIHeoimxsMV3",
+    "NxAH6jqRP5j6X1q6UJ0kH0bv4edbP0sd",
+    "fqW5f4wDhpbBWJK2zssgA5srzB6oBPKY",
+    "tsv6B1aNbDQacjr3o1hxfKCqEnU4ifnJ",
+    "MxnHEHi3daMK4vlUSChOmlq0oiGaRwrQ",
+    "Z4t3w5zozxdiO3ZzsYJpJ8FvwMjAtXzg",
+    "7YGSgxVDe3ZRDwuZJwsOYqPyVc5G9G8J",
+    "ADT67DS1dUAiF8Y6KsTjUcin0UJoNoiE",
+    "KsES1c6gcwGqmHfcQuExokUIZuCH2Pr1",
+    "P3GkDvOrXOw1ZO6JmN8tLN1YnbAMj81o",
+    "l81FAaUeF601qqaydvvmK2Gb9O7UhsjU",
+    "KHlu6uJO1jhM997My8BmyJglEN2jM0lT",
+    "WE6PDKFh4gnVaVvT2fFwgYHGe4QNwuvc",
+    "o9IezgIgJcx9gCMPnL1QfqTxRk3jicmc",
+    "g9zK1H9y3DVwRDTLxYzeLDn0JeXSpPAQ",
+    "qJtPar8BNxRpjVdzwD7De5rW9uncn3L9",
+    "OyHHkZuXV83Qay8ZNatvpFQqLqCm6ZrM",
+    "PuMaED80u4b3VhWKxYir8nwaxfY0Ebvx",
+    "JbFlRpIIKftZgMNWuI0K1N9uIZbgpwZ6",
+    "DAMnb605Am6nzwP2TeA8WiPrTxU0scY0",
+    "kpnge3AFkeuYJor8Tah2qMwMxh2kLJ3u",
+    "YKQZneuV8WlbJlkYUQMBNxzXStm24eIJ",
+    "uUIEBM7mp7zQ8AObYpZy1KMSdoNneHYh",
+    "2oUuZ968s7YJetdYMf8Mv61nWAesfl2I",
+    "OnmJ7qNjgB9c3JTTqHQx5rpY9BEp7Hjm",
+    "ZJUyJJa01Qke98Jxd56rooj8q8N5hUl5",
+    "7CUSiLldZTrlaAjrje28HFU1QwZlPcL1",
+    "SSUd4O2L1ITMJsMPKNUWCjeajCKPqXBB",
+    "lFyBem9Qyuj1c5l9uGShGIwwwZ2CQpDA",
+    "CxAThPajwWaISKPQvwDBod0oLoegtjON",
+    "0ITAbPbOIytvuCtkX9v0lsRAkstKQgVh",
+    "R0vZUAwMscEtTG5kgh9AAXa3TxBoYtpd",
+    "ozHyBjAWZOeul3hmv8JzP4ggY0LCsQiS",
+    "RVez75813whZVE6KZKZYNfx5rZqFMoIm",
+    "J2t57dP0Eg2tg1W2ii7BAwLZazmBe39C",
+    "HAfo7Dyu5kAQ5QZu7sTv7AURWB1CyIn7",
+    "LMova35TqyV3u2Z9egmw8iNFwuvNzxQL",
+    "d9GUAyQfKo8fsD91jYocBur4rFFfrNJf",
+    "VreaR6aurAFIYbyuOjPSAirV2bf647s6",
+    "xLxXSbUlzSSuazcnTzLjvEM4CWOZcyBX",
+    "7MChM6P37rbhjE3ZWnZA6aijuDumI2Rv",
+    "gmibDpk2bWCJ6e1ZWerfaA30GnxJjYt5",
+    "KxBxJatcVGepVlpvK0tiEnYKmm2ioL39",
+    "ty8F1TUYLaZeyhuDCIjkazM6url5Niah",
+    "Hese6QC9hdjS5uZTX26fNdOSpXMUnpo0",
+    "LrIqUweG9zR24RT7iLqNXtcIUX9NN9iU",
+    "fBPa0igYpDA5sVWBj5cGoXmRrURtk7gY",
+    "xqf3NuWf1UGxcRz1UVa5g5ZtJZ0uV12r",
+    "ubhpkfHl3oqeMUGEhQ6mV76PBDMbgLWW",
+    "51e6ztuwwOwvGlELmB2X8nFtPnfus6x7",
+    "EUtyeCj9rFyxV7kh7FmTcG5AdtyzcFyI",
+    "90DMkDx3iFLOeQ826bALRx5w7KCqiwSU",
+    "6SMxdzpi46M2N442oPTMt7AG9EeS7kBB",
+    "fwbilvhj9M6bkG2aT7thO6HJyWHDLcEM",
+    "Rdg1nfVlDPJdnbspfir6Is4PZ2AO9yJc",
+    "2mI7961MiIwpJfNq60gEDM82Oxo0ymT3",
+    "3N6BPSNvpQArTB83n5A9XIJoWmEiotqK",
+    "QnVZqSRlHhmPMyqipncHkfTGz6uKcnFK",
+    "a2bNQQGx9YZGIjYZZVlyMY2YoDDvCbl5",
+    "lrVJzcjvVcNGQpwu8fdAzSTtn7pjiUrm",
+    "ltTajFm1hSgcqiC1pfyUe3Ln0JRrDVMm",
+    "5EAKM81LaoH3xCx2i1IxvG0OvVRA5KYn",
+    "CxvhMFMC6E4bwG5qRx7vaPxfu9Nxy4xT",
+    "bvO2lt9ieKsAo7YW7xiD0bI6Cch4vUx0",
+    "1lQPNI2simosUqVxMAtBWwnTIMQAJwgX",
+    "J6TCxZEQk4EE5qMgpTYhYRVk9EHk7V9Q",
+    "75EqXlmWN4WJMmUABrkCPJyhcI5gX2oc",
+    "xoU4cLal33hKjthraBtCQgBXzn8rT3oL",
+    "LHArXkhQRtTMDBYvsWaSCm8E8dlsmafL",
+    "5kXmStdPrkvU0652uwwWK89aOpD8gF3W",
+    "lS22eSzM0hinNtIcqWS1KNWb1lSs8zyF",
+    "oFab167paq9x37jGaNeuZjrbPohu4qMr",
+    "kAsmMvhrQ6Pg6CBhx6Jpre86BrPiiDqc",
+    "qMI4726QFLDmxigmRxYkHJdgq1wT7NNy",
+    "8AJO7BHXvUpX3owv3OVTaWax0Ei28nTC",
+    "JRtgLJFXSS95QqbEMyqzOAdy9uxBqPH1",
+    "Cva47BFi60k3TVlCFzTrl5HnAA8yhmVE",
+    "rOlGaRVH8Av9uoj0YdlegOZI3DnmtsDH",
+    "OXJmTYfNvOuWU6eHb3MpKRcHyjMv4wjT",
+    "c5SQJZ26BF0imSx1dvA0ErHH1sprs9KK",
+    "pHpyvWCx5zh3W4ClJaeAb9OuJXCu0sr9",
+    "6mYNEuriIpcJi3yGgeDEGu50a4xPfM6i",
+    "DvW8FPOQWhk8oELC9SLV8jFFXeYzUlYu",
+    "diBqW2ou7LYGD4KQxjfgMXl3m1VNjptn",
+    "cn6hn6cscgBgYWfEnFMfEBhXRoJoIwb0",
+    "yjkaNj06dRn9aimv7XX6J3pEzKLWJyLr",
+    "mHNFomTEduyNAnixXVEl3gV7zQzc2P2c",
+    "fpIo7JvQK2e6oNqkCgxdd7DWEn3Bm1AW",
+    "3O93jzfT8qk8s5Cd72hTphTdD7wNXRk6",
+    "LUTw9fN0K9IWuk4KSNGBjWV67cJyDYTc",
+    "TVcyvzLkpbOgNY0bDFIZltV9Lhg8Vn19",
+    "8FlOCG8PJEsChKNPIGIgaI5jTo6fSBGb",
+    "QgS57N6gzxTFoE0v0h6kfYBSdIMyGAZ5",
+    "m4w3WFcqsKzf8gnyiaprMg3VOuKpANIw",
+    "E9ikr5oNBrgBWkLOATkxO5G9XfL4MSLU",
+    "pvwY6ZG1VhSoR0Oj8MX1SA5HduxgqjbL",
+    "e8oKBh4HiCbO5uSXPdEJQdjuo1zc5Ukb",
+    "Q6v5CtvyEdqupUU5vsGUgLhasQS86eQB",
+    "9NWlveRSKWYP0xNQvyLGnnUEz1h6lP2l",
+    "jDiUKB3ZRYvzjg4Oit3KoE69VFsOcFoD",
+    "cnU3pkVOnOHOSvwxxNrqW0XKQQNI3xUa",
+    "f1C3l4wkOcUUWPaHqi3kb4zY0UdYhwCW",
+    "T4wZPLuSC7B9MvHRnkd6FwqX25kIS26i",
+    "ZZYmqPg1XdFxZHjeBDeiC6dzpr6PmYug",
+    "cg2yhCTbFFeiHKOrxob7HCrjk9XNXw8P",
+    "TcSpuyz9KTbecg5Aqof9UOUSasnynAE9",
+    "0QB8KZQ9HFftSYAPyyIktvRCbbE9oL9r",
+    "18YQt57cjx79bZv8sIHXNbgSiUqqaX8N",
+    "JZcEr9ATUD2ZHGCUgwYt0Eqz39Oisisn",
+    "yPi87e0hPp5amgc9hpNYzNmph3L5iCQw",
+    "jFaXZa0AFmTd77mSZduQjiQX74waHffE",
+    "1BZpcSR5F4VbYqNOhVdsE8zw5XQuvHdt",
+    "l7MoqLWVCM4vhuxnYzc6IacY23Lk3Ztg",
+    "RBzLDY2hAvBS4Ksi4PJfvpumVvglnUVh",
+    "5ULxNpEUarRrXUN7BKDmOFRsOWIwI5Mn",
+    "RqcutotqtMUyz1ByE62FuSyLkbKN8Eou",
+    "5HS1cmawk1PW8ZMsEqZrfpEjSp4e7Qyz",
+    "kKWwD2v8mwyMZYG2mwvrx9JU9AFlYRtO",
+    "NrDwAehtFXTH3cF2cvKyDvPaYoP2qcjj",
+    "PWTBvK2q3wVkpZHyyuEKqlCSLNOREYk2",
+    "p2dAGyzxuJTCC5HWNiM2KVg2ZWuOcU1d",
+    "qStvoCzy5oRtuO9bDxKzlTBYnKniklyl",
+    "QgX0LlqNVci53VYQ2BRSYpAhET42zP1q",
+    "j4NYuCeALjJgCsmf7YIqN5SYu0d6va8D",
+    "kkPBys5FZpmNmhZuz0QlK9p8rIpzufNH",
+    "lGp5xmUBazttnqNQq61c8xgWaqH9Sx4G",
+    "LsmMSegq7kvekWIrpy2rRBy0FU90Ms7E",
+    "4AgSwhd97CieQLozbbFUFJB4YAk8QrgV",
+    "5MPiTere3bkViS9GZOkgfNKiuDF8hXJu",
+    "ZL7sn1bzejgkS1BKdbNapVcR4LK5LS46",
+    "1wMYzEENY4bhbSJoBe2LivPWzq4icVQd",
+    "BRcQDoNvPkHingixGXeNOrS2aas3d4jL",
+)
+
+TEST: Final[Tuple[str, ...]] = (
+    "1YvU4V8mDibnXW3S6BCL26ppdjiG7D3Z",
+    "StP4x8LdgrWCQ8BL4mpu84yFVuSb5JGj",
+    "kYzdATT3yRaDh6vCISCi35upjs6k57In",
+    "ZVaCWlmOTfU1JPQedNG0RtlNlHA3DN4H",
+    "J636ZN9px6dJ5X6VXoRn1JjQBKu6fZ9v",
+    "gvzXr97uisDbIkwQ8AJyydrRg4witmn0",
+    "dgsdOH0wEYhHMhcqMFS4k2AYTkF1qWSD",
+    "Qaex1S471SObCz22Imv493DmXOiB9y2v",
+    "bl4HtiNdIWBZDfwIW2jewWTVROxO85Jm",
+    "OeW6W5YfVUhXjFBpaS9ljlN4R0OPUbgS",
+    "mUtHrf7D4CMnyuI1UQEX6Vxr1yociBTj",
+    "NuAnqV38o4j6JERUwMO6V23REjDXW3a8",
+    "cSXc1vFrTDGanhwz9pssmow0smM9gE4Q",
+    "x1D0JAsDNhjAGcL8bSetj1y8yItpbWQp",
+    "ifHQUXOu8QwTd7ZL85eZY0bUt8WW2Sw5",
+    "28AwpORzdIdriuBNFPLMqnEbhRzXPx5h",
+    "tB8NVh1DuMwaFKlVG7lqajN9PzNHq6nQ",
+    "QnTrzXiBwS8J4XNnSm09xyD9Xhm6R8m6",
+    "QGBgcZCXYc7KVhqY5nPn9mwDHtBcpR0W",
+    "5VONyAkjOCgMcMwWWTCyCKguslwHAUGM",
+    "lDwGuEHNBC2XWbbYBdZME3iUEsZ5q8TN",
+    "6d60zwHmuEBgtVxagcaAW5yczuLUlug6",
+    "pGP4icNErCTxC2GWW7mDNL9QcDb2c1XQ",
+    "qRAmAEqJZdWvaoQa8eCSEx9QZ1ZyPVeS",
+    "q1IjFzPYSAPeP0BwGR9JG6t5a1FQoDZA",
+    "3kbZRFxcGPpIRJxgSXX2Ig4NGBWFWm2j",
+    "93jVQivGx53Pm09HMKgteGhjMrLyVAxe",
+    "sgMxsQpuFWJs6HaTzXwFb8qr6vWyPBRE",
+    "Zys3Jm6s1cuOBSD5C4aT63pQGfDdc65y",
+    "YPEoBLpgA1FGXsZrpQUgFxFSZuVP4an7",
+    "P38xlGK4ZVu5wDrU17KfAfu57r27I2QZ",
+    "y22H98mDMzdiBq7265sZZqFNLE6pplLY",
+    "PyUC0K0RoCFDZFbDU88RypfOzW4kFtJW",
+    "5bUFuf8u7753PJYCw1ILMsWS75s1VxNC",
+    "kHMjn6gERaukGhUsOfzVXVYbNTqNzhlp",
+    "M8HaAfypwQrJVR8oRcuQQsXtxZktyNPn",
+    "cA7f2GjXi0pVyPAuA716EbCjPpmjVARI",
+    "Zw5e21wWNLRlawDJLtCAcRbVEeqalR0h",
+    "1BuSRz8nzMa94BqClikXn185KGrsuEoe",
+    "HXKHHp8RNcz9yrvmT3iwA04gBxIqGlct",
+    "jy701NU07AspwIjI29loPK31jxy1RgL5",
+    "FgNLdomPs0Yajs2SrIunlBm4d9yeolsC",
+    "opAW45SoyqZGt3OalZVQ6YKjEgts3EN0",
+    "jsMzcKaPkwIOPS3HqCyg5hFee4D3yunx",
+    "18nUnMEm4rHOygGBrSd75eyNm2PgLgCr",
+    "HwHGXvo9UdQGLdhcnd6WFeU7AhLShB3R",
+    "hySHPU7rVt7j62SVuEb4QUZ2s84r1eJW",
+    "rwyANH4jlm1n33I0SvnTgQCHxPA33e2H",
+    "D6KgDOKPDCAZ24dvnclgtfiu6Bj8gf9e",
+    "s7TzQKUIhddvojXzhmZEjr9ms9f6Dpxb",
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+    "6R61zUH1TM5MrmoMZ6U5WYODvkOygp0P",
+    "LdMGLlQKATYO7Znd5TUyatIALKgDS7BJ",
+    "sHVO2MjDAMFv3qXy3OIZo6wSQFXFNehb",
+    "h92vW1j6ZvbXHvU9g04kL1bHbCquACXQ",
+    "QR5q4A9M7oTf1WXJZixHtSAXvfanmyAU",
+    "Ig825GU276U19mbwrhuZyREhgzWUZZnw",
+    "KuOInyuUbletr4tYGnDYyMJAd7jtpqTy",
+    "UnJ6yh4TEBlj73SkUerXVTO26lqUIsNK",
+    "lfxqq19MhK9iv0x8js9cl1ZyPAvIS16W",
+    "eIRtcX9KvXL5dqQ7Sup92NrB3FJCvVb8",
+    "Fz0rcTD310Uf0Tyqph3PC97vAlVMV9RN",
+    "A9L085MqHQkp6rNz7EHdeZbVLQF8wgWQ",
+    "FHzrZ1mvnzFv9Kl4Jhq04flSWxYWVjTD",
+    "wg3k0VDq7XMZHgdb5uiwFBAUCZ4ZMvQJ",
+    "1B6F7Do9CFeQrSCM9Su5gBHHcOUpL2cc",
+    "ShUV8Koapzb2tbQf9bmnAQvqQeuGQsB0",
+    "yvpNhLYs49wKuBed9Esf9w3IVYBUiAGI",
+    "suj9LCHuxOn3Rd5IHrEGuSBMmVPglXat",
+    "RrMWUFdlwOtJsRnVoYjFnKs1B5peaTXq",
+    "E2O7KiwVx6IwYZ9z2OyZ8E18fC6h0iEA",
+    "eBVuU0tqon5eMgg3z6UP8s5U8Q1iuJ7E",
+    "LKqIyWRkavD1cNXaRceitLWmnHryNNOV",
+    "MT8TjXdhn8Dru6WpG3TAPXuhKU2ghI7C",
+    "3SPVtjSlFhr1UmRvQoLL7pER8gJmT44r",
+    "Hn2RIamDoYFPt0HHiqRgcgYj0NwSUfWp",
+    "4A4m1wQLks2FzX9tRuKTj0RJCqtuaXUM",
+    "RKu8DYcj1kyBlJkLtbL8ecPsXoJhH9uQ",
+    "uSgGGBJFHJLRwHBHZmGe2Ax09WLoMA3N",
+)
diff --git a/src/av2/datasets/motion_forecasting/README.md b/src/av2/datasets/motion_forecasting/README.md
new file mode 100644
index 00000000..1952291e
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/README.md
@@ -0,0 +1,68 @@
+# Argoverse 2 Motion Forecasting
+
+![](https://user-images.githubusercontent.com/29715011/158486284-1a0df794-ee0a-4ae6-a320-0dd0d1daad06.gif)
+![](https://user-images.githubusercontent.com/29715011/158486286-e734e654-b879-4994-a129-9957cc591af4.gif)
+![](https://user-images.githubusercontent.com/29715011/158486288-5e7c0971-de0c-4ff5-bea7-76f7922dd1e0.gif)
+
+## Overview
+
+The Argoverse 2.0 motion forecasting dataset consists of 250,000 scenarios, collected from 6 cities spanning multiple seasons.
+
+Each scenario is specifically designed to maximize interactions relevant to the ego-vehicle. This naturally results in the inclusion of actor-dense scenes featuring a range of vehicle and non-vehicle actor types. At the time of release, AV2 provides the largest object taxonomy, in addition to the broadest mapped area of any motion forecasting dataset released so far.
+
+## Download
+
+The latest version of the AV2 motion forecasting dataset can be downloaded from the Argoverse [website](https://www.argoverse.org/data.html).
+
+## Scenarios and Tracks
+
+Each scenario is 11s long and consists of a collection of actor histories, which are represented as "tracks". For each scenario, we provide the following high-level attributes:
+
+- `scenario_id`: Unique ID associated with this scenario.
+- `timestamps_ns`: All timestamps associated with this scenario.
+- `tracks`: All tracks associated with this scenario.
+- `focal_track_id`: The track ID associated with the focal agent of the scenario.
+- `city_name`: The name of the city associated with this scenario.
+
+Each track is further associated with the following attributes:
+
+- `track_id`: Unique ID associated with this track
+- `object_states`: States for each timestep where the track object had a valid observation.
+- `object_type`: Inferred type for the track object.
+- `category`: Assigned category for track - used as an indicator for prediction requirements and data quality.
+
+Track object states bundle all information associated with a particular actor at a fixed point in time:
+
+- `observed`: Boolean indicating if this object state falls in the observed segment of the scenario.
+- `timestep`: Time step corresponding to this object state [0, num_scenario_timesteps).
+- `position`: (x, y) Coordinates of center of object bounding box.
+- `heading`: Heading associated with object bounding box (in radians, defined w.r.t the map coordinate frame).
+- `velocity`: (x, y) Instantaneous velocity associated with the object (in m/s).
+
+Each track is assigned one of the following labels, which dictate scoring behavior in the Argoverse challenges:
+
+- `TRACK_FRAGMENT`: Lower quality track that may only contain a few timestamps of observations.
+- `UNSCORED_TRACK`: Unscored track used for contextual input.
+- `SCORED_TRACK`: High-quality tracks relevant to the AV - scored in the multi-agent prediction challenge.
+- `FOCAL_TRACK`: The primary track of interest in a given scenario - scored in the single-agent prediction challenge.
+
+Each track is also assigned one of the following labels, as part of the 10-class object taxonomy:
+
+- Dynamic
+  - `VEHICLE`
+  - `PEDESTRIAN`
+  - `MOTORCYCLIST`
+  - `CYCLIST`
+  - `BUS`
+- Static
+  - `STATIC`
+  - `BACKGROUND`
+  - `CONSTRUCTION`
+  - `RIDERLESS_BICYCLE`
+- `UNKNOWN`
+
+For more additional details regarding the data schema, please refer [here](data_schema.py).
+
+## Visualization
+
+Motion forecasting scenarios can be visualized using the viz [`script`](../../../../tutorials/generate_forecasting_scenario_visualizations.py) or by calling the viz [`library`](viz/scenario_visualization.py#L48) directly.
diff --git a/src/av2/datasets/motion_forecasting/__init__.py b/src/av2/datasets/motion_forecasting/__init__.py
new file mode 100644
index 00000000..cde1422c
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Motion forecasting dataset subpackage."""
diff --git a/src/av2/datasets/motion_forecasting/data_schema.py b/src/av2/datasets/motion_forecasting/data_schema.py
new file mode 100644
index 00000000..2f94a94f
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/data_schema.py
@@ -0,0 +1,124 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Classes that define the data schema for Argoverse motion forecasting scenarios."""
+
+from dataclasses import dataclass
+from enum import Enum, unique
+from typing import List, Optional, Tuple
+
+from av2.utils.dataclass import dataclass_eq
+from av2.utils.typing import NDArrayInt
+
+##########################
+# Track-level Data Classes
+##########################
+
+
+@unique
+class TrackCategory(Enum):
+    """All tracks are categorized with one of four labels, depending on data quality and scenario generation criteria.
+
+    Members:
+        TRACK_FRAGMENT: Low quality track that may only contain a few timestamps of observations.
+        UNSCORED_TRACK: Track of reasonable quality, but not scored - can be used for contextual input.
+        SCORED_TRACK: High-quality tracks relevant to the AV - scored in the multi-agent prediction challenge.
+        FOCAL_TRACK: The track used to generate a particular scenario - scored in the single-agent prediction challenge.
+    """
+
+    TRACK_FRAGMENT: int = 0
+    UNSCORED_TRACK: int = 1
+    SCORED_TRACK: int = 2
+    FOCAL_TRACK: int = 3
+
+
+@unique
+class ObjectType(str, Enum):
+    """All tracks are assigned one of the following object type labels."""
+
+    # Dynamic movers
+    VEHICLE: str = "vehicle"
+    PEDESTRIAN: str = "pedestrian"
+    MOTORCYCLIST: str = "motorcyclist"
+    CYCLIST: str = "cyclist"
+    BUS: str = "bus"
+
+    # Static objects
+    STATIC: str = "static"
+    BACKGROUND: str = "background"
+    CONSTRUCTION: str = "construction"
+    RIDERLESS_BICYCLE: str = "riderless_bicycle"
+
+    # Catch-all type for other/unknown objects
+    UNKNOWN: str = "unknown"
+
+
+@dataclass
+class ObjectState:
+    """Bundles all state information associated with an object at a fixed point in time.
+
+    Attributes:
+        observed: Boolean indicating if this object state falls in the observed segment of the scenario.
+        timestep: Time step corresponding to this object state [0, num_scenario_timesteps).
+        position: (x, y) Coordinates of center of object bounding box.
+        heading: Heading associated with object bounding box (in radians, defined w.r.t the map coordinate frame).
+        velocity: (x, y) Instantaneous velocity associated with the object (in m/s).
+    """
+
+    observed: bool
+    timestep: int
+    position: Tuple[float, float]
+    heading: float
+    velocity: Tuple[float, float]
+
+
+@dataclass(eq=False)
+class Track:
+    """Bundles all data associated with an Argoverse track.
+
+    Attributes:
+        track_id: Unique ID associated with this track.
+        object_states: States for each timestep where the track object had a valid observation.
+        object_type: Inferred type for the track object.
+        category: Assigned category for track - used as an indicator for prediction requirements and data quality.
+    """
+
+    track_id: str
+    object_states: List[ObjectState]
+    object_type: ObjectType
+    category: TrackCategory
+
+    def __eq__(self, other: object) -> bool:
+        """Dataclass equality operator that correctly handles data members."""
+        return dataclass_eq(self, other)
+
+
+#############################
+# Scenario-level data classes
+#############################
+
+
+@dataclass(eq=False)
+class ArgoverseScenario:
+    """Bundles all data associated with an Argoverse scenario.
+
+    Attributes:
+        scenario_id: Unique ID associated with this scenario.
+        timestamps_ns: All timestamps associated with this scenario.
+        tracks: All tracks associated with this scenario.
+        focal_track_id: The track ID associated with the focal agent of the scenario.
+        city_name: The name of the city associated with this scenario.
+        map_id: The map ID associated with the scenario (used for internal bookkeeping).
+        slice_id: ID of the slice used to generate the scenario (used for internal bookkeeping).
+    """
+
+    scenario_id: str
+    timestamps_ns: NDArrayInt
+    tracks: List[Track]
+    focal_track_id: str
+    city_name: str
+    map_id: Optional[int]
+    slice_id: Optional[str]
+
+    def __eq__(self, other: object) -> bool:
+        """Dataclass equality operator that correctly handles data members."""
+        return dataclass_eq(self, other)
diff --git a/src/av2/datasets/motion_forecasting/eval/__init__.py b/src/av2/datasets/motion_forecasting/eval/__init__.py
new file mode 100644
index 00000000..212304e7
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/eval/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Motion forecasting evaluation subpackage."""
diff --git a/src/av2/datasets/motion_forecasting/eval/metrics.py b/src/av2/datasets/motion_forecasting/eval/metrics.py
new file mode 100644
index 00000000..791c786f
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/eval/metrics.py
@@ -0,0 +1,58 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Utilities to evaluate motion forecasting predictions and compute metrics."""
+
+import numpy as np
+
+from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayNumber
+
+
+def compute_ade(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNumber) -> NDArrayFloat:
+    """Compute the average displacement error for a set of K predicted trajectories (for the same actor).
+
+    Args:
+        forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
+        gt_trajectory: (N, 2) ground truth trajectory.
+
+    Returns:
+        (K,) Average displacement error for each of the predicted trajectories.
+    """
+    # (K,N)
+    displacement_errors = np.linalg.norm(forecasted_trajectories - gt_trajectory, axis=2)  # type: ignore
+    ade: NDArrayFloat = np.mean(displacement_errors, axis=1)
+    return ade
+
+
+def compute_fde(forecasted_trajectories: NDArrayNumber, gt_trajectory: NDArrayNumber) -> NDArrayFloat:
+    """Compute the final displacement error for a set of K predicted trajectories (for the same actor).
+
+    Args:
+        forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
+        gt_trajectory: (N, 2) ground truth trajectory, FDE will be evaluated against true position at index `N-1`.
+
+    Returns:
+        (K,) Final displacement error for each of the predicted trajectories.
+    """
+    # Compute final displacement error for all K trajectories
+    fde_vector = (forecasted_trajectories - gt_trajectory)[:, -1]  # type: ignore
+    fde: NDArrayFloat = np.linalg.norm(fde_vector, axis=-1)  # type: ignore
+    return fde
+
+
+def compute_is_missed_prediction(
+    forecasted_trajectories: NDArrayNumber,
+    gt_trajectory: NDArrayNumber,
+    miss_threshold_m: float = 2.0,
+) -> NDArrayBool:
+    """Compute whether each of K predicted trajectories (for the same actor) missed by more than a distance threshold.
+
+    Args:
+        forecasted_trajectories: (K, N, 2) predicted trajectories, each N timestamps in length.
+        gt_trajectory: (N, 2) ground truth trajectory, miss will be evaluated against true position at index `N-1`.
+        miss_threshold_m: Minimum distance threshold for final displacement to be considered a miss.
+
+    Returns:
+        (K,) Bools indicating whether prediction missed by more than specified threshold.
+    """
+    fde = compute_fde(forecasted_trajectories, gt_trajectory)
+    is_missed_prediction = fde > miss_threshold_m  # type: ignore
+    return is_missed_prediction
diff --git a/src/av2/datasets/motion_forecasting/scenario_serialization.py b/src/av2/datasets/motion_forecasting/scenario_serialization.py
new file mode 100644
index 00000000..bb38c82a
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/scenario_serialization.py
@@ -0,0 +1,176 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utilities to (de)serialize Argoverse forecasting scenarios."""
+
+from pathlib import Path
+from typing import List, Tuple
+
+import numpy as np
+import pandas as pd
+
+from av2.datasets.motion_forecasting.data_schema import ArgoverseScenario, ObjectState, ObjectType, Track, TrackCategory
+
+
+def serialize_argoverse_scenario_parquet(save_path: Path, scenario: ArgoverseScenario) -> None:
+    """Serialize a single Argoverse scenario in parquet format and save to disk.
+
+    Args:
+        save_path: Path where the scenario should be saved.
+        scenario: Scenario to serialize and save.
+    """
+    # Convert scenario data to DataFrame
+    tracks_df = _convert_tracks_to_tabular_format(scenario.tracks)
+    tracks_df["scenario_id"] = scenario.scenario_id
+    tracks_df["start_timestamp"] = scenario.timestamps_ns[0]
+    tracks_df["end_timestamp"] = scenario.timestamps_ns[-1]
+    tracks_df["num_timestamps"] = len(scenario.timestamps_ns)
+    tracks_df["focal_track_id"] = scenario.focal_track_id
+    tracks_df["city"] = scenario.city_name
+
+    if scenario.map_id is not None:
+        tracks_df["map_id"] = scenario.map_id
+    if scenario.slice_id is not None:
+        tracks_df["slice_id"] = scenario.slice_id
+
+    # Serialize the scenario dataframe to a parquet file
+    tracks_df.to_parquet(save_path)
+
+
+def load_argoverse_scenario_parquet(scenario_path: Path) -> ArgoverseScenario:
+    """Load a serialized Argoverse scenario from disk.
+
+    Args:
+        scenario_path: Path to the saved scenario in parquet format.
+
+    Raises:
+        FileNotFoundError: If no file exists at the specified scenario_path.
+
+    Returns:
+        Preprocessed scenario object that was saved at scenario_path.
+    """
+    if not Path(scenario_path).exists():
+        raise FileNotFoundError(f"No scenario exists at location: {scenario_path}.")
+
+    # Load scenario dataframe from parquet file
+    tracks_df = pd.read_parquet(scenario_path)
+
+    # Read track data and scenario metadata from scenario dataframe
+    tracks = _load_tracks_from_tabular_format(tracks_df)
+    scenario_id = tracks_df["scenario_id"][0]
+    focal_track_id = tracks_df["focal_track_id"][0]
+    city_name = tracks_df["city"][0]
+    map_id = tracks_df["map_id"][0] if "map_id" in tracks_df.columns else None
+    slice_id = tracks_df["slice_id"][0] if "slice_id" in tracks_df.columns else None
+
+    # Interpolate scenario timestamps based on the saved start and end timestamps
+    timestamps_ns = np.linspace(
+        tracks_df["start_timestamp"][0], tracks_df["end_timestamp"][0], num=tracks_df["num_timestamps"][0]
+    )
+
+    return ArgoverseScenario(
+        scenario_id=scenario_id,
+        timestamps_ns=timestamps_ns,
+        tracks=tracks,
+        focal_track_id=focal_track_id,
+        city_name=city_name,
+        map_id=map_id,
+        slice_id=slice_id,
+    )
+
+
+def _convert_tracks_to_tabular_format(tracks: List[Track]) -> pd.DataFrame:
+    """Convert tracks to tabular data format.
+
+    Args:
+        tracks: All tracks associated with the scenario.
+
+    Returns:
+        DataFrame containing all track data in a tabular format.
+    """
+    track_dfs: List[pd.DataFrame] = []
+
+    for track in tracks:
+
+        track_df = pd.DataFrame()
+
+        observed_states: List[bool] = []
+        timesteps: List[int] = []
+        positions_x: List[float] = []
+        positions_y: List[float] = []
+        headings: List[float] = []
+        velocities_x: List[float] = []
+        velocities_y: List[float] = []
+
+        for object_state in track.object_states:
+            observed_states.append(object_state.observed)
+            timesteps.append(object_state.timestep)
+            positions_x.append(object_state.position[0])
+            positions_y.append(object_state.position[1])
+            headings.append(object_state.heading)
+            velocities_x.append(object_state.velocity[0])
+            velocities_y.append(object_state.velocity[1])
+
+        track_df["observed"] = observed_states
+        track_df["track_id"] = track.track_id
+        track_df["object_type"] = track.object_type.value
+        track_df["object_category"] = track.category.value
+        track_df["timestep"] = timesteps
+        track_df["position_x"] = positions_x
+        track_df["position_y"] = positions_y
+        track_df["heading"] = headings
+        track_df["velocity_x"] = velocities_x
+        track_df["velocity_y"] = velocities_y
+
+        track_dfs.append(track_df)
+
+    return pd.concat(track_dfs, ignore_index=True)
+
+
+def _load_tracks_from_tabular_format(tracks_df: pd.DataFrame) -> List[Track]:
+    """Load tracks from tabular data format.
+
+    Args:
+        tracks_df: DataFrame containing all track data in a tabular format.
+
+    Returns:
+        All tracks associated with the scenario.
+    """
+    tracks: List[Track] = []
+
+    for track_id, track_df in tracks_df.groupby("track_id"):
+
+        observed_states: List[bool] = track_df.loc[:, "observed"].values.tolist()
+        object_type: ObjectType = ObjectType(track_df["object_type"].iloc[0])
+        object_category: TrackCategory = TrackCategory(track_df["object_category"].iloc[0])
+        timesteps: List[int] = track_df.loc[:, "timestep"].values.tolist()
+        positions: List[Tuple[float, float]] = list(
+            zip(
+                track_df.loc[:, "position_x"].values.tolist(),
+                track_df.loc[:, "position_y"].values.tolist(),
+            )
+        )
+        headings: List[float] = track_df.loc[:, "heading"].values.tolist()
+        velocities: List[Tuple[float, float]] = list(
+            zip(
+                track_df.loc[:, "velocity_x"].values.tolist(),
+                track_df.loc[:, "velocity_y"].values.tolist(),
+            )
+        )
+
+        object_states: List[ObjectState] = []
+        for idx in range(len(timesteps)):
+            object_states.append(
+                ObjectState(
+                    observed=observed_states[idx],
+                    timestep=timesteps[idx],
+                    position=positions[idx],
+                    heading=headings[idx],
+                    velocity=velocities[idx],
+                )
+            )
+
+        tracks.append(
+            Track(track_id=track_id, object_states=object_states, object_type=object_type, category=object_category)
+        )
+
+    return tracks
diff --git a/src/av2/datasets/motion_forecasting/viz/__init__.py b/src/av2/datasets/motion_forecasting/viz/__init__.py
new file mode 100644
index 00000000..cde1422c
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/viz/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Motion forecasting dataset subpackage."""
diff --git a/src/av2/datasets/motion_forecasting/viz/scenario_visualization.py b/src/av2/datasets/motion_forecasting/viz/scenario_visualization.py
new file mode 100644
index 00000000..9113e82f
--- /dev/null
+++ b/src/av2/datasets/motion_forecasting/viz/scenario_visualization.py
@@ -0,0 +1,249 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Visualization utils for Argoverse MF scenarios."""
+
+import io
+import math
+from pathlib import Path
+from typing import Final, List, Optional, Sequence, Set, Tuple
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.patches import Rectangle
+from PIL import Image as img
+from PIL.Image import Image
+
+from av2.datasets.motion_forecasting.data_schema import ArgoverseScenario, ObjectType, TrackCategory
+from av2.map.map_api import ArgoverseStaticMap
+from av2.utils.typing import NDArrayFloat, NDArrayInt
+
+_PlotBounds = Tuple[float, float, float, float]
+
+# Configure constants
+_OBS_DURATION_TIMESTEPS: Final[int] = 50
+_PRED_DURATION_TIMESTEPS: Final[int] = 60
+
+_ESTIMATED_VEHICLE_LENGTH_M: Final[float] = 4.0
+_ESTIMATED_VEHICLE_WIDTH_M: Final[float] = 2.0
+_ESTIMATED_CYCLIST_LENGTH_M: Final[float] = 2.0
+_ESTIMATED_CYCLIST_WIDTH_M: Final[float] = 0.7
+_PLOT_BOUNDS_BUFFER_M: Final[float] = 30.0
+
+_DRIVABLE_AREA_COLOR: Final[str] = "#7A7A7A"
+_LANE_SEGMENT_COLOR: Final[str] = "#E0E0E0"
+
+_DEFAULT_ACTOR_COLOR: Final[str] = "#D3E8EF"
+_FOCAL_AGENT_COLOR: Final[str] = "#ECA25B"
+_AV_COLOR: Final[str] = "#007672"
+_BOUNDING_BOX_ZORDER: Final[int] = 100  # Ensure actor bounding boxes are plotted on top of all map elements
+
+_STATIC_OBJECT_TYPES: Set[ObjectType] = {
+    ObjectType.STATIC,
+    ObjectType.BACKGROUND,
+    ObjectType.CONSTRUCTION,
+    ObjectType.RIDERLESS_BICYCLE,
+}
+
+
+def visualize_scenario(scenario: ArgoverseScenario, scenario_static_map: ArgoverseStaticMap, save_path: Path) -> None:
+    """Build dynamic visualization for all tracks and the local map associated with an Argoverse scenario.
+
+    Note: This function uses OpenCV to create a MP4 file using the MP4V codec.
+
+    Args:
+        scenario: Argoverse scenario to visualize.
+        scenario_static_map: Local static map elements associated with `scenario`.
+        save_path: Path where output MP4 video should be saved.
+    """
+    # Build each frame for the video
+    frames: List[Image] = []
+    plot_bounds: _PlotBounds = (0, 0, 0, 0)
+
+    for timestep in range(_OBS_DURATION_TIMESTEPS + _PRED_DURATION_TIMESTEPS):
+        _, ax = plt.subplots()
+
+        # Plot static map elements and actor tracks
+        _plot_static_map_elements(scenario_static_map)
+        cur_plot_bounds = _plot_actor_tracks(ax, scenario, timestep)
+        if cur_plot_bounds:
+            plot_bounds = cur_plot_bounds
+
+        # Set map bounds to capture focal trajectory history (with fixed buffer in all directions)
+        plt.xlim(plot_bounds[0] - _PLOT_BOUNDS_BUFFER_M, plot_bounds[1] + _PLOT_BOUNDS_BUFFER_M)
+        plt.ylim(plot_bounds[2] - _PLOT_BOUNDS_BUFFER_M, plot_bounds[3] + _PLOT_BOUNDS_BUFFER_M)
+        plt.gca().set_aspect("equal", adjustable="box")
+
+        # Minimize plot margins and make axes invisible
+        plt.gca().set_axis_off()
+        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+        plt.margins(0, 0)
+        plt.gca().xaxis.set_major_locator(plt.NullLocator())
+        plt.gca().yaxis.set_major_locator(plt.NullLocator())
+
+        # Save plotted frame to in-memory buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format="png")
+        plt.close()
+        buf.seek(0)
+        frame = img.open(buf)
+        frames.append(frame)
+
+    # Write buffered frames to MP4V-encoded video
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    vid_path = str(save_path.parents[0] / f"{save_path.stem}.mp4")
+    video = cv2.VideoWriter(vid_path, fourcc, fps=10, frameSize=frames[0].size)
+    for i in range(len(frames)):
+        frame_temp = frames[i].copy()
+        video.write(cv2.cvtColor(np.array(frame_temp), cv2.COLOR_RGB2BGR))
+    video.release()
+
+
+def _plot_static_map_elements(static_map: ArgoverseStaticMap, show_ped_xings: bool = False) -> None:
+    """Plot all static map elements associated with an Argoverse scenario.
+
+    Args:
+        static_map: Static map containing elements to be plotted.
+        show_ped_xings: Configures whether pedestrian crossings should be plotted.
+    """
+    # Plot drivable areas
+    for drivable_area in static_map.vector_drivable_areas.values():
+        _plot_polygons([drivable_area.xyz], alpha=0.5, color=_DRIVABLE_AREA_COLOR)
+
+    # Plot lane segments
+    for lane_segment in static_map.vector_lane_segments.values():
+        _plot_polylines(
+            [
+                lane_segment.left_lane_boundary.xyz,
+                lane_segment.right_lane_boundary.xyz,
+            ],
+            line_width=0.5,
+            color=_LANE_SEGMENT_COLOR,
+        )
+
+    # Plot pedestrian crossings
+    if show_ped_xings:
+        for ped_xing in static_map.vector_pedestrian_crossings.values():
+            _plot_polylines([ped_xing.edge1.xyz, ped_xing.edge2.xyz], alpha=1.0, color=_LANE_SEGMENT_COLOR)
+
+
+def _plot_actor_tracks(ax: plt.Axes, scenario: ArgoverseScenario, timestep: int) -> Optional[_PlotBounds]:
+    """Plot all actor tracks (up to a particular time step) associated with an Argoverse scenario.
+
+    Args:
+        ax: Axes on which actor tracks should be plotted.
+        scenario: Argoverse scenario for which to plot actor tracks.
+        timestep: Tracks are plotted for all actor data up to the specified time step.
+
+    Returns:
+        track_bounds: (x_min, x_max, y_min, y_max) bounds for the extent of actor tracks.
+    """
+    track_bounds = None
+    for track in scenario.tracks:
+        # Get timesteps for which actor data is valid
+        actor_timesteps: NDArrayInt = np.array(
+            [object_state.timestep for object_state in track.object_states if object_state.timestep <= timestep]
+        )
+        if actor_timesteps.shape[0] < 1 or actor_timesteps[-1] != timestep:
+            continue
+
+        # Get actor trajectory and heading history
+        actor_trajectory: NDArrayFloat = np.array(
+            [list(object_state.position) for object_state in track.object_states if object_state.timestep <= timestep]
+        )
+        actor_headings: NDArrayFloat = np.array(
+            [object_state.heading for object_state in track.object_states if object_state.timestep <= timestep]
+        )
+
+        # Plot polyline for focal agent location history
+        track_color = _DEFAULT_ACTOR_COLOR
+        if track.category == TrackCategory.FOCAL_TRACK:
+            x_min, x_max = actor_trajectory[:, 0].min(), actor_trajectory[:, 0].max()
+            y_min, y_max = actor_trajectory[:, 1].min(), actor_trajectory[:, 1].max()
+            track_bounds = (x_min, x_max, y_min, y_max)
+            track_color = _FOCAL_AGENT_COLOR
+            _plot_polylines([actor_trajectory], color=track_color, line_width=2)
+        elif track.track_id == "AV":
+            track_color = _AV_COLOR
+        elif track.object_type in _STATIC_OBJECT_TYPES:
+            continue
+
+        # Plot bounding boxes for all vehicles and cyclists
+        if track.object_type == ObjectType.VEHICLE:
+            _plot_actor_bounding_box(
+                ax,
+                actor_trajectory[-1],
+                actor_headings[-1],
+                track_color,
+                (_ESTIMATED_VEHICLE_LENGTH_M, _ESTIMATED_VEHICLE_WIDTH_M),
+            )
+        elif track.object_type == ObjectType.CYCLIST or track.object_type == ObjectType.MOTORCYCLIST:
+            _plot_actor_bounding_box(
+                ax,
+                actor_trajectory[-1],
+                actor_headings[-1],
+                track_color,
+                (_ESTIMATED_CYCLIST_LENGTH_M, _ESTIMATED_CYCLIST_WIDTH_M),
+            )
+        else:
+            plt.plot(actor_trajectory[-1, 0], actor_trajectory[-1, 1], "o", color=track_color, markersize=4)
+
+    return track_bounds
+
+
+def _plot_polylines(
+    polylines: Sequence[NDArrayFloat],
+    *,
+    style: str = "-",
+    line_width: float = 1.0,
+    alpha: float = 1.0,
+    color: str = "r",
+) -> None:
+    """Plot a group of polylines with the specified config.
+
+    Args:
+        polylines: Collection of (N, 2) polylines to plot.
+        style: Style of the line to plot (e.g. `-` for solid, `--` for dashed)
+        line_width: Desired width for the plotted lines.
+        alpha: Desired alpha for the plotted lines.
+        color: Desired color for the plotted lines.
+    """
+    for polyline in polylines:
+        plt.plot(polyline[:, 0], polyline[:, 1], style, linewidth=line_width, color=color, alpha=alpha)
+
+
+def _plot_polygons(polygons: Sequence[NDArrayFloat], *, alpha: float = 1.0, color: str = "r") -> None:
+    """Plot a group of filled polygons with the specified config.
+
+    Args:
+        polygons: Collection of polygons specified by (N,2) arrays of vertices.
+        alpha: Desired alpha for the polygon fill.
+        color: Desired color for the polygon.
+    """
+    for polygon in polygons:
+        plt.fill(polygon[:, 0], polygon[:, 1], color=color, alpha=alpha)
+
+
+def _plot_actor_bounding_box(
+    ax: plt.Axes, cur_location: NDArrayFloat, heading: float, color: str, bbox_size: Tuple[float, float]
+) -> None:
+    """Plot an actor bounding box centered on the actor's current location.
+
+    Args:
+        ax: Axes on which actor bounding box should be plotted.
+        cur_location: Current location of the actor (2,).
+        heading: Current heading of the actor (in radians).
+        color: Desired color for the bounding box.
+        bbox_size: Desired size for the bounding box (length, width).
+    """
+    (bbox_length, bbox_width) = bbox_size
+
+    # Compute coordinate for pivot point of bounding box
+    d = np.hypot(bbox_length, bbox_width)
+    theta_2 = math.atan2(bbox_width, bbox_length)
+    pivot_x = cur_location[0] - (d / 2) * math.cos(heading + theta_2)
+    pivot_y = cur_location[1] - (d / 2) * math.sin(heading + theta_2)
+
+    vehicle_bounding_box = Rectangle(
+        (pivot_x, pivot_y), bbox_length, bbox_width, np.degrees(heading), color=color, zorder=_BOUNDING_BOX_ZORDER
+    )
+    ax.add_patch(vehicle_bounding_box)
diff --git a/src/av2/datasets/sensor/README.md b/src/av2/datasets/sensor/README.md
new file mode 100644
index 00000000..6a73deed
--- /dev/null
+++ b/src/av2/datasets/sensor/README.md
@@ -0,0 +1,239 @@
+# Argoverse 2 Sensor Dataset Overview
+
+## Dataset Size
+
+The Argoverse 2 Sensor Dataset is the successor to the Argoverse 1 3D Tracking Dataset. AV2 is larger, with 1,000 scenes totalling 4.2 hours of driving data, up from 113 scenes in Argoverse 1.
+
+The total dataset amounts to 1 TB of data in its extracted form. Each vehicle log is approximately 15 seconds in duration and 1 GB in size, including ~150 LiDAR sweeps on average, and ~300 images from each of the 9 cameras (~2700 images per log).
+
+## Sensor Suite
+
+Lidar sweeps are collected at 10 Hz, along with 20 fps imagery from 7 ring cameras positioned to provide a fully panoramic field of view, and 20 fps imagery from 2 stereo cameras. In addition, camera intrinsics, extrinsics and 6-DOF ego-vehicle pose in a global coordinate system are provided. Lidar returns are captured by two 32-beam lidars, spinning at 10 Hz in the same direction, but separated in orientation by 180°. The cameras trigger in-sync with both lidars, leading to a 20 Hz frame-rate. The nine global shutter cameras are synchronized to the lidar to have their exposure centered on the lidar sweeping through their fields of view.
+
+We aggregate all returns from the two stacked 32-beam sensors into a single sweep. These sensors each have different, overlapping fields-of-view. Both lidars have their own reference frame, and we refer to them as `up_lidar` and `down_lidar`, respectively. We have egomotion-compensated the LiDAR sensor data to the egovehicle reference nanosecond timestamp. **All LiDAR returns are provided in the egovehicle reference frame, not the individual LiDAR reference frame**.
+
+Imagery is provided at (height x width) of  `2048 x 1550` (portrait orientation) for the ring front-center camera, and at `1550 x 2048` (landscape orientation) for all other 8 cameras (including the stereo cameras). **All camera imagery is provided in an undistorted format**.
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/158674314-f0c930a7-8f46-42e7-b849-3bbfd72b63db.png" height="400">
+</p>
+
+## Dataset Structure Format
+
+Tabular data (annotations, lidar sweeps, poses, calibration) are provided as [Apache Feather Files](https://arrow.apache.org/docs/python/feather.html) with the file extension `.feather`. We show examples below.
+
+## Annotations
+Object annotations are provided as 3d cuboids. Their pose is provided in the egovehicle's reference frame.
+
+```python
+io_utils.read_feather("{AV2_ROOT}/01bb304d-7bd8-35f8-bbef-7086b688e35e/annotations.feather")
+             timestamp_ns  track_uuid          category  length_m   width_m  height_m    qw   qx   qy    qz    tx_m   ty_m  tz_m  num_interior_pts
+0      315968867659956000 022c398c...           BOLLARD  0.363046  0.222484  0.746710  0.68  0.0  0.0  0.72   25.04  -2.55  0.01                10
+1      315968867659956000 12361d61...           BOLLARD  0.407004  0.206964  0.792624  0.68  0.0  0.0  0.72   34.13  -2.51 -0.05                 5
+2      315968867659956000 12cac1ed...           BOLLARD  0.337859  0.227949  0.747096  0.70  0.0  0.0  0.71   21.99  -2.55  0.03                13
+3      315968867659956000 173910b2...           BOLLARD  0.326865  0.204709  0.809859  0.71  0.0  0.0  0.69    3.79  -2.53  0.05                16
+4      315968867659956000 23716fb2...           BOLLARD  0.336697  0.226178  0.820867  0.72  0.0  0.0  0.69    6.78  -2.52  0.04                19
+...                   ...         ...               ...       ...       ...       ...   ...  ...  ...   ...     ...    ...   ...               ...
+18039  315968883159714000 c48fc856...          STROLLER  0.581798  0.502284  0.991001  0.97  0.0  0.0 -0.22  -10.84  34.33  0.14                13
+18040  315968883159714000 cf1c4301...             TRUCK  9.500000  3.010952  3.573860 -0.51  0.0  0.0  0.85  -26.97   0.09  1.41              1130
+18041  315968883159714000 a834bc72...         TRUCK_CAB  9.359874  3.260000  4.949222  0.51  0.0  0.0  0.85  138.13  13.39  0.80                18
+18042  315968883159714000 ff50196f... VEHICULAR_TRAILER  3.414590  2.658412  2.583414  0.84  0.0  0.0  0.52  -13.95   8.32  1.28               533
+18043  315968883159714000 a748a5c4...    WHEELED_DEVICE  1.078700  0.479100  1.215600  0.72  0.0  0.0  0.69   19.17  -6.07  0.28                 7
+```
+## Pose
+
+6-DOF ego-vehicle pose in a global (city) coordinate system is provided (visualized in the figure below as a red line, with red circles indicated at a 1 Hz frequency):
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/157916600-cd67a529-596e-4a75-bb99-d69bbfb9075b.jpg" height="800">
+</p>
+
+We refer to this pose as `city_SE3_egovehicle` throughout the codebase:
+
+```python
+>>> io_utils.read_feather("{AV2_ROOT}/54bc6dbc-ebfb-3fba-b5b3-57f88b4b79ca/city_SE3_egovehicle.feather")
+            timestamp_ns        qw        qx        qy        qz        tx_m         ty_m       tz_m
+0     315968112437425433 -0.740565 -0.005635 -0.006869 -0.671926  747.405602  1275.325609 -24.255610
+1     315968112442441182 -0.740385 -0.005626 -0.006911 -0.672124  747.411245  1275.385425 -24.255906
+2     315968112449927216 -0.740167 -0.005545 -0.006873 -0.672365  747.419676  1275.474686 -24.256406
+3     315968112449927217 -0.740167 -0.005545 -0.006873 -0.672365  747.419676  1275.474686 -24.256406
+4     315968112457428271 -0.739890 -0.005492 -0.006953 -0.672669  747.428448  1275.567576 -24.258680
+...                  ...       ...       ...       ...       ...         ...          ...        ...
+2692  315968128362451249 -0.694376 -0.001914 -0.006371 -0.719582  740.163738  1467.061503 -24.546971
+2693  315968128372412943 -0.694326 -0.001983 -0.006233 -0.719631  740.160489  1467.147020 -24.545918
+2694  315968128377482496 -0.694346 -0.001896 -0.006104 -0.719613  740.158684  1467.192399 -24.546316
+2695  315968128387425439 -0.694307 -0.001763 -0.005998 -0.719652  740.155543  1467.286735 -24.549918
+2696  315968128392441187 -0.694287 -0.001728 -0.005945 -0.719672  740.153742  1467.331549 -24.550363
+
+[2697 rows x 8 columns]
+```
+
+## LiDAR Sweeps
+
+For example, we show below the format of an example sweep `sensors/lidar/15970913559644000.feather` (the sweep has a reference timestamp of 15970913559644000 nanoseconds):
+
+```python
+               x          y         z  intensity  laser_number  offset_ns
+0      -1.291016   2.992188 -0.229370         24            31    3318000
+1     -25.921875  25.171875  0.992188          5            14    3318000
+2     -15.500000  18.937500  0.901855         34            16    3320303
+3      -3.140625   4.593750 -0.163696         12            30    3320303
+4      -4.445312   6.535156 -0.109802         14            29    3322607
+...          ...        ...       ...        ...           ...        ...
+98231  18.312500 -38.187500  3.279297         26            50  106985185
+98232  23.109375 -34.437500  3.003906         20            49  106987490
+98233   4.941406  -5.777344 -0.162720         12            32  106987490
+98234   6.640625  -8.257812 -0.157593          6            33  106989794
+98235  20.015625 -37.062500  2.550781         12            47  106989794
+
+[98236 rows x 6 columns]
+```
+
+## Calibration
+
+An example calibration file is shown below, parameterizing `vehicle_SE3_sensor` for each sensor (the sensor's pose in the egovehicle coordinate system):
+
+```python
+>>> io_utils.read_feather(f"{AV2_ROOT}/54bc6dbc-ebfb-3fba-b5b3-57f88b4b79ca/calibration/egovehicle_SE3_sensor.feather")
+           sensor_name        qw        qx        qy        qz      tx_m      ty_m      tz_m
+0    ring_front_center  0.502809 -0.499689  0.500147 -0.497340  1.631216 -0.000779  1.432780
+1      ring_front_left  0.635526 -0.671957  0.275463 -0.262107  1.550015  0.197539  1.431329
+2     ring_front_right  0.264354 -0.278344  0.671740 -0.633567  1.554057 -0.194171  1.430575
+3       ring_rear_left  0.600598 -0.603227 -0.371096  0.371061  1.104117  0.124369  1.446070
+4      ring_rear_right -0.368149  0.369885  0.603626 -0.602733  1.103432 -0.128317  1.428135
+5       ring_side_left  0.684152 -0.724938 -0.058345  0.054735  1.310427  0.267904  1.433233
+6      ring_side_right -0.053810  0.056105  0.727113 -0.682103  1.310236 -0.273345  1.435529
+7    stereo_front_left  0.500421 -0.499934  0.501241 -0.498399  1.625085  0.248148  1.222831
+8   stereo_front_right  0.500885 -0.503584  0.498793 -0.496713  1.633076 -0.250872  1.222173
+9             up_lidar  0.999996  0.000000  0.000000 -0.002848  1.350180  0.000000  1.640420
+10          down_lidar -0.000089 -0.994497  0.104767  0.000243  1.355162  0.000133  1.565252
+```
+
+## Intrinsics
+
+An example camera intrinsics file is shown below:
+
+```python
+>>> io_utils.read_feather("{AV2_ROOT}/54bc6dbc-ebfb-3fba-b5b3-57f88b4b79ca/calibration/intrinsics.feather")
+          sensor_name        fx_px        fy_px        cx_px  ...        k2        k3  height_px  width_px
+0   ring_front_center  1773.504272  1773.504272   775.826693  ... -0.212167  0.328694       2048      1550
+1     ring_front_left  1682.010713  1682.010713  1025.068254  ... -0.136984  0.209330       1550      2048
+2    ring_front_right  1684.834479  1684.834479  1024.373455  ... -0.133341  0.208709       1550      2048
+3      ring_rear_left  1686.494558  1686.494558  1025.655905  ... -0.129761  0.202326       1550      2048
+4     ring_rear_right  1683.375120  1683.375120  1024.381124  ... -0.129331  0.201599       1550      2048
+5      ring_side_left  1684.902403  1684.902403  1027.822264  ... -0.124561  0.196519       1550      2048
+6     ring_side_right  1682.936559  1682.936559  1024.948976  ... -0.109515  0.179383       1550      2048
+7   stereo_front_left  1685.825885  1685.825885  1025.830335  ... -0.113065  0.182441       1550      2048
+8  stereo_front_right  1683.137591  1683.137591  1024.612074  ... -0.127301  0.198538       1550      2048
+```
+
+A local map is provided per log, please refer to the [Map README](../../map/README.md) for additional details.
+
+## Privacy
+
+All faces and license plates, whether inside vehicles or outside of the drivable area, are blurred extensively to preserve privacy.
+
+## Sensor Dataset splits
+
+We randomly partitioned 1000 logs into the following splits:
+
+- Train (750 logs)
+- Validation (150 logs)
+- Test (150 logs)
+
+## Sensor Dataset Taxonomy
+
+The AV2 Sensor Dataset contains 10 Hz 3D cuboid annotations for objects within our 30 class taxonomy. Objects are annotated if they are within the “region of interest” (ROI) – within five meters of the mapped “driveable” area.
+
+![cuboids-by-category](https://user-images.githubusercontent.com/29715011/158675968-3987b2bc-ed8b-4194-85ca-44298805cfb2.png)
+
+These 30 classes are defined as follows, appearing in order of frequency:
+
+1. `REGULAR_VEHICLE`:
+Any conventionally sized passenger vehicle used for the transportation of people and cargo. This includes Cars, vans, pickup trucks, SUVs, etc.
+
+2. ``PEDESTRIAN``:
+Person that is not driving or riding in/on a vehicle. They can be walking, standing, sitting, prone, etc.
+
+3. `BOLLARD`:
+Bollards are short, sturdy posts installed in the roadway or sidewalk to control the flow of traffic. These may be temporary or permanent and are sometimes decorative.
+
+4. `CONSTRUCTION_CONE`:
+Movable traffic cone that is used to alert drivers to a hazard.  These will typically be orange and white striped and may or may not have a blinking light attached to the top.
+
+5. `CONSTRUCTION_BARREL`:
+Construction Barrel is a movable traffic barrel that is used to alert drivers to a hazard.  These will typically be orange and white striped and may or may not have a blinking light attached to the top.
+
+6. `STOP_SIGN`:
+Red octagonal traffic sign displaying the word STOP used to notify drivers that they must come to a complete stop and make sure no other road users are coming before proceeding.
+
+7. `BICYCLE`:
+Non-motorized vehicle that typically has two wheels and is propelled by human power pushing pedals in a circular motion.
+
+8. `LARGE_VEHICLE`:
+Large motorized vehicles (four wheels or more) which do not fit into any more specific subclass. Examples include extended passenger vans, fire trucks, RVs, etc.
+
+9. `WHEELED_DEVICE`:
+Objects involved in the transportation of a person and do not fit a more specific class. Examples range from skateboards, non-motorized scooters, segways, to golf-carts.
+
+10. `BUS`:
+Standard city buses designed to carry a large number of people.
+
+11. `BOX_TRUCK`:
+Chassis cab truck with an enclosed cube shaped cargo area. It should be noted that the cargo area is rigidly attached to the cab, and they do not articulate.
+
+12. `SIGN`:
+Official road signs placed by the Department of Transportation (DOT signs) which are of interest to us. This includes yield signs, speed limit signs, directional control signs, construction signs, and other signs that provide required traffic control information. Note that Stop Sign is captured separately and informative signs such as street signs, parking signs, bus stop signs, etc. are not included in this class.
+
+13. `TRUCK`:
+Vehicles that are clearly defined as a truck but does not fit into the subclasses of Box Truck or Truck Cab. Examples include common delivery vehicles (UPS, FedEx), mail trucks, garbage trucks, utility trucks, ambulances, dump trucks, etc.
+
+14. `MOTORCYCLE`:
+Motorized vehicle with two wheels where the rider straddles the engine.  These are capable of high speeds similar to a car.
+
+15. `BICYCLIST`:
+Person actively riding a bicycle, non-pedaling passengers included.
+
+16. `VEHICULAR_TRAILER`:
+Non-motorized, wheeled vehicle towed behind a motorized vehicle.
+
+17. `TRUCK_CAB`:
+Heavy truck commonly known as “Semi cab”, “Tractor”, or “Lorry”. This refers to only the front of part of an articulated tractor trailer.
+
+18. `MOTORCYCLIST`:
+Person actively riding a motorcycle or a moped, including passengers.
+
+19. `DOG`:
+Any member of the canine family.
+
+20. `SCHOOL_BUS`:
+Bus that primarily holds school children (typically yellow) and can control the flow of traffic via the use of an articulating stop sign and loading/unloading flasher lights.
+
+21. `WHEELED_RIDER`:
+Person actively riding or being carried by a wheeled device.
+
+22. `STROLLER`:
+Push-cart with wheels meant to hold a baby or toddler.
+
+23. `ARTICULATED_BUS`:
+Articulated buses perform the same function as a standard city bus, but are able to bend (articulate) towards the center. These will also have a third set of wheels not present on a typical bus.
+
+24. `MESSAGE_BOARD_TRAILER`:
+Trailer carrying a large, mounted, electronic sign to display messages. Often found around construction sites or large events.
+
+25. `MOBILE_PEDESTRIAN_SIGN`:
+Movable sign designating an area where pedestrians may cross the road.
+
+26. `WHEELCHAIR`:
+Chair fitted with wheels for use as a means of transport by a person who is unable to walk as a result of illness, injury, or disability. This includes both motorized and non-motorized wheelchairs as well as low-speed seated scooters not intended for use on the roadway.
+
+27. `RAILED_VEHICLE`:
+Any vehicle that relies on rails to move. This applies to trains, trolleys, train engines, train freight cars, train tanker cars, subways, etc.
+
+28. `OFFICIAL_SIGNALER`:
+Person with authority specifically responsible for stopping and directing vehicles through traffic.
+
+29. `TRAFFIC_LIGHT_TRAILER`:
+Mounted, portable traffic light unit commonly used in construction zones or for other temporary detours.
+
+30. `ANIMAL`:
+All recognized animals large enough to affect traffic, but that do not fit into the Cat, Dog, or Horse categories
diff --git a/src/av2/datasets/sensor/__init__.py b/src/av2/datasets/sensor/__init__.py
new file mode 100644
index 00000000..8986d95b
--- /dev/null
+++ b/src/av2/datasets/sensor/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Sensor dataset submodule."""
diff --git a/src/av2/datasets/sensor/av2_sensor_dataloader.py b/src/av2/datasets/sensor/av2_sensor_dataloader.py
new file mode 100644
index 00000000..bb1d4d4b
--- /dev/null
+++ b/src/av2/datasets/sensor/av2_sensor_dataloader.py
@@ -0,0 +1,410 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Implements a dataloader for the Argoverse 2.0 Sensor Dataset."""
+
+import logging
+from pathlib import Path
+from typing import Final, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+import av2.geometry.geometry as geometry_utils
+import av2.utils.dense_grid_interpolation as dense_grid_interpolation
+import av2.utils.io as io_utils
+from av2.datasets.sensor.constants import RingCameras
+from av2.geometry.camera.pinhole_camera import PinholeCamera
+from av2.geometry.se3 import SE3
+from av2.structures.cuboid import CuboidList
+from av2.structures.sweep import Sweep
+from av2.utils.synchronization_database import SynchronizationDB
+from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat
+
+logger = logging.getLogger(__name__)
+
+# number of nanoseconds in a single second.
+NANOSECONDS_PER_SECOND: Final[int] = int(1e9)
+MAX_MEASUREMENT_FREQUENCY_HZ: Final[int] = NANOSECONDS_PER_SECOND
+
+
+class AV2SensorDataLoader:
+    """Simple abstraction for retrieving log data, given a path to the dataset."""
+
+    def __init__(self, data_dir: Path, labels_dir: Path) -> None:
+        """Create the Sensor dataloader from a data directory and labels directory.
+
+        Args:
+            data_dir: Path to raw Argoverse 2.0 data
+            labels_dir: Path to Argoverse 2.0 data labels (e.g. labels or estimated detections/tracks)
+
+        Raises:
+            ValueError: if input arguments are not Path objects.
+        """
+        if not isinstance(data_dir, Path) or not isinstance(labels_dir, Path):
+            raise ValueError("Input arguments must be Path objects, representing paths to local directories")
+
+        self._data_dir = data_dir
+        self._labels_dir = labels_dir
+        self._sdb = SynchronizationDB(str(data_dir))
+
+    def get_city_name(self, log_id: str) -> str:
+        """Return the name of the city where the log of interest was captured."""
+        raise NotImplementedError("Method is not available yet.")
+
+    def get_log_pinhole_camera(self, log_id: str, cam_name: str) -> PinholeCamera:
+        """Return a PinholeCamera parameterized by sensor pose in vehicle frame, intrinsics, and image dimensions."""
+        log_dir = self._data_dir / log_id
+        return PinholeCamera.from_feather(log_dir=log_dir, cam_name=cam_name)
+
+    def get_city_SE3_ego(self, log_id: str, timestamp_ns: int) -> SE3:
+        """Obtain the egovehicle's pose in the city reference frame.
+
+        Note: the poses dataframe contains tables with columns {timestamp_ns, qw, qx, qy, qz, tx_m, ty_m, tz_m}.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            timestamp_ns: timestamp of sensor observation, in nanoseconds.
+
+        Returns:
+            SE(3) transformation to bring points in egovehicle frame into city frame.
+
+        Raises:
+            RuntimeError: If no recorded pose is available for the requested timestamp.
+        """
+        log_poses_df = io_utils.read_feather(self._data_dir / log_id / "city_SE3_egovehicle.feather")
+        pose_df = log_poses_df.loc[log_poses_df["timestamp_ns"] == timestamp_ns]
+
+        if len(pose_df) == 0:
+            raise RuntimeError("Pose was not available for the requested timestamp.")
+
+        city_SE3_ego = convert_pose_dataframe_to_SE3(pose_df)
+        return city_SE3_ego
+
+    def get_subsampled_ego_trajectory(self, log_id: str, sample_rate_hz: float = 1.0) -> NDArrayFloat:
+        """Get the trajectory of the AV (egovehicle) at an approximate sampling rate (Hz).
+
+        Note: the trajectory is NOT interpolated to give an *exact* sampling rate.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            sample_rate_hz: provide sample_rate_hz pose measurements per second. We approximate this
+                by providing a new pose after a required interval has elapsed. Since pose measurements
+                are provided at a high frequency, this is adequate for the purposes of visualization.
+
+        Returns:
+            array of shape (N,2) representing autonomous vehicle's (AV) trajectory.
+
+        Raises:
+            ValueError: If pose timestamps aren't in chronological order.
+        """
+        if sample_rate_hz > MAX_MEASUREMENT_FREQUENCY_HZ:
+            logger.warning(
+                "You requested at sampling rate of %d Hz, but the measurements are only at nanosecond precision, "
+                " so falling back to %d Hz.",
+                sample_rate_hz,
+                MAX_MEASUREMENT_FREQUENCY_HZ,
+            )
+
+        log_poses_df = io_utils.read_feather(self._data_dir / log_id / "city_SE3_egovehicle.feather")
+
+        # timestamp of the pose measurement.
+        timestamp_ns = list(log_poses_df.timestamp_ns)
+        tx_m = list(log_poses_df.tx_m)
+        ty_m = list(log_poses_df.ty_m)
+
+        if not np.array_equal(np.argsort(timestamp_ns), np.arange(len(timestamp_ns))):
+            raise ValueError("Pose timestamps are not sorted chronologically, invalid.")
+
+        # e.g. for 2 Hz, get a sampling rate of 500 ms, then convert to nanoseconds.
+        interval_threshold_s = 1 / sample_rate_hz
+        # addition must happen in integer domain, not float domain, to prevent overflow
+        interval_threshold_ns = int(interval_threshold_s * NANOSECONDS_PER_SECOND)
+
+        next_timestamp = timestamp_ns[0] + interval_threshold_ns
+        traj = [(tx_m[0], ty_m[0])]
+
+        for timestamp_, tx_, ty_ in zip(timestamp_ns[1:], tx_m[1:], ty_m[1:]):
+            if timestamp_ < next_timestamp:
+                # still within last interval, need to exit this interval before sampling new measurement.
+                continue
+            traj += [(tx_, ty_)]
+            next_timestamp = timestamp_ + interval_threshold_ns
+
+        traj_npy: NDArrayFloat = np.array(traj, dtype=float)
+        return traj_npy
+
+    def get_log_map_dirpath(self, log_id: str) -> Path:
+        """Fetch the path to the directory containing map files for a single vehicle log."""
+        return self._data_dir / log_id / "map"
+
+    def get_log_ids(self) -> List[str]:
+        """Return a list of all vehicle log IDs available at the provided dataroot."""
+        return sorted([d.name for d in self._data_dir.glob("*") if d.is_dir()])
+
+    def get_closest_img_fpath(self, log_id: str, cam_name: str, lidar_timestamp_ns: int) -> Optional[Path]:
+        """Return the filepath corresponding to the closest image from the lidar timestamp.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            cam_name: name of camera.
+            lidar_timestamp_ns: timestamp of LiDAR sweep capture, in nanoseconds
+
+        Returns:
+            im_fpath: path to image if one is found within the expected time interval, or else None.
+        """
+        cam_timestamp_ns = self._sdb.get_closest_cam_channel_timestamp(lidar_timestamp_ns, cam_name, log_id)
+        if cam_timestamp_ns is None:
+            return None
+        img_fpath = self._data_dir / log_id / "sensors" / "cameras" / cam_name / f"{cam_timestamp_ns}.jpg"
+        return img_fpath
+
+    def get_closest_lidar_fpath(self, log_id: str, cam_timestamp_ns: int) -> Optional[Path]:
+        """Get file path for LiDAR sweep accumulated to a timestamp closest to a camera timestamp.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            cam_timestamp_ns: integer timestamp of image capture, in nanoseconds
+
+        Returns:
+            lidar_fpath: path to sweep .feather file if one is found within the expected time interval, or else None.
+        """
+        lidar_timestamp_ns = self._sdb.get_closest_lidar_timestamp(cam_timestamp_ns, log_id)
+        if lidar_timestamp_ns is None:
+            return None
+        lidar_fname = f"{lidar_timestamp_ns}.feather"
+        lidar_fpath = self._data_dir / log_id / "sensors" / "lidar" / lidar_fname
+        return lidar_fpath
+
+    def get_lidar_fpath_at_lidar_timestamp(self, log_id: str, lidar_timestamp_ns: int) -> Optional[Path]:
+        """Return the file path for the LiDAR sweep accumulated to the query timestamp, if it exists.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            lidar_timestamp_ns: timestamp of LiDAR sweep capture, in nanoseconds
+
+        Returns:
+            lidar_fpath: path to sweep .feather file if one exists at the requested timestamp, or else None.
+        """
+        lidar_fname = f"{lidar_timestamp_ns}.feather"
+        lidar_fpath = self._data_dir / log_id / "sensors" / "lidar" / lidar_fname
+        if not lidar_fpath.exists():
+            return None
+
+        return lidar_fpath
+
+    def get_ordered_log_lidar_fpaths(self, log_id: str) -> List[Path]:
+        """Get a list of all file paths for LiDAR sweeps in a single log (ordered chronologically).
+
+        Args:
+            log_id: unique ID of vehicle log.
+
+        Returns:
+            lidar_fpaths: List of paths to chronologically ordered LiDAR feather files in this log.
+                File paths are strings are of the same length ending with a nanosecond timestamp, thus
+                sorted() will place them in numerical order.
+        """
+        lidar_fpaths = sorted(self._data_dir.glob(f"{log_id}/sensors/lidar/*.feather"))
+        return lidar_fpaths
+
+    def get_ordered_log_cam_fpaths(self, log_id: str, cam_name: str) -> List[Path]:
+        """Get a list of all file paths for one particular camera in a single log (ordered chronologically).
+
+        Args:
+            log_id: unique ID of vehicle log.
+            cam_name: camera name.
+
+        Returns:
+            List of paths, representing paths to ordered JPEG files in this log, for a specific camera.
+        """
+        cam_img_fpaths = sorted(self._data_dir.glob(f"{log_id}/sensors/cameras/{cam_name}/*.jpg"))
+        return cam_img_fpaths
+
+    def get_labels_at_lidar_timestamp(self, log_id: str, lidar_timestamp_ns: int) -> CuboidList:
+        """Load the sweep annotations at the provided timestamp.
+
+        Args:
+            log_id: Log unique id.
+            lidar_timestamp_ns: Nanosecond timestamp.
+
+        Returns:
+            Cuboid list of annotations.
+        """
+        annotations_feather_path = self._data_dir / log_id / "annotations.feather"
+
+        # Load annotations from disk.
+        # NOTE: This file contains annotations for the ENTIRE sequence.
+        # The sweep annotations are selected below.
+        cuboid_list = CuboidList.from_feather(annotations_feather_path)
+        cuboids = list(filter(lambda x: x.timestamp_ns == lidar_timestamp_ns, cuboid_list.cuboids))
+        return CuboidList(cuboids=cuboids)
+
+    def project_ego_to_img_motion_compensated(
+        self,
+        points_lidar_time: NDArrayFloat,
+        cam_name: str,
+        cam_timestamp_ns: int,
+        lidar_timestamp_ns: int,
+        log_id: str,
+    ) -> Tuple[NDArrayFloat, NDArrayFloat, NDArrayBool]:
+        """Project points in the ego frame to the image with motion compensation.
+
+        Args:
+            points_lidar_time: Numpy array of shape (N,3) representing points in the egovehicle frame.
+            cam_name: name of camera.
+            cam_timestamp_ns: timestamp (in nanoseconds) when camera image was recorded.
+            lidar_timestamp_ns: timestamp (in nanoseconds) when LiDAR sweep was recorded.
+            log_id: unique ID of vehicle log.
+
+        Returns:
+            uv: image plane coordinates, as Numpy array of shape (N,2).
+            points_cam: Numpy array of shape (N,3) representing coordinates of points within the camera frame.
+            is_valid_points: boolean indicator of valid cheirality and within image boundary, as
+                boolean Numpy array of shape (N,).
+        """
+        pinhole_camera = self.get_log_pinhole_camera(log_id=log_id, cam_name=cam_name)
+
+        # get transformation to bring point in egovehicle frame to city frame,
+        # at the time when camera image was recorded.
+        city_SE3_ego_cam_t = self.get_city_SE3_ego(log_id=log_id, timestamp_ns=cam_timestamp_ns)
+
+        # get transformation to bring point in egovehicle frame to city frame,
+        # at the time when the LiDAR sweep was recorded.
+        city_SE3_ego_lidar_t = self.get_city_SE3_ego(log_id=log_id, timestamp_ns=lidar_timestamp_ns)
+
+        return pinhole_camera.project_ego_to_img_motion_compensated(
+            points_lidar_time=points_lidar_time,
+            city_SE3_ego_cam_t=city_SE3_ego_cam_t,
+            city_SE3_ego_lidar_t=city_SE3_ego_lidar_t,
+        )
+
+    def get_colored_sweep(self, log_id: str, lidar_timestamp_ns: int) -> NDArrayByte:
+        """Given a LiDAR sweep, use its corresponding RGB imagery to color the sweep points.
+
+        If a sweep points is not captured by any ring camera, it will default to black color.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            lidar_timestamp_ns: timestamp (in nanoseconds) when LiDAR sweep was recorded.
+
+        Returns:
+            Array of shape (N,3) representing RGB colors per sweep point.
+
+        Raises:
+            ValueError: If requested timestamp has no corresponding LiDAR sweep.
+        """
+        lidar_fpath = self.get_lidar_fpath_at_lidar_timestamp(log_id=log_id, lidar_timestamp_ns=lidar_timestamp_ns)
+        if lidar_fpath is None:
+            raise ValueError("Requested colored sweep at a timestamp that has no corresponding LiDAR sweep.")
+
+        sweep = Sweep.from_feather(lidar_fpath)
+        n_sweep_pts = len(sweep)
+        # defaults to black RGB (0,0,0)
+        sweep_rgb: NDArrayByte = np.zeros((n_sweep_pts, 3), dtype=np.uint8)
+
+        # color as much of the sweep that we can, as we loop through each camera.
+        for cam_enum in list(RingCameras):
+            cam_name = cam_enum.value
+            img_fpath = self.get_closest_img_fpath(
+                log_id=log_id, cam_name=cam_name, lidar_timestamp_ns=lidar_timestamp_ns
+            )
+            if img_fpath is None:
+                continue
+            cam_timestamp_ns = int(img_fpath.stem)
+
+            uv, points_cam, is_valid = self.project_ego_to_img_motion_compensated(
+                points_lidar_time=sweep.xyz,
+                cam_name=cam_name,
+                cam_timestamp_ns=cam_timestamp_ns,
+                lidar_timestamp_ns=lidar_timestamp_ns,
+                log_id=log_id,
+            )
+            uv_valid = np.round(uv[is_valid]).astype(np.int64)  # type: ignore
+            u = uv_valid[:, 0]
+            v = uv_valid[:, 1]
+            img = io_utils.read_img(img_fpath, channel_order="RGB")
+            sweep_rgb[is_valid] = img[v, u]
+
+        return sweep_rgb
+
+    def get_depth_map_from_lidar(
+        self,
+        lidar_points: NDArrayFloat,
+        cam_name: str,
+        log_id: str,
+        cam_timestamp_ns: int,
+        lidar_timestamp_ns: int,
+        interp_depth_map: bool = True,
+    ) -> Optional[NDArrayFloat]:
+        """Create a sparse or dense depth map, with height & width equivalent to the corresponding camera image.
+
+        Args:
+            lidar_points: array of shape (K,3)
+            cam_name: name of camera from which to simulate a per-pixel depth map.
+            log_id: unique identifier of log/scenario.
+            cam_timestamp_ns: timestamp when image was captured, measured in nanoseconds.
+            lidar_timestamp_ns: timestamp when LiDAR was captured, measured in nanoseconds.
+            interp_depth_map: whether to densely interpolate the depth map from sparse LiDAR returns.
+
+        Returns:
+            depth_map: array of shape (height_px, width_px) representing a depth map.
+
+        Raises:
+            RuntimeError: If `u` or `v` are outside of the camera dimensions.
+        """
+        pinhole_camera = self.get_log_pinhole_camera(log_id=log_id, cam_name=cam_name)
+        height_px, width_px = pinhole_camera.height_px, pinhole_camera.width_px
+
+        # motion compensate always
+        uv, points_cam, is_valid_points = self.project_ego_to_img_motion_compensated(
+            points_lidar_time=lidar_points,
+            cam_name=cam_name,
+            cam_timestamp_ns=cam_timestamp_ns,
+            lidar_timestamp_ns=lidar_timestamp_ns,
+            log_id=log_id,
+        )
+        if uv is None or points_cam is None:
+            # poses were missing for either the camera or lidar timestamp
+            return None
+        if is_valid_points is None or is_valid_points.sum() == 0:
+            return None
+
+        u = np.round(uv[:, 0][is_valid_points]).astype(np.int32)  # type: ignore
+        v = np.round(uv[:, 1][is_valid_points]).astype(np.int32)  # type: ignore
+        z = points_cam[:, 2][is_valid_points]
+
+        depth_map: NDArrayFloat = np.zeros((height_px, width_px), dtype=np.float32)
+
+        # form depth map from LiDAR
+        if interp_depth_map:
+            if u.max() > pinhole_camera.width_px or v.max() > pinhole_camera.height_px:
+                raise RuntimeError("Regular grid interpolation will fail due to out-of-bound inputs.")
+
+            depth_map = dense_grid_interpolation.interp_dense_grid_from_sparse(
+                grid_img=depth_map,
+                points=np.hstack([u.reshape(-1, 1), v.reshape(-1, 1)]),
+                values=z,
+                grid_h=height_px,
+                grid_w=width_px,
+                interp_method="linear",
+            ).astype(float)
+        else:
+            depth_map[v, u] = z
+
+        return depth_map
+
+
+def convert_pose_dataframe_to_SE3(pose_df: pd.DataFrame) -> SE3:
+    """Convert a dataframe with parameterization of a single pose, to an SE(3) object.
+
+    Args:
+        pose_df: parameterization of a single pose.
+
+    Returns:
+        SE(3) object representing the egovehicle's pose in the city frame.
+    """
+    qw, qx, qy, qz = pose_df[["qw", "qx", "qy", "qz"]].to_numpy().squeeze()
+    tx_m, ty_m, tz_m = pose_df[["tx_m", "ty_m", "tz_m"]].to_numpy().squeeze()
+    city_q_ego: NDArrayFloat = np.array([qw, qx, qy, qz])
+    city_t_ego: NDArrayFloat = np.array([tx_m, ty_m, tz_m])
+    city_R_ego = geometry_utils.quat_to_mat(quat_wxyz=city_q_ego)
+    city_SE3_ego = SE3(rotation=city_R_ego, translation=city_t_ego)
+    return city_SE3_ego
diff --git a/src/av2/datasets/sensor/constants.py b/src/av2/datasets/sensor/constants.py
new file mode 100644
index 00000000..fa33eeb4
--- /dev/null
+++ b/src/av2/datasets/sensor/constants.py
@@ -0,0 +1,70 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Constants relevant to the sensors and log data of the Argoverse 2 Sensor Dataset."""
+
+from enum import Enum, unique
+
+
+@unique
+class RingCameras(str, Enum):
+    """Ring Camera names."""
+
+    RING_REAR_LEFT = "ring_rear_left"
+    RING_SIDE_LEFT = "ring_side_left"
+    RING_FRONT_LEFT = "ring_front_left"
+    RING_FRONT_CENTER = "ring_front_center"
+    RING_FRONT_RIGHT = "ring_front_right"
+    RING_SIDE_RIGHT = "ring_side_right"
+    RING_REAR_RIGHT = "ring_rear_right"
+
+
+@unique
+class StereoCameras(str, Enum):
+    """Stereo camera names."""
+
+    STEREO_FRONT_LEFT = "stereo_front_left"
+    STEREO_FRONT_RIGHT = "stereo_front_right"
+
+
+@unique
+class Cameras(str, Enum):
+    """All cameras."""
+
+    RING_CAMERAS = RingCameras
+    STEREO_CAMERAS = StereoCameras
+
+
+@unique
+class AnnotationCategories(str, Enum):
+    """Sensor dataset annotation categories."""
+
+    ANIMAL = "ANIMAL"
+    ARTICULATED_BUS = "ARTICULATED_BUS"
+    BICYCLE = "BICYCLE"
+    BICYCLIST = "BICYCLIST"
+    BOLLARD = "BOLLARD"
+    BOX_TRUCK = "BOX_TRUCK"
+    BUS = "BUS"
+    CONSTRUCTION_BARREL = "CONSTRUCTION_BARREL"
+    CONSTRUCTION_CONE = "CONSTRUCTION_CONE"
+    DOG = "DOG"
+    LARGE_VEHICLE = "LARGE_VEHICLE"
+    MESSAGE_BOARD_TRAILER = "MESSAGE_BOARD_TRAILER"
+    MOBILE_PEDESTRIAN_CROSSING_SIGN = "MOBILE_PEDESTRIAN_CROSSING_SIGN"
+    MOTORCYCLE = "MOTORCYCLE"
+    MOTORCYCLIST = "MOTORCYCLIST"
+    OFFICIAL_SIGNALER = "OFFICIAL_SIGNALER"
+    PEDESTRIAN = "PEDESTRIAN"
+    RAILED_VEHICLE = "RAILED_VEHICLE"
+    REGULAR_VEHICLE = "REGULAR_VEHICLE"
+    SCHOOL_BUS = "SCHOOL_BUS"
+    SIGN = "SIGN"
+    STOP_SIGN = "STOP_SIGN"
+    STROLLER = "STROLLER"
+    TRAFFIC_LIGHT_TRAILER = "TRAFFIC_LIGHT_TRAILER"
+    TRUCK = "TRUCK"
+    TRUCK_CAB = "TRUCK_CAB"
+    VEHICULAR_TRAILER = "VEHICULAR_TRAILER"
+    WHEELCHAIR = "WHEELCHAIR"
+    WHEELED_DEVICE = "WHEELED_DEVICE"
+    WHEELED_RIDER = "WHEELED_RIDER"
diff --git a/src/av2/datasets/sensor/sensor_dataloader.py b/src/av2/datasets/sensor/sensor_dataloader.py
new file mode 100644
index 00000000..fe221149
--- /dev/null
+++ b/src/av2/datasets/sensor/sensor_dataloader.py
@@ -0,0 +1,516 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Dataloader for the Argoverse 2 (AV2) sensor dataset."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from functools import cached_property
+from pathlib import Path
+from typing import Dict, Final, List, Optional, Tuple, Union
+
+import numpy as np
+import pandas as pd
+from rich.progress import track
+
+from av2.datasets.sensor.constants import RingCameras, StereoCameras
+from av2.datasets.sensor.utils import convert_path_to_named_record
+from av2.geometry.camera.pinhole_camera import PinholeCamera
+from av2.structures.cuboid import CuboidList
+from av2.structures.sweep import Sweep
+from av2.structures.timestamped_image import TimestampedImage
+from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego, read_feather, read_img
+from av2.utils.metric_time import TimeUnit, to_metric_time
+
+logger = logging.Logger(__name__)
+
+LIDAR_PATTERN: Final[str] = "*/sensors/lidar/*.feather"
+CAMERA_PATTERN: Final[str] = "*/sensors/cameras/*/*.jpg"
+
+Millisecond = TimeUnit.Millisecond
+Nanosecond = TimeUnit.Nanosecond
+Second = TimeUnit.Second
+
+# for both ring cameras, and stereo cameras.
+CAM_FPS: Final[int] = 20
+LIDAR_FRAME_RATE_HZ: Final[int] = 10
+
+# constants defined in milliseconds
+# below evaluates to 50 ms
+CAM_SHUTTER_INTERVAL_MS: Final[float] = to_metric_time(ts=1 / CAM_FPS, src=Second, dst=Millisecond)
+
+# below evaluates to 100 ms
+LIDAR_SWEEP_INTERVAL_MS: Final[float] = to_metric_time(ts=1 / LIDAR_FRAME_RATE_HZ, src=Second, dst=Millisecond)
+
+ALLOWED_TIMESTAMP_BUFFER_MS: Final[int] = 2  # allow 2 ms of buffer
+LIDAR_SWEEP_INTERVAL_W_BUFFER_MS: Final[float] = LIDAR_SWEEP_INTERVAL_MS + ALLOWED_TIMESTAMP_BUFFER_MS
+LIDAR_SWEEP_INTERVAL_W_BUFFER_NS: Final[float] = to_metric_time(
+    ts=LIDAR_SWEEP_INTERVAL_W_BUFFER_MS, src=Millisecond, dst=Nanosecond
+)
+
+
+@dataclass
+class SynchronizedSensorData:
+    """Represents information associated with a single sweep.
+
+    Enables motion compensation between the sweep and associated images.
+
+    Args:
+        sweep: lidar sweep.
+        timestamp_city_SE3_ego_dict: mapping from vehicle timestamp to the egovehicle's pose in the city frame.
+        log_id: unique identifier for the AV2 vehicle log.
+        sweep_number: index of the sweep in [0, N-1], of all N sweeps in the log.
+        num_sweeps_in_log: number of sweeps in the log.
+        annotations: cuboids that have been annotated within the sweep, or None.
+        synchronized_imagery: mapping from camera name to timestamped imagery, or None.
+    """
+
+    sweep: Sweep
+    timestamp_city_SE3_ego_dict: TimestampedCitySE3EgoPoses
+    log_id: str
+    sweep_number: int
+    num_sweeps_in_log: int
+
+    annotations: Optional[CuboidList] = None
+    synchronized_imagery: Optional[Dict[str, TimestampedImage]] = None
+
+
+@dataclass
+class SensorDataloader:
+    """
+    Sensor dataloader for the Argoverse 2 sensor dataset.
+
+    NOTE: We build a cache of sensor records and synchronization information to reduce I/O overhead.
+
+    Args:
+        sensor_dataset_dir: Path to the sensor dataset directory.
+        with_annotations: Flag to return annotations in the __getitem__ method.
+        with_cams: Flag to load and return synchronized imagery in the __getitem__ method.
+        with_cache: Flag to enable file directory caching.
+        matching_criterion: either "nearest" or "forward".
+
+    Returns:
+        AV2 Sensor dataset.
+    """
+
+    sensor_dataset_dir: Path
+    with_annotations: bool = True
+    with_cache: bool = True
+    cam_names: Tuple[Union[RingCameras, StereoCameras], ...] = tuple(RingCameras) + tuple(StereoCameras)
+    matching_criterion = "nearest"
+
+    sensor_records: pd.DataFrame = field(init=False)
+
+    # Initialize synchronized metadata variable.
+    # This is only populated when self.use_imagery is set.
+    sync_records: Optional[pd.DataFrame] = None
+
+    def __post_init__(self) -> None:
+        """Index the dataset for fast sensor data lookup.
+
+        Synchronization database and sensor records are separate tables. Sensor records are an enumeration of
+        the records. The synchronization database is a hierarchichal index (Pandas MultiIndex) that functions
+        as a lookup table with correspondences between nearest images.
+        Given reference LiDAR timestamp -> obtain 7 closest ring camera + 2 stereo camera timestamps.
+        First level: Log id (1000 uuids)
+        Second level: Sensor name (lidar, ring_front_center, ring_front_left, ..., stereo_front_right).
+        Third level: Nanosecond timestamp (64-bit integer corresponding to vehicle time when the data was collected).
+        SENSOR RECORDS:
+            log_id                               sensor_name         timestamp_ns
+            0c6e62d7-bdfa-3061-8d3d-03b13aa21f68 lidar               315971436059707000
+                                                 lidar               315971436159903000
+                                                 lidar               315971436260099000
+                                                 lidar               315971436359632000
+                                                 lidar               315971436459828000
+            ...                                                                           ...
+            ff0dbfc5-8a7b-3a6e-8936-e5e812e45408 stereo_front_right  315972918949927214
+                                                 stereo_front_right  315972918999927217
+                                                 stereo_front_right  315972919049927212
+                                                 stereo_front_right  315972919099927219
+                                                 stereo_front_right  315972919149927218
+        SYNCHRONIZATION RECORDS:
+                                                                             ring_front_center       stereo_front_right
+        log_id                               sensor_name timestamp_ns
+        0c6e62d7-bdfa-3061-8d3d-03b13aa21f68 lidar       315971436059707000  315971436049927217  ...  315971436049927215
+                                                         315971436159903000  315971436149927219  ...  315971436149927217
+                                                         315971436260099000  315971436249927221  ...  315971436249927219
+                                                         315971436359632000  315971436349927219  ...  315971436349927221
+                                                         315971436459828000  315971436449927218  ...  315971436449927207
+                                                         ...                 ...                 ...                 ...
+        ff0dbfc5-8a7b-3a6e-8936-e5e812e45408 lidar       315972918660124000  315972918649927220  ...  315972918649927214
+                                                         315972918759657000  315972918749927214  ...  315972918749927212
+                                                         315972918859853000  315972918849927218  ...  315972918849927213
+                                                         315972918960050000  315972918949927220  ...  315972918949927214
+                                                         315972919060249000  315972919049927214  ...  315972919049927212
+        """
+        # Load log_id, sensor_type, and timestamp_ns information.
+        self.sensor_records = self._load_sensor_records()
+
+        # Populate synchronization database.
+        if self.cam_names:
+            sync_records_path = self.sensor_dataset_dir / "._sync_records"
+
+            # If caching is enabled AND the path exists, then load from the cache file.
+            if self.with_cache and sync_records_path.exists():
+                self.sync_records = read_feather(sync_records_path)
+            else:
+                self.sync_records = self._build_sync_records()
+
+            # If caching is enabled and we haven't created the cache, then save to disk.
+            if self.with_cache and not sync_records_path.exists():
+                self.sync_records.to_feather(str(sync_records_path))
+
+            # Finally, create a MultiIndex set the sync records index and sort it.
+            self.sync_records = self.sync_records.set_index(keys=["log_id", "sensor_name", "timestamp_ns"]).sort_index()
+
+    @cached_property
+    def num_logs(self) -> int:
+        """Return the number of unique logs."""
+        return len(self.sensor_records.index.unique("log_id"))
+
+    @cached_property
+    def num_sweeps(self) -> int:
+        """Return the number of unique lidar sweeps."""
+        return int(self.sensor_counts["lidar"])
+
+    @cached_property
+    def sensor_counts(self) -> pd.Series:
+        """Return the number of records for each sensor."""
+        sensor_counts: pd.Series = self.sensor_records.index.get_level_values("sensor_name").value_counts()
+        return sensor_counts
+
+    @property
+    def num_sensors(self) -> int:
+        """Return the number of sensors present throughout the dataset."""
+        return len(self.sensor_counts)
+
+    def _load_sensor_records(self) -> pd.DataFrame:
+        """Load the sensor records from the root directory.
+
+        We glob the filesystem for all LiDAR and camera filepaths, and then convert each file path
+        to a "sensor record".
+
+        A sensor record is a 3-tuple consisting of the following:
+            log_id: uuid corresponding to ~15 seconds of sensor data.
+            sensor_name: the name of the sensor (e.g., 'lidar').
+            timestamp_ns: vehicle nanosecond timestamp at which the sensor data was recorded.
+
+        Returns:
+            Sensor record index.
+        """
+        logger.info("Building metadata ...")
+
+        # Create the cache file path.
+        sensor_records_path = self.sensor_dataset_dir / "._sensor_records"
+
+        if sensor_records_path.exists():
+            logger.info("Cache found. Loading from disk ...")
+            sensor_records = read_feather(sensor_records_path)
+        else:
+            lidar_records = self.populate_lidar_records()
+            # Load camera records if enabled.
+            if self.cam_names:
+                logger.info("Loading camera data ...")
+                cam_records = self.populate_image_records()
+                # Concatenate lidar and camera records.
+                sensor_records = pd.concat([lidar_records, cam_records])
+            else:
+                sensor_records = lidar_records
+
+            # Save the metadata if caching is enable.
+            if self.with_cache:
+                sensor_records.reset_index(drop=True).to_feather(str(sensor_records_path))
+
+        # Set index as tuples of the form: (log_id, sensor_name, timestamp_ns) and sort the index.
+        # sorts by log_id, and then by sensor name, and then by timestamp.
+        sensor_records = sensor_records.set_index(["log_id", "sensor_name", "timestamp_ns"]).sort_index()
+
+        # Return all of the sensor records.
+        return sensor_records
+
+    def populate_lidar_records(self) -> pd.DataFrame:
+        """Obtain (log_id, sensor_name, timestamp_ns) 3-tuples for all LiDAR sweeps in the dataset.
+
+        Returns:
+            DataFrame of shape (N,3) with `log_id`, `sensor_name`, and `timestamp_ns` columns.
+                N is the number of sweeps for all logs in the dataset, and the `sensor_name` column
+                should be populated with `lidar` in every entry.
+        """
+        lidar_paths = sorted(self.sensor_dataset_dir.glob(LIDAR_PATTERN), key=lambda x: int(x.stem))
+        lidar_record_list = [
+            convert_path_to_named_record(x) for x in track(lidar_paths, description="Loading lidar records ...")
+        ]
+
+        # Concatenate into single dataframe (list-of-dicts to DataFrame).
+        lidar_records = pd.DataFrame(lidar_record_list)
+        return lidar_records
+
+    def populate_image_records(self) -> pd.DataFrame:
+        """Obtain (log_id, sensor_name, timestamp_ns) 3-tuples for all images in the dataset.
+
+        Returns:
+            DataFrame of shape (N,3) with `log_id`, `sensor_name`, and `timestamp_ns` columns.
+                N is the total number of images for all logs in the dataset, and the `sensor_name` column
+                should be populated with the name of the camera that captured the corresponding image in
+                every entry.
+        """
+        # Get sorted list of camera paths.
+        cam_paths = sorted(self.sensor_dataset_dir.glob(CAMERA_PATTERN), key=lambda x: int(x.stem))
+
+        # Load entire set of camera records.
+        cam_record_list = [
+            convert_path_to_named_record(x) for x in track(cam_paths, description="Loading camera records ...")
+        ]
+
+        # Concatenate into single dataframe (list-of-dicts to DataFrame).
+        cam_records = pd.DataFrame(cam_record_list)
+        return cam_records
+
+    def __len__(self) -> int:
+        """Return the number of lidar sweeps in the dataset.
+
+        The lidar sensor operates at 10 Hz. There are roughly 15 seconds of sensor data per log.
+
+        Returns:
+            Number of lidar sweeps.
+        """
+        return self.num_sweeps
+
+    def __iter__(self) -> SensorDataloader:
+        """Initialize pointer to the current iterate."""
+        self._ptr = 0
+        return self
+
+    def __next__(self) -> SynchronizedSensorData:
+        """Return the next datum in the dataset."""
+        result = self.__getitem__(self._ptr)
+        self._ptr += 1
+        return result
+
+    def __getitem__(self, idx: int) -> SynchronizedSensorData:
+        """Load the lidar point cloud and optionally the camera imagery and annotations.
+
+        Grab the lidar sensor data and optionally the camera sensor data and annotations at the lidar record
+            corresponding to the specified index.
+
+        Args:
+            idx: Index in [0, self.num_sweeps - 1].
+
+        Returns:
+            Mapping from sensor name to data for the lidar record corresponding to the specified index.
+        """
+        # Grab the lidar record at the specified index.
+        # Selects data at a particular level of a MultiIndex.
+        record: Tuple[str, int] = self.sensor_records.xs(key="lidar", level=1).iloc[idx].name
+
+        # Grab the identifying record fields.
+        log_id, timestamp_ns = record
+        log_lidar_records = self.sensor_records.xs((log_id, "lidar")).index
+        num_frames = len(log_lidar_records)
+
+        idx = np.where(log_lidar_records == timestamp_ns)[0].item()
+
+        sensor_dir = self.sensor_dataset_dir / log_id / "sensors"
+        lidar_feather_path = sensor_dir / "lidar" / f"{str(timestamp_ns)}.feather"
+        sweep = Sweep.from_feather(lidar_feather_path=lidar_feather_path)
+
+        log_dir = self.sensor_dataset_dir / log_id
+        timestamp_city_SE3_ego_dict = read_city_SE3_ego(log_dir=log_dir)
+
+        # Construct output datum.
+        datum = SynchronizedSensorData(
+            sweep=sweep,
+            log_id=log_id,
+            timestamp_city_SE3_ego_dict=timestamp_city_SE3_ego_dict,
+            sweep_number=idx,
+            num_sweeps_in_log=num_frames,
+        )
+
+        # Load annotations if enabled.
+        if self.with_annotations:
+            datum.annotations = self._load_annotations(log_id, timestamp_ns)
+
+        # Load camera imagery if enabled.
+        if self.cam_names:
+            datum.synchronized_imagery = self._load_synchronized_cams(sensor_dir, log_id, timestamp_ns)
+
+        # Return datum at the specified index.
+        return datum
+
+    def _build_sync_records(self) -> pd.DataFrame:
+        """Build the synchronization records for lidar-camera synchronization.
+
+        This function builds a set of records to efficiently associate auxiliary sensors
+        to a target sensor. We use this function to associate the nanosecond vehicle
+        timestamps of the lidar sweep to the nearest images from all 9 cameras (7 ring + 2 stereo).
+        Once this dataframe is built, synchronized data can be queried in O(1) time.
+
+        NOTE: This function is NOT intended to be used outside of SensorDataset initialization.
+
+        Returns:
+            (self.num_sweeps, self.num_sensors) DataFrame where each row corresponds to the nanosecond camera
+                timestamp that is closest (in absolute value) to the corresponding nanonsecond lidar sweep timestamp.
+        """
+        logger.info("Building synchronization database ...")
+
+        # Get unique log ids from the entire set of sensor data records.
+        log_ids: List[str] = self.sensor_records.index.unique(level="log_id").to_list()
+
+        # Create list to store synchronized data frames.
+        sync_list: List[pd.DataFrame] = []
+
+        # Iterate over all log ids.
+        for log_id in track(log_ids, description="Building sync records ..."):
+
+            # Select records associated with the current log id.
+            log_sensor_records = self.sensor_records.xs(key=log_id, level=0, drop_level=False)
+
+            # Get unique sensor names for a particular log.
+            # If the entire dataset is available, each log should have 7 ring cameras
+            # and 2 stereo cameras. The uniqueness check is required in case a subset of
+            # the data is being used by the end-user.
+            sensor_names: List[str] = log_sensor_records.index.unique(level="sensor_name").tolist()
+
+            # Remove lidar since we're using it as the reference sensor.
+            sensor_names.remove("lidar")
+
+            # Get lidar records for the selected log.
+            target_records = log_sensor_records.xs(key="lidar", level=1, drop_level=False).reset_index()
+            for sensor_name in sensor_names:
+                # Obtain tuples, convert tuples back to DataFrame, then rename `timestamp_ns' col, to sensor name,
+                # and finally remove the `log_id` column, to leave only a single column of timestamps.
+                src_records: pd.DataFrame = (
+                    log_sensor_records.xs(sensor_name, level=1)
+                    .reset_index()
+                    .rename({"timestamp_ns": sensor_name}, axis=1)
+                    .drop(["log_id"], axis=1)
+                )
+
+                # Match on the closest nanosecond timestamp.
+                # we do not pad the values, as NaN entries are meaningful.
+                target_records = pd.merge_asof(
+                    target_records,
+                    src_records,
+                    left_on="timestamp_ns",
+                    right_on=sensor_name,
+                    direction=self.matching_criterion,
+                    tolerance=int(LIDAR_SWEEP_INTERVAL_W_BUFFER_NS),
+                )
+
+            sync_list.append(target_records)
+        return pd.concat(sync_list).reset_index(drop=True)
+
+    def get_closest_img_fpath(self, log_id: str, cam_name: str, lidar_timestamp_ns: int) -> Optional[Path]:
+        """Find the filepath to the image from a particular a camera, w/ closest timestamp to a lidar sweep timestamp.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            cam_name: name of camera.
+            lidar_timestamp_ns: integer timestamp of LiDAR sweep capture, in nanoseconds
+
+        Returns:
+            img_fpath, string representing path to image, or else None.
+
+        Raises:
+            RuntimeError: if the synchronization database (sync_records) has not been created.
+        """
+        if self.sync_records is None:
+            raise RuntimeError("Requested synchronized data, but the synchronization database has not been created.")
+
+        if lidar_timestamp_ns not in self.sync_records.loc[(log_id, "lidar")].index:
+            # this timestamp does not correspond to any LiDAR sweep.
+            return None
+
+        # Create synchronization key.
+        key = (log_id, "lidar", lidar_timestamp_ns)
+
+        # Grab the synchronization record.
+        timestamp_ns = self.sync_records.loc[key, cam_name]
+
+        if pd.isna(timestamp_ns):
+            # no match was found within tolerance.
+            return None
+
+        sensor_dir = self.sensor_dataset_dir / log_id
+        img_path = sensor_dir / "cameras" / str(cam_name) / f"{int(timestamp_ns)}.jpg"
+        return img_path
+
+    def get_closest_lidar_fpath(self, log_id: str, cam_name: str, cam_timestamp_ns: int) -> Optional[Path]:
+        """Get file path for lidar sweep accumulated to a timestamp closest to a camera timestamp.
+
+        Args:
+            log_id: unique ID of vehicle log.
+            cam_name: name of camera.
+            cam_timestamp_ns: integer timestamp of image capture, in nanoseconds
+
+        Returns:
+            lidar_fpath: path representing path to .feather file, or else None.
+
+        Raises:
+            RuntimeError: if the synchronization database (sync_records) has not been created.
+        """
+        if self.sync_records is None:
+            raise RuntimeError("Requested synchronized data, but the synchronization database has not been created.")
+
+        idx = np.argwhere(self.sync_records.xs(log_id)[cam_name].values == cam_timestamp_ns)
+        if len(idx) == 0:
+            # There is no image within the requested interval (50 ms).
+            return None
+
+        lidar_timestamp_ns = self.sync_records.xs(log_id).loc["lidar"].index[int(idx)]
+        return self.sensor_dataset_dir / log_id / "sensors" / "lidar" / f"{lidar_timestamp_ns}.feather"
+
+    def _load_annotations(self, log_id: str, sweep_timestamp_ns: int) -> CuboidList:
+        """Load the sweep annotations at the provided timestamp.
+
+        Args:
+            log_id: Log unique id.
+            sweep_timestamp_ns: Nanosecond timestamp.
+
+        Returns:
+            Cuboid list of annotations.
+        """
+        annotations_feather_path = self.sensor_dataset_dir / log_id / "annotations.feather"
+
+        # Load annotations from disk.
+        # NOTE: This contains annotations for the ENTIRE sequence.
+        # The sweep annotations are selected below.
+        cuboid_list = CuboidList.from_feather(annotations_feather_path)
+        cuboids = list(filter(lambda x: x.timestamp_ns == sweep_timestamp_ns, cuboid_list.cuboids))
+        return CuboidList(cuboids=cuboids)
+
+    def _load_synchronized_cams(
+        self, sensor_dir: Path, log_id: str, sweep_timestamp_ns: int
+    ) -> Optional[Dict[str, TimestampedImage]]:
+        """Load the synchronized imagery for a lidar sweep.
+
+        Args:
+            sensor_dir: Sensor directory.
+            log_id: Log unique id.
+            sweep_timestamp_ns: Nanosecond timestamp.
+
+        Returns:
+            Mapping between camera names and synchronized images.
+
+        Raises:
+            RuntimeError: if the synchronization database (sync_records) has not been created.
+        """
+        if self.sync_records is None:
+            raise RuntimeError("Requested synchronized data, but the synchronization database has not been created.")
+
+        cam_paths = [
+            self.get_closest_img_fpath(log_id=log_id, cam_name=cam_name, lidar_timestamp_ns=sweep_timestamp_ns)
+            for cam_name in self.cam_names
+        ]
+
+        log_dir = sensor_dir.parent
+
+        cams: Dict[str, TimestampedImage] = {}
+        for p in cam_paths:
+            if p is not None:
+                cams[p.parent.stem] = TimestampedImage(
+                    img=read_img(p, channel_order="BGR"),
+                    camera_model=PinholeCamera.from_feather(log_dir=log_dir, cam_name=p.parent.stem),
+                    timestamp_ns=int(p.stem),
+                )
+        return cams
diff --git a/src/av2/datasets/sensor/splits.py b/src/av2/datasets/sensor/splits.py
new file mode 100644
index 00000000..50732c89
--- /dev/null
+++ b/src/av2/datasets/sensor/splits.py
@@ -0,0 +1,1012 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Splits for the Argoverse 2 sensor dataset."""
+
+from typing import Final, Tuple
+
+TRAIN: Final[Tuple[str, ...]] = (
+    "00a6ffc1-6ce9-3bc3-a060-6006e9893a1a",
+    "01bb304d-7bd8-35f8-bbef-7086b688e35e",
+    "022af476-9937-3e70-be52-f65420d52703",
+    "0322b098-7e42-34db-bcec-9a4d072191e9",
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+    "c6940de1-dccf-3b42-9c39-bbb9feb2d638",
+    "c69e348a-8e10-31dc-b71b-dd8e5cfd7211",
+    "c6c55112-0078-3867-a63f-1861a0125b8d",
+    "c70b2c64-7a9a-37c5-b974-709fa0536675",
+    "c71cd96c-8e3f-3861-9ece-fcbabebc63a8",
+    "c730e199-fb8d-3abf-b7aa-bbc81bf8c08f",
+    "c780d53a-2d37-3cd8-9e89-530966aef53e",
+    "c7f5e5c1-dc52-3619-8998-420b2e280d8a",
+    "c858bd6a-81ab-3f54-b46d-ffc091ef6945",
+    "c85ebc24-0934-3423-9c14-f0fdbee64b68",
+    "c8cdffb0-7942-3ff5-9f71-210e095e1d31",
+    "c93a30c8-168c-386c-a25a-cbd8d8410fbe",
+    "c94991c0-3662-3936-972c-1af63db486d8",
+    "c96a09c8-46ed-391f-8a66-c46fa8b76029",
+    "c990cafc-f96c-3107-b213-01d217b11272",
+    "ca4144fb-10e5-3895-836f-87001f59ac65",
+    "caabc342-aed4-3104-8195-7461a4add481",
+    "ccb4e29d-e88f-3fbe-8958-67cfd62350a3",
+    "cd2353c2-0fb6-3e18-8281-4c0df1d3189a",
+    "cd822baf-4aa1-33fa-bfe5-d91386598edb",
+    "cd83b7cd-e2e7-34f6-bee7-1ff5ca3ed665",
+    "cdd752d0-caee-3d95-b1db-7fc20cbbc783",
+    "ce0575bf-c2fc-38bd-9947-ea7494a799f9",
+    "ce0e814a-d9df-3975-a521-d8ae9a091e96",
+    "ce34ff64-0faa-3fae-a79e-985f7a5172c9",
+    "ce5033ee-e74e-354a-9299-8aaefbd03f59",
+    "cea5f5c2-e786-30f5-8305-baead8923063",
+    "cf6a99cb-b8bc-34d7-bdca-30e50e66cd74",
+    "d0828f48-3e67-3136-9c70-1f99968c8280",
+    "d0ba7a1b-f5ca-39d6-98d0-29c671baec65",
+    "d0cc23f4-f4ab-3755-8e63-2e3940f68d89",
+    "d1695c5e-08a9-44fd-8f45-93c23f700c8b",
+    "d201af7e-48c8-34ad-be1c-e649af2cb5c2",
+    "d20c3612-a64d-3aa8-bd4a-58890413afbb",
+    "d26b95e4-d200-34e2-92c9-c16fda4cd9dd",
+    "d2901fe5-4b64-3144-98e1-67ef5ef83fa7",
+    "d2c6775c-e0a0-3460-b1b8-8d0ce2e0ae4e",
+    "d33f667d-7b6c-39aa-9ba9-eac2fa615ae1",
+    "d37be0e2-8223-3eeb-a0e2-c4b75d5ff87b",
+    "d3dc783e-663a-31b1-bd85-46e04ca693db",
+    "d3efe9ba-f10a-35e7-b17e-6850c66693fe",
+    "d47507ce-b079-395e-8761-8e70818ea69a",
+    "d551b8f9-feab-3946-9524-219e07988341",
+    "d58d55ea-f30c-3622-8303-1574616b9865",
+    "d5d40b4c-48d9-3b68-903a-025eb0fa334d",
+    "d6ba4898-1369-3521-981c-b9ac57420418",
+    "d72c43a5-95bf-3a3e-9019-cf25cc0a61c0",
+    "d78b78a0-2322-32c2-833a-e42ddc132d30",
+    "d842ce41-8d9c-3c0f-9c04-595d97be5140",
+    "d8bd6071-f3ae-31fd-866a-9e6e4d6519e2",
+    "d9530d0a-b83e-44a3-910a-2b5bb8f1fb80",
+    "d97ae2c0-b8d1-341c-94b7-f19d5fd2982a",
+    "d9c267be-f19a-3183-afe0-f0625a375743",
+    "d9fd666a-8f55-38bb-8387-80fa44c29348",
+    "da30abcf-652b-38df-a128-10942b225ec5",
+    "dad3e145-2f70-357f-813e-703936927fe2",
+    "db17141d-4d35-381d-9949-36ce767d6641",
+    "db447b86-8103-3ee4-93de-2c838ba061dc",
+    "dbe19bf6-93ad-372e-b96d-f7b652cdba93",
+    "dc3d4b79-6cd8-324b-bc70-cbd0e2a066da",
+    "dc4d148d-f84c-307c-b2b7-f0cd7c267f57",
+    "dc9c2d63-083f-32c3-90ff-943ca823a245",
+    "dd251cc5-736d-3b76-8ad3-3f6cb138178e",
+    "de43d0b6-d6a7-38de-9709-883e10320267",
+    "de586ff4-3413-367d-befc-ad022b73592b",
+    "debbba6b-8cb8-3ab6-adfe-54fcc6b02839",
+    "df1935dc-1e5f-3f4d-bdcb-e6c2bcb07667",
+    "df321672-461c-361b-aac9-e81cc9a88b9f",
+    "df5d0b0e-5bcb-304a-a167-18b92d0f1d45",
+    "df738339-958b-31fb-8e48-a4380f4c538a",
+    "e033cc8e-b23d-3fc6-8954-d90c5e98550e",
+    "e0ba7664-d287-39df-8193-00d60cae1417",
+    "e0cfd042-ae29-3d21-bb47-81eb8f933ec8",
+    "e0d2fe70-8f98-3ce2-8d8f-4268a81f7169",
+    "e10475f7-0d56-3a75-870d-d4206fa165d7",
+    "e123ba3f-99bd-3039-b6e7-8c62eaebf9c2",
+    "e125bb91-dcaf-3013-9cc7-da653d7e11e1",
+    "e1450d07-faed-3d97-b674-c6f8d2498d80",
+    "e1e9d341-716f-3613-9ec2-2201c72361af",
+    "e1f37027-6a39-3eb1-b38a-3f2836b84735",
+    "e2043284-6122-3cc9-a7e7-f091a16361b7",
+    "e28c16d0-084a-3dc4-aad4-9d157ca528de",
+    "e2de16b1-0ae9-39ad-82fd-edcabb26feca",
+    "e331aa95-3660-3c71-be9e-030bab0b8ee2",
+    "e38c1e1d-f0e9-3d73-8193-29cbea481b4c",
+    "e3a935bf-1428-32a4-8d53-caa71ee3de8f",
+    "e40d67c5-3749-397e-aa2a-7dfe576a31b0",
+    "e424d4f7-4b28-322f-b630-31d42ae528eb",
+    "e4279e3e-b7e1-3f43-aeef-2bfa2836dab6",
+    "e453f164-dd36-3f1a-9471-05c2627cbaa5",
+    "e4d53680-f7ef-364b-91a4-00e5aa91ab9b",
+    "e4f6dbab-f2eb-3bd2-9dbc-88640e3b4a5f",
+    "e5178032-d260-3bc8-968e-a5cb98b6ae5a",
+    "e574050e-f787-3186-9686-2e9aca8102a0",
+    "e65e405c-8aea-30f5-a926-1e0fbbeefb9f",
+    "e66d1403-755b-3f63-938b-a2a69446a48a",
+    "e68d1f0d-eb44-3751-975d-f80609f695ae",
+    "e743b441-ea8a-36d7-8124-f14dfa13a0e6",
+    "e7547e4c-1ebc-3428-8964-a5b91e81098e",
+    "e757cddd-5ff5-305a-af11-d7c6747d3979",
+    "e7e178aa-931a-4674-9bff-9278a54e6aae",
+    "e7e7ebad-79d2-3ae1-95ce-f3035bc8f719",
+    "e88132d0-4512-3d6f-a1c8-f60972332af5",
+    "e94f58d9-177b-31be-aa05-e6dd10d04124",
+    "ea6895f2-504b-37b5-bfd0-cbf7017f22c3",
+    "eaaf5ad3-2cec-3369-b3d1-97c737ecaf19",
+    "eb142141-683a-3a6d-a207-0302b1ff260d",
+    "eb222d5d-0052-3ce7-9b87-19e09054a2c0",
+    "eb69a196-fb43-3ddf-9bbe-9d55fa1e8200",
+    "eb777faa-5b76-387e-a408-90524c6f2848",
+    "ebece6dc-ec92-326f-97ed-f66b2970e358",
+    "ec02cf7e-36d3-3e9d-8835-3b6c27975bea",
+    "ec3133b4-2b17-3d49-b78a-4bc480ac5137",
+    "ed6ad297-ee09-3532-bcfc-c16ad5a05c49",
+    "ed93e1bb-7bbc-3444-8fc8-08a271438fc8",
+    "ede387f4-f390-3f0e-a071-eb543b73ed73",
+    "edf3a727-664e-38be-b990-65d34012d926",
+    "ee27a871-85cf-494c-8519-f54815040af5",
+    "eec8ae97-8de4-354a-b11a-d3a14b276479",
+    "eed8593d-60e3-3e41-9fea-55f544b01749",
+    "ef4a46c4-138e-3478-b94e-3e60a567ec7d",
+    "ef625e46-d0d4-38b9-9403-5614e7b39ec8",
+    "f03bfd11-5ba2-3bc6-ad76-4166b06491f5",
+    "f110598d-7e01-3ed7-a227-4e958987a31f",
+    "f150d98f-0109-3380-8480-c6846fb8e9c8",
+    "f2b0585b-ada3-3123-963e-14df7d96ca9e",
+    "f303e577-a888-30db-97f7-ed36c90227e1",
+    "f3cc42c7-84a8-35c5-8683-13878bb9beeb",
+    "f3d1e3c3-2770-3504-a592-b62619598812",
+    "f3f8f680-e471-3662-a06a-0c00e6d88f43",
+    "f41d0e8f-856e-3f7d-a3f9-ff5ba7c8e06d",
+    "f46707f9-435f-3a06-9017-deae11feab53",
+    "f4c6ade0-7b9e-4ad7-8d86-13d2f4c91499",
+    "f4cb6ba4-cd0f-30cc-9cc9-52bd14bfb3cc",
+    "f4d1a3c3-5002-336b-a67f-775b3725237e",
+    "f54c1d50-48a3-4651-bfb0-50b87f13dc9e",
+    "f5a3ee79-a131-3f8a-91e9-a6475d778149",
+    "f61bcee1-2964-3c4b-95a5-697df5f42f47",
+    "f6350a4f-eee8-31bd-8520-28f9c81c98a8",
+    "f648b945-6c70-3105-bd23-9502894e37d4",
+    "f64ed43e-417a-31ad-a322-b6108bf99a71",
+    "f7cdc2d1-f59a-30a2-aae8-8bb81c769e6e",
+    "f7cf93d8-f7bd-3799-8500-fbe842a96f63",
+    "f8412dbd-48b9-39f3-b534-08950f6e633b",
+    "f84b4941-8e99-3957-b6f6-db1590338cf6",
+    "f8825b65-5631-3417-8309-bd5677d694aa",
+    "f9f6a7e9-4f79-3fdf-b1a7-ba300622f116",
+    "fa708289-f2b2-399e-989e-53f83fa379c5",
+    "fa9ec72a-cbcf-35dc-be20-4d0d9e7215ef",
+    "fac8a63c-6b75-39d0-9f57-4344fde0f794",
+    "fb0c5970-dbf7-4c77-b3b6-2c8cc41b9d6f",
+    "fb149ce0-7706-3bb5-9860-8c63d1c77592",
+    "fb2cce69-655d-3203-990a-74301895408d",
+    "fb720691-1736-3fa2-940b-07b97603efc6",
+    "fd4e2c4c-f7e9-3110-8e32-28d3add3937d",
+    "fd5c6932-2ee2-3cfb-9bdc-0b30bfb33a91",
+    "fdb0578d-4fa7-37a7-b60d-5472b4d39136",
+    "fdc0f552-4976-36a6-8691-9a8c6a5ba389",
+    "ff52c01e-3d7b-32b1-b6a1-bcff3459ccdd",
+    "ff6adc87-5f47-32f7-b36a-546453c0e332",
+    "ff8e7fdb-1073-3592-ba5e-8111bc3ce48b",
+)
+VAL: Final[Tuple[str, ...]] = (
+    "02678d04-cc9f-3148-9f95-1ba66347dff9",
+    "02a00399-3857-444e-8db3-a8f58489c394",
+    "04994d08-156c-3018-9717-ba0e29be8153",
+    "05fa5048-f355-3274-b565-c0ddc547b315",
+    "070bbf42-31d3-3aa9-aca4-c262afc9077d",
+    "0aa4e8f5-2f9a-39a1-8f80-c2fdde4405a2",
+    "0b5142c1-420b-3fea-9e98-b87327ae22c6",
+    "0b86f508-5df9-4a46-bc59-5b9536dbde9f",
+    "0bae3b5e-417d-3b03-abaa-806b433233b8",
+    "0c3bad78-9f1e-395d-a376-2eb7499229fd",
+    "0fb7276f-ecb5-3e5b-87a8-cc74c709c715",
+    "11ba4e81-c26f-3cd1-827d-b6913bcef64e",
+    "15ec0778-826e-3ed7-9775-54fbf66997f4",
+    "182ba3f7-b89a-36cc-ae40-32a341b0d3e9",
+    "185d3943-dd15-397a-8b2e-69cd86628fb7",
+    "19350c96-623d-4d77-af96-f8c23f00c358",
+    "19f53e16-9f99-3035-9672-7e860f3b0048",
+    "1da4a0aa-22ae-3958-856d-05303de1f576",
+    "1f434d15-8745-3fba-9c3e-ccb026688397",
+    "201fe83b-7dd7-38f4-9d26-7b4a668638a9",
+    "20bcd747-ef60-391a-9f4a-ae99f049c260",
+    "20d47f81-46e8-3adf-a0ca-564fbb5c599d",
+    "20dd185d-b4eb-3024-a17a-b4e5d8b15b65",
+    "214e388e-cbd7-3dde-a204-d2ec42298808",
+    "22052525-4f85-3fe8-9d7d-000a9fffce36",
+    "24642607-2a51-384a-90a7-228067956d05",
+    "25e5c600-36fe-3245-9cc0-40ef91620c22",
+    "27be7d34-ecb4-377b-8477-ccfd7cf4d0bc",
+    "27c03d98-6ac3-38a3-ba5e-102b184d01ef",
+    "280269f9-6111-311d-b351-ce9f63f88c81",
+    "29a00842-ead2-3050-b587-c5ef507e4125",
+    "2c652f9e-8db8-3572-aa49-fae1344a875b",
+    "2e3f2ae7-9ab9-3aef-a3ce-a0a97a0cb1ab",
+    "2f2321d2-7912-3567-a789-25e46a145bda",
+    "2ff4f798-78d9-3384-87e9-61928aa4cb6d",
+    "335aabef-269e-3211-a99d-2c3a3a8f8475",
+    "36aec72e-5086-376c-b109-295b128e77e1",
+    "3b3570b4-7b0b-3268-a571-b0889dbf40b6",
+    "3bffdcff-c3a7-38b6-a0f2-64196d130958",
+    "3de5b5d6-68c4-3c95-84ed-be7c83d829f8",
+    "42f92807-0c5e-3397-bd45-9d5303b4db2a",
+    "44adf4c4-6064-362f-94d3-323ed42cfda9",
+    "47286726-5dd4-4e26-bd2d-5324f429e445",
+    "472a240a-10cd-39cd-8681-558f7c7cf868",
+    "4c33fc38-5e59-34f8-96ba-4e5a404d3988",
+    "4e3fedbb-847c-3d5b-8a62-c9ff84550985",
+    "51bbdd4d-3065-34ae-b369-b6e0444f34db",
+    "52071780-5758-3ed4-8835-0d64ecdc5575",
+    "52971a8a-ed62-3bfd-bcd4-ca3308b594e0",
+    "544a8102-0ef5-3044-921e-dc0544370376",
+    "5589de60-1727-3e3f-9423-33437fc5da4b",
+    "58e82365-03bc-3b2f-b55a-a4ad0e3e792d",
+    "58fed0d4-97d5-469b-89a4-4394838e10c7",
+    "5c0584a3-52a6-3029-b6ff-ca45a19d8aa6",
+    "5f278cdd-ca28-3c53-8f5c-04e62308811d",
+    "5f8f4a26-59b1-3f70-bcab-b5e3e615d3bc",
+    "65387aee-4490-38b9-8f4f-1fc43bd4ac06",
+    "6803104a-bb06-402e-8471-e5af492db0a8",
+    "6aaf5b08-9f84-3a2e-8a32-2e50e5e11a3c",
+    "6c932547-4c11-31d7-b8ef-0c16a13dbfc3",
+    "6f128f23-ee40-3ea9-8c50-c9cdb9d3e8b6",
+    "7039e410-b5ab-35aa-96bc-2c4b89d3c5e3",
+    "72cf3ca1-1a9e-3254-bca0-29c62521e454",
+    "7606de8d-486c-4916-9cbb-002ee966f834",
+    "76916359-96f4-3274-81fe-bb145d497c11",
+    "77574006-881f-3bc8-bbb6-81d79cf02d83",
+    "78683234-e6f1-3e4e-af52-6f839254e4c0",
+    "78da7b7e-8ddf-3c7d-8716-eaa890106dd3",
+    "78f7cb5c-9d51-34f0-b356-9b3d83263c75",
+    "7a2c222d-addc-30b2-aac6-596cb65a22e3",
+    "7dbc2eac-5871-3480-b322-246e03d954d2",
+    "7de2e535-81df-3d5f-a5ca-62e4b940eb54",
+    "7e4d67b3-c3cc-3288-afe5-043602ea3c70",
+    "7fab2350-7eaf-3b7e-a39d-6937a4c1bede",
+    "858d739b-a0ba-35aa-bafc-4f7988bcad17",
+    "8749f79f-a30b-3c3f-8a44-dbfa682bbef1",
+    "87ca3d9f-f317-3efb-b1cb-aaaf525227e5",
+    "88f47a10-87b4-3ea8-a0c7-a07d825b647d",
+    "91aab547-1912-3b8e-8e7f-df3b202147bf",
+    "92b900b1-ac4a-3d41-b118-e42c66382c91",
+    "95bf6003-7068-3a78-a0c0-9e470a06e60f",
+    "96dd6923-994c-3afe-9830-b15bdfd60f64",
+    "9a448a80-0e9a-3bf0-90f3-21750dfef55a",
+    "9bb1f857-8b61-369f-a537-484c1323ae32",
+    "9e9bcfb7-601d-3d80-bc12-ef7025174beb",
+    "9f871fb4-3b8e-34b3-9161-ed961e71a6da",
+    "a060c4c1-b9fc-39c1-9d30-d93a124c9066",
+    "a1589ae2-2678-310e-91cc-c4b512cd7fa5",
+    "a33a44fb-6008-3dc2-b7c5-2d27b70741e8",
+    "a7636fca-4d9e-3052-bef2-af0ce5d1df74",
+    "a91d4c7b-bf55-3a0e-9eba-1a43577bcca8",
+    "adf9a841-e0db-30ab-b5b3-bf0b61658e1e",
+    "b19f3c1a-a84a-3a2d-8d1b-8a4ae201020b",
+    "b2053fdc-0b94-30bc-aee7-5bc6fb7e9f52",
+    "b213af37-7d89-342d-ae39-8a3c72159a01",
+    "b50c4763-5d1e-37f4-a009-2244aeebabcd",
+    "b5a7ff7e-d74a-3be6-b95d-3fc0042215f6",
+    "b6500255-eba3-3f77-acfd-626c07aa8621",
+    "b6e967f6-92bc-3bf5-99c9-1b0c4649fd67",
+    "b8489c02-60d0-3f44-a3b4-9de62830d666",
+    "b87683ae-14c5-321f-8af3-623e7bafc3a7",
+    "ba67827f-6b99-3d2a-96ab-7c829eb999bb",
+    "bbd19ca1-805a-3c22-8df3-cd7501aa06f3",
+    "bd90cd1a-38b6-33b7-adec-ba7d4207a8c0",
+    "bdb9d309-f14b-3ff6-ad1f-5d3f3f95a13e",
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+)
+TEST: Final[Tuple[str, ...]] = (
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+    "ed5fc860-c172-39c5-91c0-d712957fb1cd",
+    "f2325996-961e-3f63-bbc0-44b7e76aeac9",
+    "f3887a09-2881-3de6-bdb9-55b62a0d15f5",
+    "f554d503-4901-3b97-9516-a16398c66631",
+    "f6107596-76e0-3064-a4a6-86332a90e539",
+    "f77889f6-ef5a-4eed-a4cd-5d67d4a6e9c5",
+    "f7c4cf87-6bab-3723-bd74-1c9ac5add9cb",
+    "f7d568d4-0836-3f47-b330-f8d204c4b96e",
+    "f849731b-d288-3bec-8f35-6bea979f7dd8",
+    "fb207d3b-d2d5-3100-94c0-9145aebc770b",
+    "fee0f78c-cf00-35c5-975b-72724f53fd64",
+)
diff --git a/src/av2/datasets/sensor/utils.py b/src/av2/datasets/sensor/utils.py
new file mode 100644
index 00000000..8316abbf
--- /dev/null
+++ b/src/av2/datasets/sensor/utils.py
@@ -0,0 +1,29 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utilities for the sensor dataset."""
+
+import logging
+from pathlib import Path
+from typing import Dict, Union
+
+logger = logging.Logger(__name__)
+
+
+def convert_path_to_named_record(path: Path) -> Dict[str, Union[str, int]]:
+    """Convert the unique record for any sensor or pose data (log_id, sensor_name, timestamp_ns).
+
+    Args:
+        path: Path to the data.
+
+    Returns:
+        Mapping of name to record field.
+    """
+    sensor_name = path.parent.stem
+
+    # log_id is 2 directories up for the lidar filepaths, but 3 levels up for images
+    # {log_id}/sensors/cameras/ring_*/*.jpg vs.
+    # {log_id}/sensors/lidar/*.feather
+    parent_idx = 2 if sensor_name == "lidar" else 3
+    log_id = path.parents[parent_idx].stem
+    sensor_name, timestamp_ns = path.parent.stem, int(path.stem)
+    return {"log_id": log_id, "sensor_name": sensor_name, "timestamp_ns": timestamp_ns}
diff --git a/src/av2/datasets/tbv/README.md b/src/av2/datasets/tbv/README.md
new file mode 100644
index 00000000..57ae10cc
--- /dev/null
+++ b/src/av2/datasets/tbv/README.md
@@ -0,0 +1,139 @@
+# Trust, but Verify (TbV) Dataset Overview
+
+## Dataset Size
+
+The Trust, but Verify (TbV) Dataset consists of 1045 vehicle logs. Each vehicle log, on average, is 54 seconds in duration, including 536 LiDAR sweeps on average, and 1073 images from each of the 7 cameras (7512 images per log). Some logs are as short as 4 seconds, and other logs are up to 117 seconds in duration.
+
+The total dataset amounts to 15.57 hours of driving data, amounting to 922 GB of data in its extracted form. There are 7.85 Million images in the dataset, and 563,745 LiDAR sweeps in total.
+
+## Downloading TbV
+
+Install `s5cmd` using [the installation instructions here](../../../../../DOWNLOAD.md), and then run download 21 tar.gz archives from Amazon S3 as follows:
+
+```bash
+SHARD_DIR={DESIRED PATH FOR TAR.GZ files}
+s5cmd cp s3://argoai-argoverse/av2/tars/tbv/*.tar.gz ${SHARD_DIR}
+```
+Next, extract TbV tar.gz files that were just downloaded to a local disk:
+```bash
+python tutorial/untar_tbv.py
+```
+
+## Baselines
+We provide both pre-trained models for HD map change detection and code for training such models at [https://github.com/johnwlambert/tbv](https://github.com/johnwlambert/tbv).
+
+## Sensor Suite
+
+The sensor suite is identical to the Argoverse 2 Sensor Dataset, except no stereo sensor data is provided, and the sensor imagery for 6 of the cameras is provided at half of the image resolution (`ring_front_center` is at an identical resolution, however).
+
+Lidar sweeps are collected at 10 Hz, along with 20 fps imagery from 7 ring cameras positioned to provide a fully panoramic field of view. In addition, camera intrinsics, extrinsics and 6-DOF ego-vehicle pose in a global coordinate system are provided. Lidar returns are captured by two 32-beam lidars, spinning at 10 Hz in the same direction, but separated in orientation by 180°. The cameras trigger in-sync with both lidars, leading to a 20 Hz frame-rate. The seven global shutter cameras are synchronized to the lidar to have their exposure centered on the lidar sweeping through their fields of view. 
+
+We aggregate all returns from the two stacked 32-beam sensors into a single sweep. These sensors each have different, overlapping fields-of-view. Both lidars have their own reference frame, and we refer to them as `up_lidar` and `down_lidar`, respectively. We have egomotion-compensated the LiDAR sensor data to the egovehicle reference nanosecond timestamp. **All LiDAR returns are provided in the egovehicle reference frame, not the individual LiDAR reference frame**.
+
+TbV imagery is provided at (height x width) of  `2048 x 1550` (portrait orientation) for the ring front-center camera, and at `775 x 1024` (landscape orientation) for all other 6 cameras. Please note that the ring front-center camera imagery is provided at higher resolution. **All camera imagery is provided in an undistorted format**.
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/158674314-f0c930a7-8f46-42e7-b849-3bbfd72b63db.png" height="400">
+</p>
+
+## Dataset Structure Format
+
+Tabular data (lidar sweeps, poses, calibration) are provided as [Apache Feather Files](https://arrow.apache.org/docs/python/feather.html) with the file extension `.feather`. We show examples below.
+
+Unlike the Argoverse 2 Sensor Dataset, TbV features no object annotations.
+
+## Maps
+
+A local vector map and a local ground height raster map is provided per log, please refer to the [Map README](../../map/README.md) for additional details. For example, for log `VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020`, the `map` subdirectory contains 3 files:
+
+- `log_map_archive_VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020____DTW_city_73942.json`: local vector map.
+- `VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020_ground_height_surface____DTW.npy`: local ground height raster map, at 30 cm resolution.
+- `VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020___img_Sim2_city.json`: mapping from city coordinates to raster grid/array coordinates.
+
+## Pose
+
+6-DOF ego-vehicle pose in a global (city) coordinate system is provided (visualized in the figure below as a red line, with red circles indicated at a 1 Hz frequency):
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/157916600-cd67a529-596e-4a75-bb99-d69bbfb9075b.jpg" height="800">
+</p>
+
+We refer to this pose as `city_SE3_egovehicle` throughout the codebase:
+
+```python
+>>> import av2.utils.io as io_utils
+>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/city_SE3_egovehicle.feather")
+            timestamp_ns        qw        qx        qy        qz         tx_m         ty_m       tz_m
+0     315969466027482498  0.245655  0.009583 -0.014121 -0.969207  9277.579933  6805.407468 -22.647127
+1     315969466042441191  0.245661  0.009824 -0.014529 -0.969197  9277.496340  6805.362364 -22.647355
+2     315969466057428264  0.245682  0.009999 -0.015003 -0.969183  9277.418457  6805.317208 -22.648150
+3     315969466060265000  0.245687  0.010025 -0.015133 -0.969179  9277.402699  6805.308645 -22.648235
+4     315969466077482496  0.245723  0.010218 -0.015682 -0.969159  9277.306645  6805.257303 -22.648716
+...                  ...       ...       ...       ...       ...          ...          ...        ...
+8811  315969525887425441  0.843540  0.008404 -0.005364 -0.536974  9371.218847  6465.181151 -23.095571
+8812  315969525892441193  0.843547  0.008349 -0.005421 -0.536963  9371.243129  6465.129394 -23.097279
+8813  315969525899927216  0.843569  0.008234 -0.005435 -0.536930  9371.278003  6465.054774 -23.097989
+8814  315969525907428274  0.843575  0.008092 -0.005358 -0.536924  9371.312815  6464.980204 -23.098440
+8815  315969525912451243  0.843601  0.008013 -0.005400 -0.536883  9371.333643  6464.934933 -23.095809
+
+[8816 rows x 8 columns]
+```
+
+## LiDAR Sweeps
+
+For example, we show below the format of an example sweep `sensors/lidar/315969468259945000.feather` (the sweep has a reference timestamp of 315969468259945000 nanoseconds). Unlike the sensor dataset, TbV sweeps **do not** contain timestamps per return (there is no `offset_ns` attribute):
+
+```python
+>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/sensors/lidar/315969468259945000.feather")
+               x          y          z  intensity  laser_number
+0     -13.023438  12.492188  -0.138794        103            25
+1     -10.992188  10.726562   1.831055         36             7
+2     -15.273438  14.460938   0.356445         35            23
+3     -10.828125  10.609375   1.076172         49            19
+4     -10.570312  10.421875   1.456055        104             3
+...          ...        ...        ...        ...           ...
+89261   4.136719  -2.882812   1.631836          0            19
+89262   4.054688  -2.783203   1.546875         23             3
+89263  60.312500 -77.937500  10.671875         47            25
+89264  17.984375 -21.390625   1.214844          6             7
+89265   4.160156  -2.953125   1.719727         36            23
+
+[89266 rows x 5 columns]
+```
+
+## Calibration
+
+An example calibration file is shown below, parameterizing `vehicle_SE3_sensor` for each sensor (the sensor's pose in the egovehicle coordinate system):
+
+```python
+>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/calibration/egovehicle_SE3_sensor.feather")
+         sensor_name        qw        qx        qy        qz      tx_m      ty_m      tz_m
+0  ring_front_center  0.501067 -0.499697  0.501032 -0.498200  1.626286 -0.020252  1.395709
+1    ring_front_left  0.635731 -0.671186  0.277021 -0.261946  1.549577  0.177582  1.388212
+2   ring_front_right  0.262148 -0.277680  0.670922 -0.635638  1.546437 -0.216452  1.394248
+3     ring_rear_left  0.602832 -0.602666 -0.368113  0.371322  1.099130  0.106534  1.389519
+4    ring_rear_right  0.371203 -0.367863 -0.601619  0.604103  1.101165 -0.141049  1.399768
+5     ring_side_left  0.686808 -0.722414 -0.058060  0.055145  1.308706  0.255756  1.379285
+6    ring_side_right  0.055626 -0.056105 -0.722917  0.686403  1.306407 -0.291250  1.394200
+7           up_lidar  0.999995  0.000000  0.000000 -0.003215  1.350110 -0.013707  1.640420
+8         down_lidar  0.000080 -0.994577  0.103998  0.000039  1.355172 -0.021696  1.507259
+```
+
+## Intrinsics
+
+An example camera intrinsics file is shown below:
+
+```python
+>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/calibration/intrinsics.feather")
+         sensor_name        fx_px        fy_px       cx_px        cy_px        k1        k2        k3  height_px  width_px
+0  ring_front_center  1686.020228  1686.020228  775.467979  1020.785939 -0.245028 -0.196287  0.301861       2048      1550
+1    ring_front_left   842.323546   842.323546  513.397368   387.828521 -0.262302 -0.108561  0.179488        775      1024
+2   ring_front_right   842.813516   842.813516  514.154170   387.181497 -0.257722 -0.125524  0.199077        775      1024
+3     ring_rear_left   841.669682   841.669682  513.211190   387.324359 -0.257018 -0.130649  0.204405        775      1024
+4    ring_rear_right   843.832813   843.832813  512.201788   387.673600 -0.256830 -0.132244  0.208272        775      1024
+5     ring_side_left   842.178507   842.178507  512.314602   388.188297 -0.256152 -0.131642  0.205564        775      1024
+6    ring_side_right   842.703781   842.703781  513.191605   386.876520 -0.260558 -0.110271  0.179140        775      1024
+```
+
+## Privacy
+
+All faces and license plates, whether inside vehicles or outside of the drivable area, are blurred extensively to preserve privacy.
diff --git a/src/av2/datasets/tbv/splits.py b/src/av2/datasets/tbv/splits.py
new file mode 100644
index 00000000..358814b2
--- /dev/null
+++ b/src/av2/datasets/tbv/splits.py
@@ -0,0 +1,4 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Splits for the Trust, but Verify (Argoverse 2 Map Change) dataset."""
+
diff --git a/src/av2/geometry/__init__.py b/src/av2/geometry/__init__.py
new file mode 100644
index 00000000..a1155471
--- /dev/null
+++ b/src/av2/geometry/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Geometry subpackage."""
diff --git a/src/av2/geometry/camera/__init__.py b/src/av2/geometry/camera/__init__.py
new file mode 100644
index 00000000..6d4b6447
--- /dev/null
+++ b/src/av2/geometry/camera/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Camera models subpackage."""
diff --git a/src/av2/geometry/camera/pinhole_camera.py b/src/av2/geometry/camera/pinhole_camera.py
new file mode 100644
index 00000000..d2380de8
--- /dev/null
+++ b/src/av2/geometry/camera/pinhole_camera.py
@@ -0,0 +1,427 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Implements a pinhole camera interface."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import Tuple, Union
+
+import numpy as np
+
+import av2.geometry.geometry as geometry_utils
+import av2.utils.io as io_utils
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt
+
+
+@dataclass(frozen=True)
+class Intrinsics:
+    """Models a camera intrinsic matrix.
+
+    Args:
+        fx_px: Horizontal focal length in pixels.
+        fy_px: Vertical focal length in pixels.
+        cx_px: Horizontal focal center in pixels.
+        cy_px: Vertical focal center in pixels.
+        width_px: Width of image in pixels.
+        height_px: Height of image in pixels.
+    """
+
+    fx_px: float
+    fy_px: float
+    cx_px: float
+    cy_px: float
+    width_px: int
+    height_px: int
+
+    @cached_property
+    def K(self) -> NDArrayFloat:
+        """Camera intrinsic matrix."""
+        K: NDArrayFloat = np.eye(3, dtype=float)
+        K[0, 0] = self.fx_px
+        K[1, 1] = self.fy_px
+        K[0, 2] = self.cx_px
+        K[1, 2] = self.cy_px
+        return K
+
+
+@dataclass(frozen=True)
+class PinholeCamera:
+    """Parameterizes a pinhole camera with zero skew.
+
+    Args:
+        ego_SE3_cam: pose of camera in the egovehicle frame (inverse of extrinsics matrix).
+        intrinsics: `Intrinsics` object containing intrinsic parameters and image dimensions.
+        cam_name: sensor name that camera parameters correspond to.
+    """
+
+    ego_SE3_cam: SE3
+    intrinsics: Intrinsics
+    cam_name: str
+
+    @property
+    def width_px(self) -> int:
+        """Return the width of the image in pixels."""
+        return self.intrinsics.width_px
+
+    @property
+    def height_px(self) -> int:
+        """Return the height of the image in pixels."""
+        return self.intrinsics.height_px
+
+    @cached_property
+    def extrinsics(self) -> NDArrayFloat:
+        """Return the camera extrinsics."""
+        return self.ego_SE3_cam.inverse().transform_matrix
+
+    @classmethod
+    def from_feather(cls, log_dir: Path, cam_name: str) -> PinholeCamera:
+        """Create a pinhole camera model from a feather file.
+
+        Note: Data is laid out with sensor names along row dimension, and columns are sensor attribute data.
+
+        Args:
+            log_dir: path to a log directory containing feather files w/ calibration info.
+            cam_name: name of the camera.
+
+        Returns:
+            A new PinholeCamera object, containing camera extrinsics and intrinsics.
+        """
+        intrinsics_path = log_dir / "calibration" / "intrinsics.feather"
+        intrinsics_df = io_utils.read_feather(intrinsics_path).set_index("sensor_name")
+        params = intrinsics_df.loc[cam_name]
+        intrinsics = Intrinsics(
+            fx_px=params["fx_px"],
+            fy_px=params["fy_px"],
+            cx_px=params["cx_px"],
+            cy_px=params["cy_px"],
+            width_px=int(params["width_px"]),
+            height_px=int(params["height_px"]),
+        )
+        sensor_name_to_pose = io_utils.read_ego_SE3_sensor(log_dir)
+        return cls(
+            ego_SE3_cam=sensor_name_to_pose[cam_name],
+            intrinsics=intrinsics,
+            cam_name=cam_name,
+        )
+
+    def cull_to_view_frustum(self, uv: NDArrayFloat, points_cam: NDArrayFloat) -> NDArrayBool:
+        """Cull 3d points to camera view frustum.
+
+        Given a set of coordinates in the image plane and corresponding points
+        in the camera coordinate reference frame, determine those points
+        that have a valid projection into the image. 3d points with valid
+        projections have x coordinates in the range [0,width_px-1], y-coordinates
+        in the range [0,height_px-1], and a positive z-coordinate (lying in
+        front of the camera frustum).
+
+        Ref: https://en.wikipedia.org/wiki/Hidden-surface_determination#Viewing-frustum_culling
+
+        Args:
+            uv: Numpy array of shape (N,2) representing image plane coordinates in [0,W-1] x [0,H-1]
+                where (H,W) are the image height and width.
+            points_cam: Numpy array of shape (N,3) representing corresponding 3d points in the camera coordinate frame.
+
+        Returns:
+            Numpy boolean array of shape (N,) indicating which points fall within the camera view frustum.
+        """
+        is_valid_x = np.logical_and(0 <= uv[:, 0], uv[:, 0] < self.width_px - 1)
+        is_valid_y = np.logical_and(0 <= uv[:, 1], uv[:, 1] < self.height_px - 1)
+        is_valid_z = points_cam[:, 2] > 0
+        is_valid_points: NDArrayBool = np.logical_and.reduce([is_valid_x, is_valid_y, is_valid_z])
+        return is_valid_points
+
+    def project_ego_to_img(
+        self, points_ego: NDArrayFloat, remove_nan: bool = False
+    ) -> Tuple[NDArrayFloat, NDArrayFloat, NDArrayBool]:
+        """Project a collection of 3d points (provided in the egovehicle frame) to the image plane.
+
+        Args:
+            points_ego: numpy array of shape (N,3) representing points in the egovehicle frame.
+            remove_nan: whether to remove coordinates that project to invalid (NaN) values.
+
+        Returns:
+            uv: image plane coordinates, as Numpy array of shape (N,2).
+            points_cam: camera frame coordinates as Numpy array of shape (N,3) representing
+            is_valid_points: boolean indicator of valid cheirality and within image boundary, as
+                boolean Numpy array of shape (N,).
+        """
+        # convert cartesian to homogeneous coordinates.
+        points_ego_hom = geometry_utils.cart_to_hom(points_ego)
+        points_cam: NDArrayFloat = self.extrinsics @ points_ego_hom.T
+
+        # remove bottom row of all 1s.
+        uv = self.intrinsics.K @ points_cam[:3, :]
+        uv = uv.T
+        points_cam = points_cam.T
+
+        if remove_nan:
+            uv, points_cam = remove_nan_values(uv, points_cam)
+
+        uv = uv[:, :2] / uv[:, 2].reshape(-1, 1)
+        is_valid_points = self.cull_to_view_frustum(uv, points_cam)
+
+        return uv, points_cam, is_valid_points
+
+    def project_cam_to_img(
+        self, points_cam: NDArrayFloat, remove_nan: bool = False
+    ) -> Tuple[NDArrayFloat, NDArrayFloat, NDArrayBool]:
+        """Project a collection of 3d points in the camera reference frame to the image plane.
+
+        Args:
+            points_cam: numpy array of shape (N,3) representing points in the egovehicle frame.
+            remove_nan: whether to remove coordinates that project to invalid (NaN) values.
+
+        Returns:
+            uv: image plane coordinates, as Numpy array of shape (N,2).
+            points_cam: camera frame coordinates as Numpy array of shape (N,3) representing
+            is_valid_points: boolean indicator of valid cheirality and within image boundary, as
+                boolean Numpy array of shape (N,).
+        """
+        uv = self.intrinsics.K @ points_cam[:3, :]
+        uv = uv.T
+        points_cam = points_cam.T
+
+        if remove_nan:
+            uv, points_cam = remove_nan_values(uv, points_cam)
+
+        uv = uv[:, :2] / uv[:, 2].reshape(-1, 1)
+        is_valid_points = self.cull_to_view_frustum(uv, points_cam)
+
+        return uv, points_cam, is_valid_points
+
+    def project_ego_to_img_motion_compensated(
+        self,
+        points_lidar_time: NDArrayFloat,
+        city_SE3_ego_cam_t: SE3,
+        city_SE3_ego_lidar_t: SE3,
+    ) -> Tuple[NDArrayFloat, NDArrayFloat, NDArrayBool]:
+        """Project points in the ego frame to the image with motion compensation.
+
+        Because of the high frame rate, motion compensation's role between the
+        sensors is not very significant, moving points only by millimeters
+        to centimeters. If the vehicle is moving at 25 miles per hour, equivalent
+        to 11 meters/sec, then in 17 milliseconds (the max time between a lidar sweep
+        and camera image capture) we should be able to move up to 187 millimeters.
+
+        This can be verified in practice as the mean_change:
+            mean_change = np.amax(points_h_cam_time.T[:,:3] - points_h_lidar_time ,axis=0)
+
+        Adjust LiDAR points for egovehicle motion. This function accepts the
+        egovehicle's pose in the city map both at camera time and also at
+        the LiDAR time.
+
+        We perform the following transformation:
+
+            pt_egovehicle_cam_t = egovehicle_cam_t_SE3_city * city_SE3_egovehicle_lidar_t * pt_egovehicle_lidar_t
+
+        Note that both "cam_time_points_h" and "lidar_time_points_h" are 3D points in the
+        vehicle coordinate frame, but captured at different times. These LiDAR points
+        always live in the vehicle frame, but just in different timestamps. If we take
+        a lidar point in the egovehicle frame, captured at lidar time, and bring it into
+        the map at this lidar timestamp, then we know the transformation from map to
+        egovehicle reference frame at the time when the camera image was captured.
+
+        Thus, we move from egovehicle @ lidar time, to the map (which is time agnostic),
+        then we move from map to egovehicle @ camera time. Now we suddenly have lidar points
+        living in the egovehicle frame @ camera time.
+
+        Args:
+            points_lidar_time: Numpy array of shape (N,3)
+            city_SE3_ego_cam_t: egovehicle pose when camera image was recorded.
+            city_SE3_ego_lidar_t: egovehicle pose when LiDAR sweep was recorded.
+
+        Returns:
+            uv: image plane coordinates, as Numpy array of shape (N,2).
+            points_cam: Numpy array of shape (N,3) representing coordinates of points within the camera frame.
+            is_valid_points_cam: boolean indicator of valid cheirality and within image boundary, as
+                boolean Numpy array of shape (N,).
+
+        Raises:
+            ValueError: If `city_SE3_ego_cam_t` or `city_SE3_ego_lidar_t` is `None`.
+        """
+        if city_SE3_ego_cam_t is None:
+            raise ValueError("city_SE3_ego_cam_t cannot be `None`!")
+        if city_SE3_ego_lidar_t is None:
+            raise ValueError("city_SE3_ego_lidar_t cannot be `None`!")
+
+        ego_cam_t_SE3_ego_lidar_t = city_SE3_ego_cam_t.inverse().compose(city_SE3_ego_lidar_t)
+        points_cam_time = ego_cam_t_SE3_ego_lidar_t.transform_point_cloud(points_lidar_time)
+        return self.project_ego_to_img(points_cam_time)
+
+    @cached_property
+    def right_clipping_plane(self) -> NDArrayFloat:
+        """Form the right clipping plane for a camera view frustum.
+
+        Returns:
+            (4,) tuple of Hessian normal coefficients.
+        """
+        a, b, c, d = -self.intrinsics.fx_px, 0.0, self.width_px / 2.0, 0.0
+        coeffs: NDArrayFloat = np.array([a, b, c, d]) / np.linalg.norm([a, b, c])  # type: ignore
+        return coeffs
+
+    @cached_property
+    def left_clipping_plane(self) -> NDArrayFloat:
+        """Form the left clipping plane for a camera view frustum.
+
+        Returns:
+            (4,) tuple of Hessian normal coefficients.
+        """
+        a, b, c, d = self.intrinsics.fx_px, 0.0, self.width_px / 2.0, 0.0
+        coeffs: NDArrayFloat = np.array([a, b, c, d]) / np.linalg.norm([a, b, c])  # type: ignore
+        return coeffs
+
+    @cached_property
+    def top_clipping_plane(self) -> NDArrayFloat:
+        """Top clipping plane for a camera view frustum.
+
+        Returns:
+            (4,) tuple of Hessian normal coefficients.
+        """
+        a, b, c, d = 0.0, self.intrinsics.fx_px, self.height_px / 2.0, 0.0
+        coeffs: NDArrayFloat = np.array([a, b, c, d]) / np.linalg.norm([a, b, c])  # type: ignore
+        return coeffs
+
+    @cached_property
+    def bottom_clipping_plane(self) -> NDArrayFloat:
+        """Bottom clipping plane for a camera view frustum.
+
+        Returns:
+            (4,) tuple of Hessian normal coefficients.
+        """
+        a, b, c, d = 0.0, -self.intrinsics.fx_px, self.height_px / 2.0, 0.0
+        coeffs: NDArrayFloat = np.array([a, b, c, d]) / np.linalg.norm([a, b, c])  # type: ignore
+        return coeffs
+
+    def near_clipping_plane(self, near_clip_m: float) -> NDArrayFloat:
+        """Near clipping plane for a camera view frustum.
+
+        Args:
+            near_clip_m: Near clipping plane distance in meters.
+
+        Returns:
+            (4,) tuple of Hessian normal coefficients.
+        """
+        a, b, c, d = 0.0, 0.0, 1.0, -near_clip_m
+        coeffs: NDArrayFloat = np.array([a, b, c, d])
+        return coeffs
+
+    def frustum_planes(self, near_clip_dist: float = 0.5) -> NDArrayFloat:
+        """Compute the planes enclosing the field of view (view frustum).
+
+            Reference (1): https://en.wikipedia.org/wiki/Viewing_frustum
+            Reference (2): https://en.wikipedia.org/wiki/Plane_(geometry)
+
+            Solve for the coefficients of all frustum planes:
+                ax + by + cz = d
+
+        Args:
+            near_clip_dist: Distance of the near clipping plane from the origin.
+
+        Returns:
+            (5, 4) matrix where each row corresponds to the coeffients of a plane.
+        """
+        left_plane = self.left_clipping_plane
+        right_plane = self.right_clipping_plane
+
+        top_plane = self.top_clipping_plane
+        bottom_plane = self.bottom_clipping_plane
+
+        near_plane = self.near_clipping_plane(near_clip_dist)
+        planes: NDArrayFloat = np.stack([left_plane, right_plane, near_plane, bottom_plane, top_plane])
+        return planes
+
+    @cached_property
+    def egovehicle_yaw_cam_rad(self) -> float:
+        """Compute the camera's yaw, in the egovehicle frame.
+
+        R takes the x axis to be a vector equivalent to the first column of R.
+        Similarly, the y and z axes are transformed to be the second and third columns.
+
+        Returns:
+            Counter-clockwise angle from x=0 (in radians) of camera center ray, in the egovehicle frame.
+        """
+        egovehicle_SE3_camera = self.ego_SE3_cam
+        # the third column of this rotation matrix, is the new basis vector for the z axis (pointing out of camera)
+        # take its x and y components (the z component is near zero, as close to horizontal)
+        new_z_axis = egovehicle_SE3_camera.rotation[:, 2]
+        dx, dy, dz = new_z_axis
+        egovehicle_yaw_cam_rad = np.arctan2(dy, dx)
+        return float(egovehicle_yaw_cam_rad)
+
+    @cached_property
+    def fov_theta_rad(self) -> float:
+        """Compute the field of view of a camera frustum to use for view frustum culling during rendering.
+
+        Returns:
+            Angular extent of camera's field of view (measured in radians).
+        """
+        fov_theta_rad = 2 * np.arctan(0.5 * self.width_px / self.intrinsics.fx_px)
+        return float(fov_theta_rad)
+
+    def compute_pixel_ray_directions(self, uv: Union[NDArrayFloat, NDArrayInt]) -> NDArrayFloat:
+        """Given (u,v) coordinates and intrinsics, generate pixel rays in the camera coordinate frame.
+
+        Assume +z points out of the camera, +y is downwards, and +x is across the imager.
+
+        Args:
+            uv: Numpy array of shape (N,2) with (u,v) coordinates
+
+        Returns:
+            Array of shape (N,3) with ray directions to each pixel, provided in the camera frame.
+
+        Raises:
+            ValueError: If input (u,v) coordinates are not (N,2) in shape.
+            RuntimeError: If generated ray directions are not (N,3) in shape.
+        """
+        fx, fy = self.intrinsics.fx_px, self.intrinsics.fy_px
+        img_h, img_w = self.height_px, self.width_px
+
+        if not np.isclose(fx, fy, atol=1e-3):
+            raise ValueError(f"Focal lengths in the x and y directions must match: {fx} != {fy}")
+
+        if uv.shape[1] != 2:
+            raise ValueError("Input (u,v) coordinates must be (N,2) in shape.")
+
+        # Approximation for principal point
+        px = img_w / 2
+        py = img_h / 2
+
+        u = uv[:, 0]
+        v = uv[:, 1]
+        num_rays = uv.shape[0]
+        ray_dirs = np.zeros((num_rays, 3))
+        # x center offset from center
+        ray_dirs[:, 0] = u - px
+        # y center offset from center
+        ray_dirs[:, 1] = v - py
+        ray_dirs[:, 2] = fx
+
+        # elementwise multiplication of scalars requires last dim to match
+        ray_dirs = ray_dirs / np.linalg.norm(ray_dirs, axis=1, keepdims=True)  # type: ignore
+        if ray_dirs.shape[1] != 3:
+            raise RuntimeError("Ray directions must be (N,3)")
+        return ray_dirs
+
+
+def remove_nan_values(uv: NDArrayFloat, points_cam: NDArrayFloat) -> Tuple[NDArrayFloat, NDArrayFloat]:
+    """Remove NaN values from camera coordinates and image plane coordinates (accepts corrupt array).
+
+    Args:
+        uv: image plane coordinates, as Numpy array of shape (N,2).
+        points_cam: Numpy array of shape (N,3) representing coordinates of points within the camera frame.
+
+    Returns:
+        uv_valid: subset of image plane coordinates, which contain no NaN coordinates.
+        is_valid_points_cam: subset of 3d points within the camera frame, which contain no NaN coordinates.
+    """
+    is_u_valid = np.logical_not(np.isnan(uv[:, 0]))
+    is_v_valid = np.logical_not(np.isnan(uv[:, 1]))
+    is_uv_valid = np.logical_and(is_u_valid, is_v_valid)
+
+    uv_valid = uv[is_uv_valid]
+    is_valid_points_cam = points_cam[is_uv_valid]
+    return uv_valid, is_valid_points_cam
diff --git a/src/av2/geometry/geometry.py b/src/av2/geometry/geometry.py
new file mode 100644
index 00000000..8865765a
--- /dev/null
+++ b/src/av2/geometry/geometry.py
@@ -0,0 +1,295 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Geometric utilities for manipulation point clouds, rigid objects, and vector geometry."""
+
+from typing import Tuple, Union
+
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+from av2.utils.constants import PI
+from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt
+
+
+def wrap_angles(angles: NDArrayFloat, period: float = PI) -> NDArrayFloat:
+    """Map angles (in radians) from domain [-∞, ∞] to [0, π).
+
+    Args:
+        angles: (N,) array of angles
+        period: Length of the domain.
+
+    Returns:
+        Angles (in radians) mapped to the interval [0, π).
+    """
+    # Map angles to [0, ∞].
+    angles = np.abs(angles)
+
+    # Calculate floor division and remainder simultaneously.
+    divs, mods = np.divmod(angles, period)
+
+    # Select angles which exceed specified period.
+    angle_complement_mask = np.nonzero(divs)
+
+    # Take set complement of `mods` w.r.t. the set [0, π].
+    # `mods` must be nonzero, thus the image is the interval [0, π).
+    angles[angle_complement_mask] = period - mods[angle_complement_mask]
+    return angles
+
+
+def xy_to_uv(xy: NDArrayFloat, width: int, height: int) -> NDArrayFloat:
+    """Convert coordinates in R^2 (x,y) to texture coordinates (u,v) in R^2.
+
+         (x,y) coordinates             (u,v) coordinates
+                      (+y)             (0,0) - - - - - (+u)
+                       |                 |
+                       |        ->       |
+                       |                 |
+        (+x) - - - - (0,0)              (+v)
+
+    The xy to uv coordinate transformation is shown above. We model pixel coordinates
+    using the uv texture mapping convention.
+
+    NOTE: Ellipses indicate any number of proceeding dimensions allowed for input.
+
+    Args:
+        xy: (...,2) array of coordinates in R^2 (x,y).
+        width: Texture grid width.
+        height: Texture grid height.
+
+    Returns:
+        (...,2) array of texture / pixel coordinates.
+    """
+    x = xy[..., 0]
+    y = xy[..., 1]
+
+    u = width - x - 1
+    v = height - y - 1
+    return np.stack((u, v), axis=-1)
+
+
+def quat_to_mat(quat_wxyz: NDArrayFloat) -> NDArrayFloat:
+    """Convert a quaternion to a 3D rotation matrix.
+
+    NOTE: SciPy uses the scalar last quaternion notation. Throughout this repository,
+        we use the scalar FIRST convention.
+
+    Args:
+        quat_wxyz: (...,4) array of quaternions in scalar first order.
+
+    Returns:
+        (...,3,3) 3D rotation matrix.
+    """
+    # Convert quaternion from scalar first to scalar last.
+    quat_xyzw = quat_wxyz[..., [1, 2, 3, 0]]
+    mat: NDArrayFloat = Rotation.from_quat(quat_xyzw).as_matrix()
+    return mat
+
+
+def mat_to_quat(mat: NDArrayFloat) -> NDArrayFloat:
+    """Convert a 3D rotation matrix to a scalar _first_ quaternion.
+
+    NOTE: SciPy uses the scalar last quaternion notation. Throughout this repository,
+        we use the scalar FIRST convention.
+
+    Args:
+        mat: (...,3,3) 3D rotation matrices.
+
+    Returns:
+        (...,4) Array of scalar first quaternions.
+    """
+    # Convert quaternion from scalar first to scalar last.
+    quat_xyzw: NDArrayFloat = Rotation.from_matrix(mat).as_quat()
+    quat_wxyz: NDArrayFloat = quat_xyzw[..., [3, 0, 1, 2]]
+    return quat_wxyz
+
+
+def mat_to_xyz(mat: NDArrayFloat) -> NDArrayFloat:
+    """Convert a 3D rotation matrix to a sequence of _extrinsic_ rotations.
+
+    In other words, 3D rotation matrix and returns a sequence of Tait-Bryan angles
+    representing the transformation.
+
+    Reference: https://en.wikipedia.org/wiki/Euler_angles#Rotation_matrix
+    Reference: https://en.wikipedia.org/wiki/Euler_angles#Tait%E2%80%93Bryan_angles_2
+
+    Args:
+        mat: (...,3,3) Rotation matrix.
+
+    Returns:
+        (...,3) Tait-Bryan angles (in radians) formulated for a sequence of extrinsic rotations.
+    """
+    xyz_rad: NDArrayFloat = Rotation.from_matrix(mat).as_euler("xyz", degrees=False)
+    return xyz_rad
+
+
+def xyz_to_mat(xyz_rad: NDArrayFloat) -> NDArrayFloat:
+    """Convert a sequence of rotations about the (x,y,z) axes to a 3D rotation matrix.
+
+    In other words, this function takes in a sequence of Tait-Bryan angles and
+    returns a 3D rotation matrix which represents the sequence of rotations.
+
+    Computes:
+        R = Rz(z) * Ry(y) * Rx(x)
+
+    Reference: https://en.wikipedia.org/wiki/Euler_angles#Tait%E2%80%93Bryan_angles_2
+    Reference: https://en.wikipedia.org/wiki/Euler_angles#Rotation_matrix
+
+    Args:
+        xyz_rad: (...,3) Tait-Bryan angles (in radians) of extrinsic rotations.
+
+    Returns:
+        (...,3,3) 3D Rotation matrix.
+    """
+    mat: NDArrayFloat = Rotation.from_euler("xyz", xyz_rad, degrees=False).as_matrix()
+    return mat
+
+
+def cart_to_sph(xyz: NDArrayFloat) -> NDArrayFloat:
+    """Convert Cartesian coordinates into spherical coordinates.
+
+    This function converts a set of points in R^3 to its spherical representation in R^3.
+
+    NOTE: Ellipses indicate any number of proceeding dimensions allowed for input.
+
+    Args:
+        xyz: (...,3) Array of points (x,y,z) in Cartesian space.
+
+    Returns:
+        (...,3) Array in spherical space. [Order: (azimuth, inclination, radius)].
+    """
+    x = xyz[..., 0]
+    y = xyz[..., 1]
+    z = xyz[..., 2]
+
+    hypot_xy = np.hypot(x, y)
+    radius = np.hypot(hypot_xy, z)
+    inclination = np.arctan2(z, hypot_xy)
+    azimuth = np.arctan2(y, x)
+
+    return np.stack((azimuth, inclination, radius), axis=-1)
+
+
+def cart_to_hom(cart: NDArrayFloat) -> NDArrayFloat:
+    """Convert Cartesian coordinates into Homogenous coordinates.
+
+    This function converts a set of points in R^N to its homogeneous representation in R^(N+1).
+
+    Args:
+        cart: (M,N) Array of points in Cartesian space.
+
+    Returns:
+        NDArrayFloat: (M,N+1) Array in Homogeneous space.
+    """
+    M, N = cart.shape
+    hom: NDArrayFloat = np.ones((M, N + 1))
+    hom[:, :N] = cart
+    return hom
+
+
+def hom_to_cart(hom: NDArrayFloat) -> NDArrayFloat:
+    """Convert Homogenous coordinates into Cartesian coordinates.
+
+    This function converts a set of points in R^(N+1) to its Cartesian representation in R^N.
+
+    Args:
+        hom: (M,N+1) Array of points in Homogeneous space.
+
+    Returns:
+        NDArrayFloat: (M,N) Array in Cartesian space.
+    """
+    N = hom.shape[1] - 1
+    cart: NDArrayFloat = hom[:, :N] / hom[:, N : N + 1]
+    return cart
+
+
+def crop_points(
+    points: Union[NDArrayFloat, NDArrayInt],
+    lower_bound_inclusive: Tuple[float, ...],
+    upper_bound_exclusive: Tuple[float, ...],
+) -> Tuple[NDArrayFloat, NDArrayFloat]:
+    """Crop points to a lower and upper boundary.
+
+    NOTE: Ellipses indicate any number of proceeding dimensions allowed for input.
+
+    Args:
+        points: (...,n) n-dimensional array of points.
+        lower_bound_inclusive: (n,) Coordinates lower bound (inclusive).
+        upper_bound_exclusive: (n,) Coordinates upper bound (exclusive).
+
+    Raises:
+        ValueError: If dimensions between xyz and the provided bounds don't match.
+
+    Returns:
+        (...,n) Tuple of cropped points and the corresponding boolean mask.
+    """
+    # Gather dimensions.
+    n_dim = points.shape[-1]
+    lb_dim = len(lower_bound_inclusive)
+    ub_dim = len(upper_bound_exclusive)
+
+    # Ensure that the logical operations will broadcast.
+    if n_dim != lb_dim or n_dim != ub_dim:
+        raise ValueError(f"Dimensions n_dim {n_dim} must match both lb_dim {lb_dim} and ub_dim {ub_dim}")
+
+    # Ensure that the lower bound less than or equal to the upper bound for each dimension.
+    if not all(lb < ub for lb, ub in zip(lower_bound_inclusive, upper_bound_exclusive)):
+        raise ValueError("Lower bound must be less than or equal to upper bound for each dimension")
+
+    # Lower bound mask.
+    lb_mask = np.greater_equal(points, lower_bound_inclusive)
+
+    # Upper bound mask.
+    ub_mask = np.less(points, upper_bound_exclusive)
+
+    # Bound mask.
+    is_valid_points = np.logical_and(lb_mask, ub_mask).all(axis=-1)
+    return points[is_valid_points], is_valid_points
+
+
+def compute_interior_points_mask(points_xyz: NDArrayFloat, cuboid_vertices: NDArrayFloat) -> NDArrayBool:
+    r"""Compute the interior points mask for the cuboid.
+
+    Reference: https://math.stackexchange.com/questions/1472049/check-if-a-point-is-inside-a-rectangular-shaped-area-3d
+
+            5------4
+            |\\    |\\
+            | \\   | \\
+            6--\\--7  \\
+            \\  \\  \\ \\
+        l    \\  1-------0    h
+         e    \\ ||   \\ ||   e
+          n    \\||    \\||   i
+           g    \\2------3    g
+            t      width.     h
+             h.               t.
+
+    Args:
+        points_xyz: (N,3) Array representing a point cloud in Cartesian coordinates (x,y,z).
+        cuboid_vertices: (8,3) Array representing 3D cuboid vertices, ordered as shown above. 
+   
+    Returns:
+        (N,) An array of boolean flags indicating whether the points are interior to the cuboid.
+    """
+    # Get three corners of the cuboid vertices.
+    vertices: NDArrayFloat = np.stack((cuboid_vertices[6], cuboid_vertices[3], cuboid_vertices[1]))  # (3,3)
+
+    # Choose reference vertex.
+    # vertices and choice of ref_vertex are coupled.
+    ref_vertex = cuboid_vertices[2]  # (3,)
+
+    # Compute orthogonal edges of the cuboid.
+    uvw = ref_vertex - vertices  # (3,3)
+
+    # Compute signed values which are proportional to the distance from the vector.
+    sim_uvw_points = points_xyz @ uvw.transpose()  # (N,3)
+    sim_uvw_ref = uvw @ ref_vertex  # (3,)
+
+    # Only care about the diagonal.
+    sim_uvw_vertices: NDArrayFloat = np.diag(uvw @ vertices.transpose())  # type: ignore # (3,)
+
+    # Check 6 conditions (2 for each of the 3 orthogonal directions).
+    # Refer to the linked reference for additional information.
+    constraint_a = np.logical_and(sim_uvw_ref <= sim_uvw_points, sim_uvw_points <= sim_uvw_vertices)
+    constraint_b = np.logical_and(sim_uvw_ref >= sim_uvw_points, sim_uvw_points >= sim_uvw_vertices)
+    is_interior: NDArrayBool = np.logical_or(constraint_a, constraint_b).all(axis=1)
+    return is_interior
diff --git a/src/av2/geometry/infinity_norm_utils.py b/src/av2/geometry/infinity_norm_utils.py
new file mode 100644
index 00000000..959a65a4
--- /dev/null
+++ b/src/av2/geometry/infinity_norm_utils.py
@@ -0,0 +1,42 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utilities for querying distance to an object by infinity norm."""
+
+import numpy as np
+
+from av2.utils.typing import NDArrayFloat
+
+
+def has_pts_in_infinity_norm_radius(points: NDArrayFloat, window_center: NDArrayFloat, window_sz: float) -> bool:
+    """Check if a map entity has points within a search radius from a single query point.
+
+    Note: Distance is measured by the infinity norm.
+
+    Args:
+        points: (N,2) or (N,3) array, representing map entity coordinates.
+        window_center: (1,2), or (2,) array, representing query point.
+        window_sz: search radius, in meters.
+
+    Returns:
+        boolean array of shape (N,) indicating which points lie within the search radius.
+
+    Raises:
+        ValueError: If `points` is not in R^2 or R^3.
+    """
+    if points.ndim != 2 or points.shape[1] not in (2, 3):
+        raise ValueError(f"Input points array must have shape (N,2) or (N,3) - received {points.shape}.")
+
+    if points.shape[1] == 3:
+        # take only x,y dimensions
+        points = points[:, :2]
+
+    if window_center.ndim == 2:
+        window_center = window_center.squeeze()
+    if window_center.size == 3:
+        window_center = window_center[:2]
+
+    # reshape just in case was given column vector
+    window_center = window_center.reshape(1, 2)
+
+    dists = np.linalg.norm(points - window_center, ord=np.inf, axis=1)  # type: ignore
+    return bool(dists.min() < window_sz)
diff --git a/src/av2/geometry/interpolate.py b/src/av2/geometry/interpolate.py
new file mode 100644
index 00000000..8b0af1bb
--- /dev/null
+++ b/src/av2/geometry/interpolate.py
@@ -0,0 +1,241 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utilities for interpolating polylines or poses."""
+
+from typing import Final, Tuple
+
+import numpy as np
+from scipy.spatial.transform import Rotation, Slerp
+
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayFloat, NDArrayInt
+
+# For a single line segment
+NUM_CENTERLINE_INTERP_PTS: Final[int] = 10
+
+
+def compute_lane_width(left_even_pts: NDArrayFloat, right_even_pts: NDArrayFloat) -> float:
+    """Compute the width of a lane, given an explicit left and right boundary.
+
+    Requires an equal number of waypoints on each boundary. For 3d polylines, this incorporates
+    the height difference between the left and right polyline into the lane width as a hypotenuse
+    of triangle formed by lane width in a flat plane, and the height difference.
+
+    Args:
+        left_even_pts: Numpy array of shape (N,2) or (N,3)
+        right_even_pts: Numpy array of shape (N,2) or (N,3)
+
+    Raises:
+        ValueError: If the shapes of left_even_pts and right_even_pts don't match.
+
+    Returns:
+        float representing average width of a lane
+    """
+    if left_even_pts.shape != right_even_pts.shape:
+        raise ValueError(
+            f"Shape of left_even_pts {left_even_pts.shape} did not match right_even_pts {right_even_pts.shape}"
+        )
+    lane_width = float(np.mean(np.linalg.norm(left_even_pts - right_even_pts, axis=1)))  # type: ignore
+    return lane_width
+
+
+def compute_mid_pivot_arc(single_pt: NDArrayFloat, arc_pts: NDArrayFloat) -> Tuple[NDArrayFloat, float]:
+    """Compute an arc by pivoting around a single point.
+
+    Given a line of points on one boundary, and a single point on the other side,
+    produce the middle arc we get by pivoting around the single point.
+
+    Occurs when mapping cul-de-sacs.
+
+    Args:
+        single_pt: Numpy array of shape (2,) or (3,) representing a single 2d or 3d coordinate.
+        arc_pts: Numpy array of shape (N,2) or (N,3) representing a 2d or 3d polyline.
+
+    Returns:
+        centerline_pts: Numpy array of shape (N,3)
+        lane_width: average width of the lane.
+    """
+    num_pts = len(arc_pts)
+    # form ladder with equal number of vertices on each side
+    single_pt_tiled = np.tile(single_pt, (num_pts, 1))  # type: ignore
+    # compute midpoint for each rung of the ladder
+    centerline_pts = (single_pt_tiled + arc_pts) / 2.0
+    lane_width = compute_lane_width(single_pt_tiled, arc_pts)
+    return centerline_pts, lane_width
+
+
+def compute_midpoint_line(
+    left_ln_boundary: NDArrayFloat,
+    right_ln_boundary: NDArrayFloat,
+    num_interp_pts: int = NUM_CENTERLINE_INTERP_PTS,
+) -> Tuple[NDArrayFloat, float]:
+    """Compute the midpoint line from left and right lane segments.
+
+    Interpolate n points along each lane boundary, and then average the left and right waypoints.
+
+    Note that the number of input waypoints along the left and right boundaries
+    can be vastly different -- consider cul-de-sacs, for example.
+
+    Args:
+        left_ln_boundary: Numpy array of shape (M,2)
+        right_ln_boundary: Numpy array of shape (N,2)
+        num_interp_pts: number of midpoints to compute for this lane segment,
+            except if it is a cul-de-sac, in which case the number of midpoints
+            will be equal to max(M,N).
+
+    Returns:
+        centerline_pts: Numpy array of shape (N,2) representing centerline of ladder.
+
+    Raises:
+        ValueError: If the left and right lane boundaries aren't a list of 2d or 3d waypoints.
+    """
+    if left_ln_boundary.ndim != 2 or right_ln_boundary.ndim != 2:
+        raise ValueError("Left and right lane boundaries must consist of a sequence of 2d or 3d waypoints.")
+
+    dim = left_ln_boundary.shape[1]
+    if dim not in [2, 3]:
+        raise ValueError("Left and right lane boundaries must be 2d or 3d.")
+
+    if left_ln_boundary.shape[1] != right_ln_boundary.shape[1]:
+        raise ValueError("Left ")
+
+    if len(left_ln_boundary) == 1:
+        centerline_pts, lane_width = compute_mid_pivot_arc(single_pt=left_ln_boundary, arc_pts=right_ln_boundary)
+        return centerline_pts[:, :2], lane_width
+
+    if len(right_ln_boundary) == 1:
+        centerline_pts, lane_width = compute_mid_pivot_arc(single_pt=right_ln_boundary, arc_pts=left_ln_boundary)
+        return centerline_pts[:, :2], lane_width
+
+    # fall back to the typical case.
+    left_even_pts = interp_arc(num_interp_pts, points=left_ln_boundary)
+    right_even_pts = interp_arc(num_interp_pts, points=right_ln_boundary)
+
+    centerline_pts = (left_even_pts + right_even_pts) / 2.0  # type: ignore
+
+    lane_width = compute_lane_width(left_even_pts, right_even_pts)
+    return centerline_pts, lane_width
+
+
+def interp_arc(t: int, points: NDArrayFloat) -> NDArrayFloat:
+    """Linearly interpolate equally-spaced points along a polyline, either in 2d or 3d.
+
+    We use a chordal parameterization so that interpolated arc-lengths
+    will approximate original polyline chord lengths.
+        Ref: M. Floater and T. Surazhsky, Parameterization for curve
+            interpolation. 2005.
+            https://www.mathworks.com/matlabcentral/fileexchange/34874-interparc
+
+    For the 2d case, we remove duplicate consecutive points, since these have zero
+    distance and thus cause division by zero in chord length computation.
+
+    Args:
+        t: number of points that will be uniformly interpolated and returned
+        points: Numpy array of shape (N,2) or (N,3), representing 2d or 3d-coordinates of the arc.
+
+    Returns:
+        Numpy array of shape (N,2)
+
+    Raises:
+        ValueError: If `points` is not in R^2 or R^3.
+    """
+    if points.ndim != 2:
+        raise ValueError("Input array must be (N,2) or (N,3) in shape.")
+
+    # the number of points on the curve itself
+    n, _ = points.shape
+
+    # equally spaced in arclength -- the number of points that will be uniformly interpolated
+    eq_spaced_points = np.linspace(0, 1, t)
+
+    # Compute the chordal arclength of each segment.
+    # Compute differences between each x coord, to get the dx's
+    # Do the same to get dy's. Then the hypotenuse length is computed as a norm.
+    chordlen: NDArrayFloat = np.linalg.norm(np.diff(points, axis=0), axis=1)  # type: ignore
+    # Normalize the arclengths to a unit total
+    chordlen = chordlen / np.sum(chordlen)
+    # cumulative arclength
+
+    cumarc: NDArrayFloat = np.zeros(len(chordlen) + 1)
+    cumarc[1:] = np.cumsum(chordlen)
+
+    # which interval did each point fall in, in terms of eq_spaced_points? (bin index)
+    tbins: NDArrayInt = np.digitize(eq_spaced_points, bins=cumarc).astype(int)  # type: ignore
+
+    # #catch any problems at the ends
+    tbins[np.where((tbins <= 0) | (eq_spaced_points <= 0))] = 1  # type: ignore
+    tbins[np.where((tbins >= n) | (eq_spaced_points >= 1))] = n - 1
+
+    s = np.divide((eq_spaced_points - cumarc[tbins - 1]), chordlen[tbins - 1])
+    anchors = points[tbins - 1, :]
+    # broadcast to scale each row of `points` by a different row of s
+    offsets = (points[tbins, :] - points[tbins - 1, :]) * s.reshape(-1, 1)
+    points_interp: NDArrayFloat = anchors + offsets
+
+    return points_interp
+
+
+def linear_interpolation(
+    key_timestamps: Tuple[int, int], key_translations: Tuple[NDArrayFloat, NDArrayFloat], query_timestamp: int
+) -> NDArrayFloat:
+    """Given two 3d positions at specific timestamps, interpolate an intermediate position at a given timestamp.
+
+    Args:
+        key_timestamps: pair of integer-valued nanosecond timestamps (representing t0 and t1).
+        key_translations: pair of (3,) arrays, representing 3d positions.
+        query_timestamp: interpolate the position at this timestamp.
+
+    Returns:
+        interpolated translation (3,).
+
+    Raises:
+        ValueError: If query_timestamp does not fall within [t0,t1].
+    """
+    t0, t1 = key_timestamps
+    if query_timestamp < t0 or query_timestamp > t1:
+        raise ValueError("Query timestamp must be witin the interval [t0,t1].")
+
+    interval = t1 - t0
+    t = (query_timestamp - t0) / interval
+
+    vec = key_translations[1] - key_translations[0]  # type: ignore
+    translation_interp = key_translations[0] + vec * t  # type: ignore
+    return translation_interp
+
+
+def interpolate_pose(key_timestamps: Tuple[int, int], key_poses: Tuple[SE3, SE3], query_timestamp: int) -> SE3:
+    """Given two SE(3) poses at specific timestamps, interpolate an intermediate pose at a given timestamp.
+
+    Note: we use a straight line interpolation for the translation, while still using interpolate (aka "slerp")
+    for the rotational component.
+
+    Other implementations are possible, see:
+    https://github.com/borglab/gtsam/blob/develop/gtsam/geometry/Pose3.h#L129
+    https://github.com/borglab/gtsam/blob/744db328e7ae537e71329e04cc141b3a28b0d6bd/gtsam/base/Lie.h#L327
+
+    Args:
+        key_timestamps: list of timestamps, representing timestamps of the keyframes.
+        key_poses: list of poses, representing the keyframes.
+        query_timestamp: interpolate the pose at this timestamp.
+
+    Returns:
+        Inferred SE(3) pose at the query time.
+
+    Raises:
+        ValueError: If query_timestamp does not fall within [t0,t1].
+    """
+    t0, t1 = key_timestamps
+    if query_timestamp < t0 or query_timestamp > t1:
+        raise ValueError("Query timestamp must be witin the interval [t0,t1].")
+
+    # Setup the fixed keyframe rotations and times
+    key_rots = Rotation.from_matrix(np.array([kp.rotation for kp in key_poses]))
+    slerp = Slerp(key_timestamps, key_rots)
+
+    # Interpolate the rotations at the given time:
+    R_interp = slerp(query_timestamp).as_matrix()
+
+    key_translations = (key_poses[0].translation, key_poses[1].translation)
+    t_interp = linear_interpolation(key_timestamps, key_translations=key_translations, query_timestamp=query_timestamp)
+    pose_interp = SE3(rotation=R_interp, translation=t_interp)
+    return pose_interp
diff --git a/src/av2/geometry/mesh_grid.py b/src/av2/geometry/mesh_grid.py
new file mode 100644
index 00000000..275d3a80
--- /dev/null
+++ b/src/av2/geometry/mesh_grid.py
@@ -0,0 +1,45 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Mesh grid utility functions."""
+
+import math
+
+import numpy as np
+
+from av2.utils.typing import NDArrayFloat
+
+
+def get_mesh_grid_as_point_cloud(
+    min_x: int, max_x: int, min_y: int, max_y: int, downsample_factor: float = 1.0
+) -> NDArrayFloat:
+    """Sample regular grid and return the (x, y) coordinates.
+
+    For a (2,2) grid, 9 sampled points will be returned (representing the unique corners
+    of every grid cell).
+
+    Args:
+        min_x: Minimum x-coordinate of 2D grid
+        max_x: Maximum x-coordinate of 2D grid
+        min_y: Minimum y-coordinate of 2D grid
+        max_y: Maximum y-coordinate of 2D grid
+        downsample_factor: the resolution of the grid. Defaults to 1 m resolution.
+            For example, to sample just the 4 corners of a 3x3 grid (@ 3 meter resolution),
+            the downsample_factor should be set to 3.
+
+    Returns:
+        Array of shape (N, 2) and type float64 representing 2d (x,y) coordinates.
+    """
+    nx = max_x - min_x
+    ny = max_y - min_y
+    x = np.linspace(min_x, max_x, math.ceil(nx / downsample_factor) + 1)
+    y = np.linspace(min_y, max_y, math.ceil(ny / downsample_factor) + 1)
+    x_grid, y_grid = np.meshgrid(x, y)  # type: ignore
+
+    x_grid = x_grid.flatten()
+    y_grid = y_grid.flatten()
+
+    x_grid = x_grid[:, np.newaxis]
+    y_grid = y_grid[:, np.newaxis]
+
+    pts: NDArrayFloat = np.hstack([x_grid, y_grid])
+    return pts
diff --git a/src/av2/geometry/polyline_utils.py b/src/av2/geometry/polyline_utils.py
new file mode 100644
index 00000000..e3cf9b8e
--- /dev/null
+++ b/src/av2/geometry/polyline_utils.py
@@ -0,0 +1,211 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Polyline related utilities."""
+
+import datetime
+import math
+from typing import Final, Tuple
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import av2.geometry.interpolate as interp_utils
+from av2.utils.typing import NDArrayBool, NDArrayFloat
+
+AVG_LANE_WIDTH_M: Final[float] = 3.8
+EPS: Final[float] = 1e-10
+
+
+def get_polyline_length(polyline: NDArrayFloat) -> float:
+    """Calculate the length of a polyline.
+
+    Args:
+        polyline: Numpy array of shape (N,3)
+
+    Returns:
+        The length of the polyline as a scalar.
+
+    Raises:
+        RuntimeError: If `polyline` doesn't have shape (N,2) or (N,3).
+    """
+    if polyline.shape[1] not in [2, 3]:
+        raise RuntimeError("Polyline must have shape (N,2) or (N,3)")
+    offsets = np.diff(polyline, axis=0)  # type: ignore
+    return float(np.linalg.norm(offsets, axis=1).sum())  # type: ignore
+
+
+def interp_polyline_by_fixed_waypt_interval(polyline: NDArrayFloat, waypt_interval: float) -> Tuple[NDArrayFloat, int]:
+    """Resample waypoints of a polyline so that waypoints appear roughly at fixed intervals from the start.
+
+    Args:
+        polyline: array pf shape (N,2) or (N,3) representing a polyline.
+        waypt_interval: space interval between waypoints, in meters.
+
+    Returns:
+        interp_polyline: array of shape (N,2) or (N,3) representing a resampled/interpolated polyline.
+        num_waypts: number of computed waypoints.
+
+    Raises:
+        RuntimeError: If `polyline` doesn't have shape (N,2) or (N,3).
+    """
+    if polyline.shape[1] not in [2, 3]:
+        raise RuntimeError("Polyline must have shape (N,2) or (N,3)")
+
+    # get the total length in meters of the line segment
+    len_m = get_polyline_length(polyline)
+
+    # count number of waypoints to get the desired length
+    # add one for the extra endpoint
+    num_waypts = math.floor(len_m / waypt_interval) + 1
+    interp_polyline = interp_utils.interp_arc(t=num_waypts, points=polyline)
+    return interp_polyline, num_waypts
+
+
+def get_double_polylines(polyline: NDArrayFloat, width_scaling_factor: float) -> Tuple[NDArrayFloat, NDArrayFloat]:
+    """Treat any polyline as a centerline, and extend a narrow strip on both sides.
+
+    Dimension is preserved (2d->2d, and 3d->3d).
+
+    Args:
+        polyline: array of shape (N,2) or (N,3) representing a polyline.
+        width_scaling_factor: controls the spacing between the two polylines representing the lane boundary,
+            e.g. for a "DOUBLE_SOLID" marking.
+
+    Returns:
+        left: array of shape (N,2) or (N,3) representing left polyline.
+        right: array of shape (N,2) or (N,3) representing right polyline.
+    """
+    double_line_polygon = centerline_to_polygon(centerline=polyline, width_scaling_factor=width_scaling_factor)
+    num_pts = double_line_polygon.shape[0]
+
+    # split index -- polygon from right boundary, left boundary, then close it w/ 0th vertex of right
+    # we swap left and right since our polygon is generated about a boundary, not a centerline
+    k = num_pts // 2
+    left = double_line_polygon[:k]
+    right = double_line_polygon[k:-1]  # throw away the last point, since it is just a repeat
+
+    return left, right
+
+
+def swap_left_and_right(
+    condition: NDArrayBool, left_centerline: NDArrayFloat, right_centerline: NDArrayFloat
+) -> Tuple[NDArrayFloat, NDArrayFloat]:
+    """Swap points in left and right centerline according to condition.
+
+    Args:
+        condition: Numpy array of shape (N,) of type boolean. Where true, swap the values in the left
+            and right centerlines.
+        left_centerline: The left centerline, whose points should be swapped with the right centerline.
+        right_centerline: The right centerline.
+
+    Returns:
+        Left and right centerlines.
+    """
+    right_swap_indices = right_centerline[condition]
+    left_swap_indices = left_centerline[condition]
+
+    left_centerline[condition] = right_swap_indices
+    right_centerline[condition] = left_swap_indices
+    return left_centerline, right_centerline
+
+
+def centerline_to_polygon(
+    centerline: NDArrayFloat, width_scaling_factor: float = 1.0, visualize: bool = False
+) -> NDArrayFloat:
+    """Convert a lane centerline polyline into a rough polygon of the lane's area.
+
+    The input polyline may be 2d or 3d. Centerline height will be propagated to the two new
+    polylines.
+
+    On average, a lane is 3.8 meters in width. Thus, we allow 1.9 m on each side.
+    We use this as the length of the hypotenuse of a right triangle, and compute the
+    other two legs to find the scaled x and y displacement.
+
+    Args:
+        centerline: Numpy array of shape (N,3) or (N,2).
+        width_scaling_factor: Multiplier that scales avg. lane width to get a new lane width.
+        visualize: Save a figure showing the the output polygon.
+
+    Returns:
+        Numpy array of shape (2N+1,2) or (2N+1,3), with duplicate first and last vertices.
+    """
+    # eliminate duplicates
+    _, inds = np.unique(centerline, axis=0, return_index=True)  # type: ignore
+    # does not return indices in sorted order
+    inds = np.sort(inds)
+    centerline = centerline[inds]
+
+    grad: NDArrayFloat = np.gradient(centerline, axis=0)  # type: ignore
+    dx = grad[:, 0]
+    dy = grad[:, 1]
+
+    # compute the normal at each point
+    slopes = dy / (dx + EPS)
+    inv_slopes = -1.0 / (slopes + EPS)
+
+    thetas = np.arctan(inv_slopes)
+    x_disp = AVG_LANE_WIDTH_M * width_scaling_factor / 2.0 * np.cos(thetas)
+    y_disp = AVG_LANE_WIDTH_M * width_scaling_factor / 2.0 * np.sin(thetas)
+
+    displacement: NDArrayFloat = np.hstack([x_disp[:, np.newaxis], y_disp[:, np.newaxis]])
+
+    # preserve z coordinates.
+    right_centerline = centerline.copy()
+    right_centerline[:, :2] += displacement
+
+    left_centerline = centerline.copy()
+    left_centerline[:, :2] -= displacement
+
+    # right centerline position depends on sign of dx and dy
+    subtract_cond1 = np.logical_and(dx > 0, dy < 0)
+    subtract_cond2 = np.logical_and(dx > 0, dy > 0)
+    subtract_cond = np.logical_or(subtract_cond1, subtract_cond2)
+    left_centerline, right_centerline = swap_left_and_right(subtract_cond, left_centerline, right_centerline)
+
+    # right centerline also depended on if we added or subtracted y
+    neg_disp_cond = displacement[:, 1] > 0
+    left_centerline, right_centerline = swap_left_and_right(neg_disp_cond, left_centerline, right_centerline)
+
+    if visualize:
+        plt.scatter(centerline[:, 0], centerline[:, 1], 20, marker=".", color="b")
+        plt.scatter(right_centerline[:, 0], right_centerline[:, 1], 20, marker=".", color="r")
+        plt.scatter(left_centerline[:, 0], left_centerline[:, 1], 20, marker=".", color="g")
+        fname = datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S_%f")
+        plt.savefig(f"polygon_unit_tests/{fname}.png")
+        plt.close("all")
+
+    # return the polygon
+    return convert_lane_boundaries_to_polygon(right_centerline, left_centerline)
+
+
+def convert_lane_boundaries_to_polygon(right_lane_bounds: NDArrayFloat, left_lane_bounds: NDArrayFloat) -> NDArrayFloat:
+    """Convert lane boundaries to a polygon.
+
+    Given left and right boundaries of a lane segment, provide the exterior vertices of the
+        2d or 3d lane segment polygon.
+
+    NOTE: We chain the right segment with a reversed left segment, and then repeat the first vertex. In other words,
+    the first and last vertex are identical.
+
+    L _________
+      .       .
+      .       .
+    R _________
+
+    Args:
+        right_lane_bounds: Array of shape (K,2) or (K,3) representing right lane boundary points.
+        left_lane_bounds: Array of shape (M,2) or (M,3) representing left lane boundary points.
+
+    Returns:
+        Numpy array of shape (K+M+1,2) or (K+M+1,3)
+
+    Raises:
+        RuntimeError: If the last dimension of the left and right boundary polylines do not match.
+    """
+    if not right_lane_bounds.shape[-1] == left_lane_bounds.shape[-1]:
+        raise RuntimeError("Last dimension of left and right boundary polylines must match.")
+
+    polygon: NDArrayFloat = np.vstack([right_lane_bounds, left_lane_bounds[::-1], right_lane_bounds[0]])
+    if not polygon.ndim == 2 or polygon.shape[1] not in [2, 3]:
+        raise RuntimeError("Polygons must be Nx2 or Nx3 in shape.")
+    return polygon
diff --git a/src/av2/geometry/se3.py b/src/av2/geometry/se3.py
new file mode 100644
index 00000000..db199b97
--- /dev/null
+++ b/src/av2/geometry/se3.py
@@ -0,0 +1,99 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""SE(3) lie group for rigid transformations on points."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import cached_property
+
+import numpy as np
+
+from av2.utils.typing import NDArrayFloat
+
+
+@dataclass
+class SE3:
+    """SE(3) lie group object.
+
+    References:
+        http://ethaneade.com/lie_groups.pdf
+
+    Args:
+        rotation: Array of shape (3, 3)
+        translation: Array of shape (3,)
+    """
+
+    rotation: NDArrayFloat
+    translation: NDArrayFloat
+
+    def __post_init__(self) -> None:
+        """Check validity of rotation and translation arguments.
+
+        Raises:
+            ValueError: If rotation is not shape (3,3) or translation is not shape (3,).
+        """
+        if self.rotation.shape != (3, 3):
+            raise ValueError("Rotation matrix must be shape (3,3)!")
+        if self.translation.shape != (3,):
+            raise ValueError("Translation vector must be shape (3,)")
+
+    @cached_property
+    def transform_matrix(self) -> NDArrayFloat:
+        """4x4 homogeneous transformation matrix."""
+        transform_matrix = np.eye(4)
+        transform_matrix[:3, :3] = self.rotation
+        transform_matrix[:3, 3] = self.translation
+        return transform_matrix
+
+    def transform_from(self, point_cloud: NDArrayFloat) -> NDArrayFloat:
+        """Apply the SE(3) transformation to this point cloud.
+
+        Args:
+            point_cloud: Array of shape (N, 3). If the transform represents dst_SE3_src,
+                then point_cloud should consist of points in frame `src`
+
+        Returns:
+            Array of shape (N, 3) representing the transformed point cloud, i.e. points in frame `dst`
+        """
+        return point_cloud @ self.rotation.T + self.translation
+
+    def transform_point_cloud(self, point_cloud: NDArrayFloat) -> NDArrayFloat:
+        """Apply the SE(3) transformation to this point cloud.
+
+        Args:
+            point_cloud: Array of shape (N, 3). If the transform represents dst_SE3_src,
+                then point_cloud should consist of points in frame `src`
+
+        Returns:
+            Array of shape (N, 3) representing the transformed point cloud, i.e. points in frame `dst`
+        """
+        return self.transform_from(point_cloud)
+
+    def inverse(self) -> SE3:
+        """Return the inverse of the current SE(3) transformation.
+
+        For example, if the current object represents target_SE3_src, we will return instead src_SE3_target.
+
+        Returns:
+            instance of SE3 class, representing inverse of SE3 transformation target_SE3_src.
+        """
+        return SE3(rotation=self.rotation.T, translation=self.rotation.T.dot(-self.translation))
+
+    def compose(self, right_SE3: SE3) -> SE3:
+        """Compose (right multiply) this class' transformation matrix T with another SE(3) instance.
+
+        Algebraic representation: chained_se3 = T * right_SE3
+
+        Args:
+            right_SE3: Another instance of SE3 class.
+
+        Returns:
+            New instance of SE3 class.
+        """
+        chained_transform_matrix: NDArrayFloat = self.transform_matrix @ right_SE3.transform_matrix
+        chained_SE3 = SE3(
+            rotation=chained_transform_matrix[:3, :3],
+            translation=chained_transform_matrix[:3, 3],
+        )
+        return chained_SE3
diff --git a/src/av2/geometry/sim2.py b/src/av2/geometry/sim2.py
new file mode 100644
index 00000000..8a8f71d4
--- /dev/null
+++ b/src/av2/geometry/sim2.py
@@ -0,0 +1,209 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Similarity(2) lie group for rigid transformations and scaling on points.
+
+References:
+    http://ethaneade.com/lie_groups.pdf
+    https://github.com/borglab/gtsam/blob/develop/gtsam/geometry/Similarity3.h
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import numbers
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+import av2.utils.io as io_utils
+from av2.utils.helpers import assert_np_array_shape
+from av2.utils.typing import NDArrayFloat
+
+
+@dataclass(frozen=True)
+class Sim2:
+    """Similarity(2) lie group object.
+
+    Args:
+        R: array of shape (2x2) representing 2d rotation matrix
+        t: array of shape (2,) representing 2d translation
+        s: scaling factor
+    """
+
+    R: NDArrayFloat
+    t: NDArrayFloat
+    s: float
+
+    def __post_init__(self) -> None:
+        """Check validity of rotation, translation, and scale arguments.
+
+        Raises:
+            ValueError: If `R` is not shape (3,3), `t` is not shape (2,),
+                 or `s` is not a numeric type.
+            ZeroDivisionError: If `s` is close or equal to zero.
+        """
+        assert_np_array_shape(self.R, (2, 2))
+        assert_np_array_shape(self.t, (2,))
+
+        if not isinstance(self.s, numbers.Number):
+            raise ValueError("Scale `s` must be a numeric type!")
+        if math.isclose(self.s, 0.0):
+            raise ZeroDivisionError("3x3 matrix formation would require division by zero")
+
+    @property
+    def theta_deg(self) -> float:
+        """Recover the rotation angle `theta` (in degrees) from the 2d rotation matrix.
+
+        Note: the first column of the rotation matrix R provides sine and cosine of theta,
+            since R is encoded as [c,-s]
+                                  [s, c]
+
+        We use the following identity: tan(theta) = s/c = (opp/hyp) / (adj/hyp) = opp/adj
+
+        Returns:
+            Rotation angle from the 2d rotation matrix.
+        """
+        c, s = self.R[0, 0], self.R[1, 0]
+        theta_rad = np.arctan2(s, c)
+        return float(np.rad2deg(theta_rad))
+
+    def __repr__(self) -> str:
+        """Return a human-readable string representation of the class."""
+        trans = np.round(self.t, 2)  # type: ignore
+        return f"Angle (deg.): {self.theta_deg:.1f}, Trans.: {trans}, Scale: {self.s:.1f}"
+
+    def __eq__(self, other: object) -> bool:
+        """Check for equality with other Sim(2) object."""
+        if not isinstance(other, Sim2):
+            return False
+
+        if not np.isclose(self.scale, other.scale):
+            return False
+
+        if not np.allclose(self.rotation, other.rotation):
+            return False
+
+        if not np.allclose(self.translation, other.translation):
+            return False
+
+        return True
+
+    @property
+    def rotation(self) -> NDArrayFloat:
+        """Return the 2x2 rotation matrix."""
+        return self.R
+
+    @property
+    def translation(self) -> NDArrayFloat:
+        """Return the (2,) translation vector."""
+        return self.t
+
+    @property
+    def scale(self) -> float:
+        """Return the scale."""
+        return self.s
+
+    @property
+    def matrix(self) -> NDArrayFloat:
+        """Calculate 3*3 matrix group equivalent."""
+        T = np.zeros((3, 3))
+        T[:2, :2] = self.R
+        T[:2, 2] = self.t
+        T[2, 2] = 1 / self.s
+        return T
+
+    def compose(self, S: Sim2) -> Sim2:
+        """Composition with another Sim2.
+
+        This can be understood via block matrix multiplication, if self is parameterized as (R1,t1,s1)
+        and if `S` is parameterized as (R2,t2,s2):
+
+        [R1  t1]   [R2  t2]   [R1 @ R2   R1@t2 + t1/s2]
+        [0 1/s1] @ [0 1/s2] = [ 0          1/(s1*s2)  ]
+
+        Args:
+            S: Similarity(2) transformation.
+
+        Returns:
+            Composed Similarity(2) transformation.
+        """
+        # fmt: off
+        return Sim2(
+            R=self.R @ S.R,
+            t=self.R @ S.t + ((1.0 / S.s) * self.t),  # type: ignore
+            s=self.s * S.s
+        )
+        # fmt: on
+
+    def inverse(self) -> Sim2:
+        """Return the inverse."""
+        Rt = self.R.T
+        sRt: NDArrayFloat = -Rt @ (self.s * self.t)
+        return Sim2(Rt, sRt, 1.0 / self.s)
+
+    def transform_from(self, points_xy: NDArrayFloat) -> NDArrayFloat:
+        """Transform point cloud from reference frame a to b.
+
+        If the points are in frame A, and our Sim(3) transform is defines as bSa, then we get points
+        back in frame B:
+            p_b = bSa * p_a
+        Action on a point p is s*(R*p+t).
+
+        Args:
+            points_xy: Nx2 array representing 2d points in frame A.
+
+        Returns:
+            Nx2 array representing 2d points in frame B.
+
+        Raises:
+            ValueError: if `points_xy` isn't in R^2.
+        """
+        if not points_xy.ndim == 2:
+            raise ValueError("Input points are not 2-dimensional.")
+        assert_np_array_shape(points_xy, (None, 2))
+        # (2,2) x (2,N) + (2,1) = (2,N) -> transpose
+        transformed_point_cloud: NDArrayFloat = (points_xy @ self.R.T) + self.t
+
+        # now scale points
+        return transformed_point_cloud * self.s
+
+    def transform_point_cloud(self, points_xy: NDArrayFloat) -> NDArrayFloat:
+        """Alias for `transform_from()`, for synchrony w/ API provided by SE(2) and SE(3) classes."""
+        return self.transform_from(points_xy)
+
+    def save_as_json(self, save_fpath: Path) -> None:
+        """Save the Sim(2) object to a JSON representation on disk.
+
+        Args:
+            save_fpath: path to where json file should be saved
+        """
+        dict_for_serialization = {
+            "R": self.rotation.flatten().tolist(),
+            "t": self.translation.flatten().tolist(),
+            "s": self.scale,
+        }
+        io_utils.save_json_dict(save_fpath, dict_for_serialization)
+
+    @classmethod
+    def from_json(cls, json_fpath: Path) -> Sim2:
+        """Generate class inst. from a JSON file containing Sim(2) parameters as flattened matrices (row-major)."""
+        with open(json_fpath, "r") as f:
+            json_data = json.load(f)
+
+        R: NDArrayFloat = np.array(json_data["R"]).reshape(2, 2)
+        t: NDArrayFloat = np.array(json_data["t"]).reshape(2)
+        s = float(json_data["s"])
+        return cls(R, t, s)
+
+    @classmethod
+    def from_matrix(cls, T: NDArrayFloat) -> Sim2:
+        """Generate class instance from a 3x3 Numpy matrix."""
+        if np.isclose(T[2, 2], 0.0):
+            raise ZeroDivisionError("Sim(2) scale calculation would lead to division by zero.")
+
+        R = T[:2, :2]
+        t = T[:2, 2]
+        s = 1 / T[2, 2]
+        return cls(R, t, s)
diff --git a/src/av2/map/README.md b/src/av2/map/README.md
new file mode 100644
index 00000000..25a3f9b4
--- /dev/null
+++ b/src/av2/map/README.md
@@ -0,0 +1,146 @@
+
+# Maps for Argoverse 2.0
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Map Counts](#map-counts)
+- [Vector Map: Lane Graph and Lane Segments](#lane-segments)
+- [Vector Map: Drivable Area](#drivable-area)
+- [Vector Map: Pedestrian Crossings](#ped-crossings)
+- [Area of Local Maps](#area-of-local-maps)
+- [Raster Maps: Ground surface height](#ground-height)
+- [Training Online Map Inference Models](#training-online-map-inference-models)
+
+## Overview
+
+In all three datasets, each scenario contains its own HD Map with 3D lane, crosswalk, and drivable area geometry — sourced from data captured in six distinct cities (Austin, Detroit, Miami, Palo Alto, Pittsburgh, and Washington D.C.).
+
+Each scenario in the three datasets described above shares the same HD map representation. Each scenario carries its own local map region. This is a departure from the original Argoverse datasets in which all scenarios were localized onto two city-scale maps. Advantages of per-scenario maps include:
+
+- More efficient queries.
+- The ability to handle map changes. A particular intersection might be observed multiple times in our datasets, and there could be changes to the lanes, crosswalks, or even ground height in between the times when the logs or sequences were captured.
+
+## Map Counts
+
+Argoverse 2.0 offers a massive number of highly diverse HD maps:
+
+- **Motion Forecasting Dataset**: ~250,000 vector maps.
+- **Sensor Dataset**: 1,000 vector maps and 1,000 ground height raster maps.
+- **LiDAR Dataset**: 20,000 vector maps.
+- **TbV Dataset**: 1,038 vector maps and 1,038 ground height raster maps.
+
+The core data structure that holds Argoverse 2.0 map data is the [`ArgoverseStaticMap`](map_api.py#280) class. Please refer to the [map tutorial notebook](../../../tutorials/map_tutorial.ipynb) for more examples of how to use the map API.
+
+<a name="lane-segments"></a>
+
+## Vector Map: Lane Graph and Lane Segments
+
+The core feature of the HD map is the lane graph, consisting of a graph G = (V, E), where V are individual lane segments. In the [supplemental material](https://openreview.net/attachment?id=vKQGe36av4k&name=supplementary_material), we enumerate and define the attributes we provide for each lane segment. Unlike Argoverse 1, we provide the actual 3D lane boundaries, instead of only centerlines. However, our API provides code to quickly infer the centerlines at any desired sampling resolution. Polylines are quantized to 1 cm resolution in the release.
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/157802162-e40098c1-8677-4c16-ac60-e9bbded6badf.png" height="300">
+</p>
+
+These vector map files are provided as JSON, e.g. `log_map_archive_00a6ffc1-6ce9-3bc3-a060-6006e9893a1a____PIT_city_31785.json`
+
+They may be loaded as follows:
+
+```python
+from av2.map.map_api import ArgoverseStaticMap
+log_map_dirpath = Path("av2") / "00a6ffc1-6ce9-3bc3-a060-6006e9893a1a" / "map"
+avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath=log_map_dirpath, build_raster=False)
+```
+
+Please refer to the [`LaneSegment`](lane_segment.py#L71) class, with the following attributes:
+
+- `id`: unique identifier for this lane segment (guaranteed to be unique only within this local map).
+- `is_intersection`: boolean value representing whether or not this lane segment lies within an intersection.
+- `lane_type`: designation of which vehicle types may legally utilize this lane for travel (see [`LaneType`](lane_segment.py#L23)).
+- `right_lane_boundary`: 3d polyline representing the right lane boundary (see [`Polyline`](map_primitives.py#L37)).
+- `left_lane_boundary`: 3d polyline representing the left lane boundary.
+- `right_mark_type`: type of painted marking found along the right lane boundary (see [`LaneMarkType`](lane_segment.py#L31)).
+- `left_mark_type`: type of painted marking found along the left lane boundary.
+- `predecessors`: unique identifiers of lane segments that are predecessors of this object.
+- `successors`: unique identifiers of lane segments that represent successor of this object. Note: this list will be empty if no successors exist.
+- `right_neighbor_id`: unique identifier of the lane segment representing this object's right neighbor.
+- `left_neighbor_id`: unique identifier of the lane segment representing this object's left neighbor.
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/157915615-42fa04ff-86f7-407e-bf3a-743e127aec0b.jpg" height="500">
+  <img src="https://user-images.githubusercontent.com/29715011/157915595-b8c4a627-49fb-4f52-b375-0aa81593eac7.jpg" height="500">
+</p>
+
+<a name="drivable-area"></a>
+
+## Vector Map: Drivable Area
+
+Instead of providing drivable area segmentation in a rasterized format, as we did in Argoverse 1, we release it in a vector format  (i.e. as 3D polygons). This offers multiple advantages, chiefly in compression - allowing us to store separate maps for tens of thousands of scenarios, while ensuring that the raster format is still easily derivable. The polygon vertices are quantized to 1 cm resolution.
+
+Please refer to the [`DrivableArea`](drivable_area.py#L17) class, with the following attributes:
+
+- `id`: unique identifier.
+- `area_boundary`: 3d vertices of polygon, representing the drivable area's boundary.
+
+<a name="ped-crossings"></a>
+
+## Vector Map: Pedestrian Crossings
+
+These entities represent crosswalks, and are provided in vector format. They are parameterized by two edges along a principal axis. Both lines should be pointing in nominally the same direction and a pedestrian is expected to move either roughly parallel to both lines or anti-parallel to both lines.
+
+Please refer to the [`PedestrianCrossing`](pedestrian_crossing.py#L17) class, with the following attributes:
+
+- `id`: unique identifier of pedestrian crossing.
+- `edge1`: 3d polyline representing one edge of the crosswalk, with 2 waypoints.
+- `edge2`: 3d polyline representing the other edge of the crosswalk, with 2 waypoints.
+
+## Area of Local Maps
+
+Each scenario’s local map includes all entities found within a 100 m dilation in l2-norm from the ego-vehicle trajectory.
+
+<a name="ground-height"></a>
+
+## Raster Maps: Ground Surface Height
+
+Only the AV 2.0 Sensor Dataset and TbV includes a dense ground surface height map. (The AV 2.0 LiDAR dataset and AV 2.0 Motion Forecasting (MF) datasets **do not** come up with raster maps, but still have sparse 3D height information on polylines).
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/29715011/157802151-1eecfb9f-1e3f-4639-a417-a245d9898b1e.png" height="300">
+</p>
+
+Ground surface height is provided for areas within a 5 m isocontour of the drivable area boundary, which we define as the region of interest (ROI). We do so because the notion of ground surface height is ill-defined for the interior of buildings and interior of densely constructed city blocks, areas where ground vehicles cannot observe due to occlusion. The raster grid is quantized to a 30 cm resolution, a higher resolution than the 1 m resolution in Argoverse 1.
+
+This data is provided as `float16` Numpy files:
+`00a6ffc1-6ce9-3bc3-a060-6006e9893a1a_ground_height_surface____PIT.npy`
+
+**Sim(2) representation**: In order to access the raster values, a Similarity(2) mapping from city coordinates to the high-resolution raster grid (which we refer to as `img` or `array`). This Similarity(2) object provided as a JSON file per log encapsulating (R,t,s), e.g.:
+
+`00a6ffc1-6ce9-3bc3-a060-6006e9893a1a___img_Sim2_city.json`
+
+Points may be transformed as follows:
+
+```python
+from av2.geometry.sim2 import Sim2
+array_Sim2_city = Sim2.from_json(json_fpath)
+points_array = array_Sim2_city.transform_from(points_city)
+```
+
+However, instead of indexing manually into the raster arrays, the API will handle this for you:
+
+```python
+avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath=log_map_dirpath, build_raster=True)
+points_z = avm.raster_ground_height_layer.get_ground_height_at_xy(points_xy)
+```
+
+## Training Online Map Inference Models
+
+Argoverse 2.0 offers new opportunities for training online map inference models, as the largest source of paired sensor data and HD maps publicly available at the time of release.
+
+However, a few Sensor 2.0 Dataset logs intentionally feature HD map changes:
+
+1. `75e8adad-50a6-3245-8726-5e612db3d165`
+2. `54bc6dbc-ebfb-3fba-b5b3-57f88b4b79ca`
+3. `af170aac-8465-3d7b-82c5-64147e94af7d`
+4. `6e106cf8-f6dd-38f6-89c8-9be7a71e7275`
+
+These logs should not be used for training online map inference models, as the real-world scene has recently been updated via constructed, leaving the map out-of-date.
diff --git a/src/av2/map/__init__.py b/src/av2/map/__init__.py
new file mode 100644
index 00000000..b33d130b
--- /dev/null
+++ b/src/av2/map/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""HD Maps subpackage."""
diff --git a/src/av2/map/drivable_area.py b/src/av2/map/drivable_area.py
new file mode 100644
index 00000000..b8a46aae
--- /dev/null
+++ b/src/av2/map/drivable_area.py
@@ -0,0 +1,40 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Class describing a drivable area polygon."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, List
+
+import numpy as np
+
+from av2.map.map_primitives import Point
+from av2.utils.typing import NDArrayFloat
+
+
+@dataclass
+class DrivableArea:
+    """Represents a single polygon, not a polyline.
+
+    Args:
+        id: unique identifier.
+        area_boundary: 3d vertices of polygon, representing the drivable area's boundary.
+    """
+
+    id: int
+    area_boundary: List[Point]
+
+    @property
+    def xyz(self) -> NDArrayFloat:
+        """Return (N,3) array representing the ordered 3d coordinates of the polygon vertices."""
+        return np.vstack([wpt.xyz for wpt in self.area_boundary])
+
+    @classmethod
+    def from_dict(cls, json_data: Dict[str, Any]) -> DrivableArea:
+        """Generate object instance from dictionary read from JSON data."""
+        point_list = [Point(x=v["x"], y=v["y"], z=v["z"]) for v in json_data["area_boundary"]]
+        # append the first vertex to the end of vertex list
+        point_list.append(point_list[0])
+
+        return cls(id=json_data["id"], area_boundary=point_list)
diff --git a/src/av2/map/lane_segment.py b/src/av2/map/lane_segment.py
new file mode 100644
index 00000000..6f654d83
--- /dev/null
+++ b/src/av2/map/lane_segment.py
@@ -0,0 +1,177 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Classes describing lane segments and their associated properties."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Dict, Final, List, Optional
+
+import av2.geometry.infinity_norm_utils as infinity_norm_utils
+import av2.geometry.interpolate as interp_utils
+import av2.geometry.polyline_utils as polyline_utils
+from av2.map.map_primitives import Polyline
+from av2.utils.typing import NDArrayFloat
+
+WPT_INFINITY_NORM_INTERP_NUM: Final[int] = 50
+
+logger = logging.getLogger(__name__)
+
+
+class LaneType(str, Enum):
+    """Describes the sorts of objects that may use the lane for travel."""
+
+    VEHICLE: str = "VEHICLE"
+    BIKE: str = "BIKE"
+    BUS: str = "BUS"
+
+
+class LaneMarkType(str, Enum):
+    """Color and pattern of a painted lane marking, located on either the left or ride side of a lane segment.
+
+    The `NONE` type indicates that lane boundary is not marked by any paint; its extent should be implicitly inferred.
+    """
+
+    DASH_SOLID_YELLOW: str = "DASH_SOLID_YELLOW"
+    DASH_SOLID_WHITE: str = "DASH_SOLID_WHITE"
+    DASHED_WHITE: str = "DASHED_WHITE"
+    DASHED_YELLOW: str = "DASHED_YELLOW"
+    DOUBLE_SOLID_YELLOW: str = "DOUBLE_SOLID_YELLOW"
+    DOUBLE_SOLID_WHITE: str = "DOUBLE_SOLID_WHITE"
+    DOUBLE_DASH_YELLOW: str = "DOUBLE_DASH_YELLOW"
+    DOUBLE_DASH_WHITE: str = "DOUBLE_DASH_WHITE"
+    SOLID_YELLOW: str = "SOLID_YELLOW"
+    SOLID_WHITE: str = "SOLID_WHITE"
+    SOLID_DASH_WHITE: str = "SOLID_DASH_WHITE"
+    SOLID_DASH_YELLOW: str = "SOLID_DASH_YELLOW"
+    SOLID_BLUE: str = "SOLID_BLUE"
+    NONE: str = "NONE"
+
+
+@dataclass
+class LocalLaneMarking:
+    """Information about a lane marking, representing either the left or right boundary of a lane segment.
+
+    Args:
+        mark_type: type of marking that represents the lane boundary, e.g. "SOLID_WHITE" or "DASHED_YELLOW".
+        src_lane_id: id of lane segment to which this lane marking belongs.
+        bound_side: string representing which side of a lane segment this marking represents, i.e. "left" or "right".
+        polyline: array of shape (N,3) representing the waypoints of the lane segment's marked boundary.
+    """
+
+    mark_type: LaneMarkType
+    src_lane_id: int
+    bound_side: str
+    polyline: NDArrayFloat
+
+
+@dataclass(frozen=False)
+class LaneSegment:
+    """Vector representation of a single lane segment within a log-specific Argoverse 2.0 map.
+
+    Args:
+        id: unique identifier for this lane segment (guaranteed to be unique only within this local map).
+        is_intersection: boolean value representing whether or not this lane segment lies within an intersection.
+        lane_type: designation of which vehicle types may legally utilize this lane for travel.
+        right_lane_boundary: 3d polyline representing the right lane boundary.
+        left_lane_boundary: 3d polyline representing the right lane boundary
+        right_mark_type: type of painted marking found along the right lane boundary.
+        left_mark_type: type of painted marking found along the left lane boundary.
+        predecessors: unique identifiers of lane segments that are predecessors of this object.
+        successors: unique identifiers of lane segments that represent successor of this object.
+            Note: this list will be empty if no successors exist.
+        right_neighbor_id: unique identifier of the lane segment representing this object's right neighbor.
+        left_neighbor_id: unique identifier of the lane segment representing this object's left neighbor.
+    """
+
+    id: int
+    is_intersection: bool
+    lane_type: LaneType
+    right_lane_boundary: Polyline
+    left_lane_boundary: Polyline
+    right_mark_type: LaneMarkType
+    left_mark_type: LaneMarkType
+    predecessors: List[int]
+    successors: List[int]
+    right_neighbor_id: Optional[int] = None
+    left_neighbor_id: Optional[int] = None
+
+    @classmethod
+    def from_dict(cls, json_data: Dict[str, Any]) -> LaneSegment:
+        """Convert JSON to a LaneSegment instance."""
+        return cls(
+            id=json_data["id"],
+            lane_type=LaneType(json_data["lane_type"]),
+            right_lane_boundary=Polyline.from_json_data(json_data["right_lane_boundary"]),
+            left_lane_boundary=Polyline.from_json_data(json_data["left_lane_boundary"]),
+            right_mark_type=LaneMarkType(json_data["right_lane_mark_type"]),
+            left_mark_type=LaneMarkType(json_data["left_lane_mark_type"]),
+            right_neighbor_id=json_data["right_neighbor_id"],
+            left_neighbor_id=json_data["left_neighbor_id"],
+            predecessors=json_data["predecessors"],
+            successors=json_data["successors"],
+            is_intersection=json_data["is_intersection"],
+        )
+
+    @property
+    def left_lane_marking(self) -> LocalLaneMarking:
+        """Retrieve the left lane marking associated with this lane segment."""
+        return LocalLaneMarking(
+            mark_type=self.left_mark_type, src_lane_id=self.id, bound_side="left", polyline=self.left_lane_boundary.xyz
+        )
+
+    @property
+    def right_lane_marking(self) -> LocalLaneMarking:
+        """Retrieve the right lane marking associated with this lane segment."""
+        return LocalLaneMarking(
+            mark_type=self.right_mark_type,
+            src_lane_id=self.id,
+            bound_side="right",
+            polyline=self.right_lane_boundary.xyz,
+        )
+
+    @property
+    def polygon_boundary(self) -> NDArrayFloat:
+        """Extract coordinates of the polygon formed by the lane segment's left and right boundaries.
+
+        Returns:
+            array of shape (N,3).
+        """
+        return polyline_utils.convert_lane_boundaries_to_polygon(
+            self.right_lane_boundary.xyz, self.left_lane_boundary.xyz
+        )
+
+    def is_within_l_infinity_norm_radius(self, query_center: NDArrayFloat, search_radius_m: float) -> bool:
+        """Whether any waypoint of lane boundaries falls within search_radius_m of query center, by l-infinity norm.
+
+        Could have very long segment, with endpoints and all waypoints outside of radius, therefore cannot check just
+        its endpoints.
+
+        Args:
+            query_center: array of shape (3,) representing 3d coordinates of query center.
+            search_radius_m: distance threshold in meters (by infinity norm) to use for search.
+
+        Returns:
+            whether the lane segment has any waypoint within search_radius meters of the query center.
+        """
+        try:
+            right_ln_bnd_interp = interp_utils.interp_arc(
+                t=WPT_INFINITY_NORM_INTERP_NUM, points=self.right_lane_boundary.xyz[:, :2]
+            )
+            left_ln_bnd_interp = interp_utils.interp_arc(
+                t=WPT_INFINITY_NORM_INTERP_NUM, points=self.left_lane_boundary.xyz[:, :2]
+            )
+        except Exception:
+            logger.exception("Interpolation failed for lane segment %d", self.id)
+            right_ln_bnd_interp = self.right_lane_boundary.xyz[:, :2]
+            left_ln_bnd_interp = self.left_lane_boundary.xyz[:, :2]
+
+        left_in_bounds = infinity_norm_utils.has_pts_in_infinity_norm_radius(
+            right_ln_bnd_interp, query_center, search_radius_m
+        )
+        right_in_bounds = infinity_norm_utils.has_pts_in_infinity_norm_radius(
+            left_ln_bnd_interp, query_center, search_radius_m
+        )
+        return left_in_bounds or right_in_bounds
diff --git a/src/av2/map/map_api.py b/src/av2/map/map_api.py
new file mode 100644
index 00000000..f0436924
--- /dev/null
+++ b/src/av2/map/map_api.py
@@ -0,0 +1,680 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""API for loading and Argoverse 2.0 maps.
+
+These include left and right lane boundaries, instead of only lane centerlines,
+as was the case in Argoverse 1.0 and 1.1.
+
+Separate map data (files) is provided for each log/scenario. This local map data represents
+map entities that fall within some distance according to l-infinity norm from the trajectory
+of the egovehicle (AV).
+"""
+
+from __future__ import annotations
+
+import copy
+import logging
+import math
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+from typing import Dict, Final, List, Optional, Tuple, Union
+
+import numpy as np
+
+import av2.geometry.interpolate as interp_utils
+import av2.utils.dilation_utils as dilation_utils
+import av2.utils.io as io_utils
+import av2.utils.raster as raster_utils
+from av2.geometry.sim2 import Sim2
+from av2.map.drivable_area import DrivableArea
+from av2.map.lane_segment import LaneSegment
+from av2.map.pedestrian_crossing import PedestrianCrossing
+from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat, NDArrayInt
+
+# 1 meter resolution is insufficient for the online-generated drivable area and ROI raster grids
+# these grids can be generated at an arbitrary resolution, from vector (polygon) objects.
+ONLINE_RASTER_RESOLUTION_M: Final[float] = 0.1  # 10 cm resolution
+ONLINE_RASTER_RESOLUTION_SCALE: Final[float] = 1 / ONLINE_RASTER_RESOLUTION_M
+
+GROUND_HEIGHT_THRESHOLD_M: Final[float] = 0.3  # 30 centimeters
+ROI_ISOCONTOUR_M: Final[float] = 5.0  # in meters
+ROI_ISOCONTOUR_GRID: Final[float] = ROI_ISOCONTOUR_M * ONLINE_RASTER_RESOLUTION_SCALE
+
+WPT_INFINITY_NORM_INTERP_NUM: Final[int] = 50
+
+
+logger = logging.getLogger(__name__)
+
+
+class RasterLayerType(str, Enum):
+    """Raster layer types."""
+
+    ROI = "ROI"
+    DRIVABLE_AREA = "DRIVABLE_AREA"
+    GROUND_HEIGHT = "GROUND_HEIGHT"
+
+
+@dataclass(frozen=True)
+class RasterMapLayer:
+    """Data sampled at points along a regular grid, and a mapping from city coordinates to grid array coordinates."""
+
+    array: Union[NDArrayByte, NDArrayFloat]
+    array_Sim2_city: Sim2
+
+    def get_raster_values_at_coords(
+        self, points_xyz: NDArrayFloat, fill_value: Union[float, int]
+    ) -> Union[NDArrayFloat, NDArrayInt]:
+        """Index into a raster grid and extract values corresponding to city coordinates.
+
+        Note: a conversion is required between city coordinates and raster grid coordinates, via Sim(2).
+
+        Args:
+            points_xyz: array of shape (N,2) or (N,3) representing coordinates in the city coordinate frame.
+            fill_value: float representing default "raster" return value for out-of-bounds queries.
+
+        Returns:
+            raster_values: array of shape (N,) representing raster values at the N query coordinates.
+        """
+        # Note: we do NOT round here, because we need to enforce scaled discretization.
+        city_coords = points_xyz[:, :2]
+
+        npyimage_coords = self.array_Sim2_city.transform_point_cloud(city_coords)
+        npyimage_coords = npyimage_coords.astype(np.int64)
+
+        # out of bounds values will default to the fill value, and will not be indexed into the array.
+        # index in at (x,y) locations, which are (y,x) in the image
+        raster_values = np.full((npyimage_coords.shape[0]), fill_value)
+        # generate boolean array indicating whether the value at each index represents a valid coordinate.
+        ind_valid_pts = (
+            (npyimage_coords[:, 1] >= 0)
+            * (npyimage_coords[:, 1] < self.array.shape[0])
+            * (npyimage_coords[:, 0] >= 0)
+            * (npyimage_coords[:, 0] < self.array.shape[1])
+        )
+        raster_values[ind_valid_pts] = self.array[npyimage_coords[ind_valid_pts, 1], npyimage_coords[ind_valid_pts, 0]]
+        return raster_values
+
+
+@dataclass(frozen=True)
+class GroundHeightLayer(RasterMapLayer):
+    """Rasterized ground height map layer.
+
+    Stores the "ground_height_matrix" and also the array_Sim2_city: Sim(2) that produces takes point in city
+    coordinates to numpy image/matrix coordinates, e.g. p_npyimage = array_Transformation_city * p_city
+    """
+
+    @classmethod
+    def from_file(cls, log_map_dirpath: Path) -> GroundHeightLayer:
+        """Load ground height values (w/ values at 30 cm resolution) from .npy file, and associated Sim(2) mapping.
+
+        Note: ground height values are stored on disk as a float16 2d-array, but cast to float32 once loaded for
+        compatibility with matplotlib.
+
+        Args:
+            log_map_dirpath: path to directory which contains map files associated with one specific log/scenario.
+
+        Returns:
+            The ground height map layer.
+
+        Raises:
+            RuntimeError: If raster ground height layer file is missing or Sim(2) mapping from city to image coordinates
+                is missing.
+        """
+        ground_height_npy_fpaths = sorted(log_map_dirpath.glob("*_ground_height_surface____*.npy"))
+        if not len(ground_height_npy_fpaths) == 1:
+            raise RuntimeError("Raster ground height layer file is missing")
+
+        Sim2_json_fpaths = sorted(log_map_dirpath.glob("*___img_Sim2_city.json"))
+        if not len(Sim2_json_fpaths) == 1:
+            raise RuntimeError("Sim(2) mapping from city to image coordinates is missing")
+
+        # load the file with rasterized values
+        ground_height_array: NDArrayFloat = np.load(ground_height_npy_fpaths[0])  # type: ignore
+
+        array_Sim2_city = Sim2.from_json(Sim2_json_fpaths[0])
+
+        return cls(array=ground_height_array.astype(np.float32), array_Sim2_city=array_Sim2_city)
+
+    def get_ground_points_boolean(self, points_xyz: NDArrayFloat) -> NDArrayBool:
+        """Check whether each 3d point is likely to be from the ground surface.
+
+        Args:
+            points_xyz: Numpy array of shape (N,3) representing 3d coordinates of N query locations.
+
+        Returns:
+            Numpy array of shape (N,) where ith entry is True if the 3d point (e.g. a LiDAR return) is likely
+                located on the ground surface.
+
+        Raises:
+            ValueError: If `points_xyz` aren't 3d.
+        """
+        if points_xyz.shape[1] != 3:
+            raise ValueError("3-dimensional points must be provided to classify them as `ground` with the map.")
+
+        ground_height_values = self.get_ground_height_at_xy(points_xyz)
+        z = points_xyz[:, 2]
+        near_ground: NDArrayBool = np.absolute(z - ground_height_values) <= GROUND_HEIGHT_THRESHOLD_M
+        underground: NDArrayBool = z < ground_height_values
+        is_ground_boolean_arr: NDArrayBool = near_ground | underground
+        return is_ground_boolean_arr
+
+    def get_rasterized_ground_height(self) -> Tuple[NDArrayFloat, Sim2]:
+        """Get ground height matrix along with Sim(2) that maps matrix coordinates to city coordinates.
+
+        Returns:
+            ground_height_matrix:
+            array_Sim2_city: Sim(2) that produces takes point in city coordinates to image coordinates, e.g.
+                    p_image = image_Transformation_city * p_city
+        """
+        ground_height_matrix: NDArrayFloat = self.array.astype(float)
+        return ground_height_matrix, self.array_Sim2_city
+
+    def get_ground_height_at_xy(self, points_xyz: NDArrayFloat) -> NDArrayFloat:
+        """Get ground height for each of the xy locations for all points {(x,y,z)} in a point cloud.
+
+        Args:
+            points_xyz: Numpy array of shape (K,2) or (K,3)
+
+        Returns:
+            Numpy array of shape (K,)
+        """
+        ground_height_values: NDArrayFloat = self.get_raster_values_at_coords(points_xyz, fill_value=np.nan).astype(
+            float
+        )
+        return ground_height_values
+
+
+@dataclass(frozen=True)
+class DrivableAreaMapLayer(RasterMapLayer):
+    """Rasterized drivable area map layer.
+
+    This provides the "drivable area" as a binary segmentation mask in the bird's eye view.
+    """
+
+    @classmethod
+    def from_vector_data(cls, drivable_areas: List[DrivableArea]) -> DrivableAreaMapLayer:
+        """Return a drivable area map from vector data.
+
+        NOTE: This function provides "drivable area" as a binary segmentation mask in the bird's eye view.
+
+        Args:
+            drivable_areas: List of drivable areas.
+
+        Returns:
+            Driveable area map layer.
+        """
+        # We compute scene boundaries on the fly, based on the vertices of all drivable area polygons.
+        # These scene boundaries are used to define the raster grid extents.
+        x_min, y_min, x_max, y_max = compute_data_bounds(drivable_areas)
+
+        # The resolution of the rasterization will affect image dimensions.
+        array_s_city = ONLINE_RASTER_RESOLUTION_SCALE
+        img_h = int((y_max - y_min + 1) * array_s_city)
+        img_w = int((x_max - x_min + 1) * array_s_city)
+
+        # scale determines the resolution of the raster DA layer.
+        array_Sim2_city = Sim2(R=np.eye(2), t=np.array([-x_min, -y_min]), s=array_s_city)
+
+        # convert vertices for each polygon from a 3d array in city coordinates, to a 2d array
+        # in image/array coordinates.
+        da_polygons_img = []
+        for da_polygon_city in drivable_areas:
+            da_polygon_img = array_Sim2_city.transform_from(da_polygon_city.xyz[:, :2])
+            da_polygon_img = np.round(da_polygon_img).astype(np.int32)  # type: ignore
+            da_polygons_img.append(da_polygon_img)
+
+        da_mask = raster_utils.get_mask_from_polygons(da_polygons_img, img_h, img_w)
+
+        return cls(array=da_mask, array_Sim2_city=array_Sim2_city)
+
+
+@dataclass(frozen=True)
+class RoiMapLayer(RasterMapLayer):
+    """Rasterized Region of Interest (RoI) map layer.
+
+    This layer provides the "region of interest" as a binary segmentation mask in the bird's eye view.
+    """
+
+    @classmethod
+    def from_drivable_area_layer(cls, drivable_area_layer: DrivableAreaMapLayer) -> RoiMapLayer:
+        """Rasterize and return 3d vector drivable area as a 2d array, and dilate it by 5 meters, to return a ROI mask.
+
+        Args:
+            drivable_area_layer: Drivable map layer.
+
+        Returns:
+            ROI Layer, containing a (M,N) matrix representing a binary segmentation for the region of interest,
+                and `array_Sim2_city`, Similarity(2) transformation that transforms point in the city coordinates to
+                2d array coordinates:
+                    p_array  = array_Sim2_city * p_city
+        """
+        # initialize ROI as zero-level isocontour of drivable area, and the dilate to 5-meter isocontour
+        roi_mat_init: NDArrayByte = copy.deepcopy(drivable_area_layer.array).astype(np.uint8)
+        roi_mask = dilation_utils.dilate_by_l2(roi_mat_init, dilation_thresh=ROI_ISOCONTOUR_GRID)
+
+        return cls(array=roi_mask, array_Sim2_city=drivable_area_layer.array_Sim2_city)
+
+
+def compute_data_bounds(drivable_areas: List[DrivableArea]) -> Tuple[int, int, int, int]:
+    """Find the minimum and maximum coordinates along the x and y axes for a set of drivable areas.
+
+    Args:
+        drivable_areas: list of drivable area objects, defined in the city coordinate frame.
+
+    Returns:
+        xmin: float representing minimum x-coordinate of any vertex of any provided drivable area.
+        ymin: float representing minimum y-coordinate, as above.
+        xmax: float representing maximum x-coordinate, as above.
+        ymax: float representing maximum y-coordinate, as above.
+    """
+    xmin = math.floor(min([da.xyz[:, 0].min() for da in drivable_areas]))
+    ymin = math.floor(min([da.xyz[:, 1].min() for da in drivable_areas]))
+    xmax = math.ceil(max([da.xyz[:, 0].max() for da in drivable_areas]))
+    ymax = math.ceil(max([da.xyz[:, 1].max() for da in drivable_areas]))
+
+    return xmin, ymin, xmax, ymax
+
+
+@dataclass
+class ArgoverseStaticMap:
+    """API to interact with a local map for a single log (within a single city).
+
+    Nodes in the lane graph are lane segments. Edges in the lane graph provided the lane segment connectivity, via
+    left and right neighbors and successors.
+
+    Lane segments are parameterized by 3d waypoints representing their left and right boundaries.
+        Note: predecessors are implicit and available by reversing the directed graph dictated by successors.
+
+    Args:
+        log_id: unique identifier for log/scenario.
+        vector_drivable_areas: drivable area polygons. Each polygon is represented by a Nx3 array of its vertices.
+            Note: the first and last polygon vertex are identical (i.e. the first index is repeated).
+        vector_lane_segments: lane segments that are local to this log/scenario. Consists of a mapping from
+            lane segment ID to vector lane segment object, parameterized in 3d.
+        vector_pedestrian_crossings: all pedestrian crossings (i.e. crosswalks) that are local to this log/scenario.
+            Note: the lookup index is simply a list, rather than a dictionary-based mapping, since pedestrian crossings
+            are not part of a larger graph.
+        raster_drivable_area_layer: 2d raster representation of drivable area segmentation.
+        raster_roi_layer: 2d raster representation of region of interest segmentation.
+        raster_ground_height_layer: not provided for Motion Forecasting-specific scenarios/logs.
+    """
+
+    # handle out-of-bounds lane segment ids with ValueError
+
+    log_id: str
+    vector_drivable_areas: Dict[int, DrivableArea]
+    vector_lane_segments: Dict[int, LaneSegment]
+    vector_pedestrian_crossings: Dict[int, PedestrianCrossing]
+    raster_drivable_area_layer: Optional[DrivableAreaMapLayer]
+    raster_roi_layer: Optional[RoiMapLayer]
+    raster_ground_height_layer: Optional[GroundHeightLayer]
+
+    @classmethod
+    def from_json(cls, static_map_path: Path) -> ArgoverseStaticMap:
+        """Instantiate an Argoverse static map object (without raster data) from a JSON file containing map data.
+
+        Args:
+            static_map_path: Path to the JSON file containing map data. The file name must match
+                the following pattern: "log_map_archive_{log_id}.json".
+
+        Returns:
+            An Argoverse HD map.
+        """
+        log_id = static_map_path.stem.split("log_map_archive_")[1]
+        vector_data = io_utils.read_json_file(static_map_path)
+
+        vector_drivable_areas = {da["id"]: DrivableArea.from_dict(da) for da in vector_data["drivable_areas"].values()}
+        vector_lane_segments = {ls["id"]: LaneSegment.from_dict(ls) for ls in vector_data["lane_segments"].values()}
+
+        if "pedestrian_crossings" not in vector_data:
+            logger.error("Missing Pedestrian crossings!")
+            vector_pedestrian_crossings = {}
+        else:
+            vector_pedestrian_crossings = {
+                pc["id"]: PedestrianCrossing.from_dict(pc) for pc in vector_data["pedestrian_crossings"].values()
+            }
+
+        return cls(
+            log_id=log_id,
+            vector_drivable_areas=vector_drivable_areas,
+            vector_lane_segments=vector_lane_segments,
+            vector_pedestrian_crossings=vector_pedestrian_crossings,
+            raster_drivable_area_layer=None,
+            raster_roi_layer=None,
+            raster_ground_height_layer=None,
+        )
+
+    @classmethod
+    def from_map_dir(cls, log_map_dirpath: Path, build_raster: bool = False) -> ArgoverseStaticMap:
+        """Instantiate an Argoverse map object from data stored within a map data directory.
+
+        Note: the ground height surface file and associated coordinate mapping is not provided for the
+        2.0 Motion Forecasting dataset, so `build_raster` defaults to False. If raster functionality is
+        desired, users should pass `build_raster` to True (e.g. for the Sensor Datasets and Map Change Datasets).
+
+        Args:
+            log_map_dirpath: Path to directory containing scenario-specific map data,
+                JSON file must follow this schema: "log_map_archive_{log_id}.json".
+            build_raster: Whether to rasterize drivable areas, compute region of interest BEV binary segmentation,
+                and to load raster ground height from disk (when available).
+
+        Returns:
+            The HD map.
+
+        Raises:
+            RuntimeError: If the vector map data JSON file is missing.
+        """
+        # Load vector map data from JSON file
+        vector_data_fnames = sorted(log_map_dirpath.glob("log_map_archive_*.json"))
+        if not len(vector_data_fnames) == 1:
+            raise RuntimeError(f"JSON file containing vector map data is missing (searched in {log_map_dirpath})")
+        vector_data_fname = vector_data_fnames[0]
+
+        vector_data_json_path = log_map_dirpath / vector_data_fname
+        static_map = cls.from_json(vector_data_json_path)
+        static_map.log_id = log_map_dirpath.parent.stem
+
+        # Avoid file I/O and polygon rasterization when not needed
+        if build_raster:
+            drivable_areas: List[DrivableArea] = list(static_map.vector_drivable_areas.values())
+            static_map.raster_drivable_area_layer = DrivableAreaMapLayer.from_vector_data(drivable_areas=drivable_areas)
+            static_map.raster_roi_layer = RoiMapLayer.from_drivable_area_layer(static_map.raster_drivable_area_layer)
+            static_map.raster_ground_height_layer = GroundHeightLayer.from_file(log_map_dirpath)
+
+        return static_map
+
+    def get_scenario_vector_drivable_areas(self) -> List[DrivableArea]:
+        """Fetch a list of polygons, whose union represents the drivable area for the log/scenario.
+
+        NOTE: this function provides drivable areas in vector, not raster, format).
+
+        Returns:
+            List of drivable area polygons.
+        """
+        return list(self.vector_drivable_areas.values())
+
+    def get_lane_segment_successor_ids(self, lane_segment_id: int) -> Optional[List[int]]:
+        """Get lane id for the lane sucessor of the specified lane_segment_id.
+
+        Args:
+            lane_segment_id: unique identifier for a lane segment within a log scenario map (within a single city).
+
+        Returns:
+            successor_ids: list of integers, representing lane segment IDs of successors. If there are no
+                successor lane segments, then the list will be empty.
+        """
+        successor_ids = self.vector_lane_segments[lane_segment_id].successors
+        return successor_ids
+
+    def get_lane_segment_left_neighbor_id(self, lane_segment_id: int) -> Optional[int]:
+        """Get id of lane segment that is the left neighbor (if any exists) to the query lane segment id.
+
+        Args:
+            lane_segment_id: unique identifier for a lane segment within a log scenario map (within a single city).
+
+        Returns:
+            integer representing id of left neighbor to the query lane segment id, or None if no such neighbor exists.
+        """
+        return self.vector_lane_segments[lane_segment_id].left_neighbor_id
+
+    def get_lane_segment_right_neighbor_id(self, lane_segment_id: int) -> Optional[int]:
+        """Get id of lane segment that is the right neighbor (if any exists) to the query lane segment id.
+
+        Args:
+            lane_segment_id: unique identifier for a lane segment within a log scenario map (within a single city).
+
+        Returns:
+            integer representing id of right neighbor to the query lane segment id, or None if no such neighbor exists.
+        """
+        return self.vector_lane_segments[lane_segment_id].right_neighbor_id
+
+    def get_scenario_lane_segment_ids(self) -> List[int]:
+        """Get ids of all lane segments that are local to this log/scenario (according to l-infinity norm).
+
+        Returns:
+            list containing ids of local lane segments
+        """
+        return list(self.vector_lane_segments.keys())
+
+    def get_lane_segment_centerline(self, lane_segment_id: int) -> NDArrayFloat:
+        """Infer a 3D centerline for any particular lane segment by forming a ladder of left and right waypoints.
+
+        Args:
+            lane_segment_id: unique identifier for a lane segment within a log scenario map (within a single city).
+
+        Returns:
+            Numpy array of shape (N,3).
+        """
+        left_ln_bound = self.vector_lane_segments[lane_segment_id].left_lane_boundary.xyz
+        right_ln_bound = self.vector_lane_segments[lane_segment_id].right_lane_boundary.xyz
+
+        lane_centerline, _ = interp_utils.compute_midpoint_line(
+            left_ln_boundary=left_ln_bound,
+            right_ln_boundary=right_ln_bound,
+            num_interp_pts=interp_utils.NUM_CENTERLINE_INTERP_PTS,
+        )
+        return lane_centerline
+
+    def get_lane_segment_polygon(self, lane_segment_id: int) -> NDArrayFloat:
+        """Return an array contained coordinates of vertices that represent the polygon's boundary.
+
+        Args:
+            lane_segment_id: unique identifier for a lane segment within a log scenario map (within a single city).
+
+        Returns:
+            Array of polygon boundary (K,3), with identical and last boundary points
+        """
+        return self.vector_lane_segments[lane_segment_id].polygon_boundary
+
+    def lane_is_in_intersection(self, lane_segment_id: int) -> bool:
+        """Check if the specified lane_segment_id falls within an intersection.
+
+        Args:
+            lane_segment_id: unique identifier for a lane segment within a log scenario map (within a single city).
+
+        Returns:
+            boolean indicating if the lane segment falls within an intersection
+        """
+        return self.vector_lane_segments[lane_segment_id].is_intersection
+
+    def get_scenario_ped_crossings(self) -> List[PedestrianCrossing]:
+        """Return a list of all pedestrian crossing objects that are local to this log/scenario (by l-infinity norm).
+
+        Returns:
+            lpcs: local pedestrian crossings
+        """
+        return list(self.vector_pedestrian_crossings.values())
+
+    def get_nearby_ped_crossings(self, query_center: NDArrayFloat, search_radius_m: float) -> List[PedestrianCrossing]:
+        """Return nearby pedestrian crossings.
+
+        Returns pedestrian crossings for which any waypoint of their boundary falls within `search_radius_m` meters
+        of query center, by l-infinity norm.
+
+        Search radius defined in l-infinity norm (could also provide an l2 norm variant).
+
+        Args:
+            query_center: Numpy array of shape (2,) representing 2d query center.
+            search_radius_m: distance threshold in meters (by infinity norm) to use for search.
+
+        Raises:
+            NotImplementedError: Always (not implemented!).
+        """
+        raise NotImplementedError("This method isn't currently supported.")
+
+    def get_scenario_lane_segments(self) -> List[LaneSegment]:
+        """Return a list of all lane segments objects that are local to this log/scenario.
+
+        Returns:
+            vls_list: lane segments local to this scenario (any waypoint within 100m by L2 distance)
+        """
+        return list(self.vector_lane_segments.values())
+
+    def get_nearby_lane_segments(self, query_center: NDArrayFloat, search_radius_m: float) -> List[LaneSegment]:
+        """Return the nearby lane segments.
+
+        Return lane segments for which any waypoint of their lane boundaries falls
+            within search_radius meters of query center, by l-infinity norm.
+
+        Args:
+            query_center: Numpy array of shape (2,) representing 2d query center.
+            search_radius_m: distance threshold in meters (by infinity norm) to use for search.
+
+        Returns:
+            ls_list: lane segments that fall within the requested search radius.
+        """
+        scenario_lane_segments = self.get_scenario_lane_segments()
+        return [
+            ls for ls in scenario_lane_segments if ls.is_within_l_infinity_norm_radius(query_center, search_radius_m)
+        ]
+
+    def remove_ground_surface(self, points_xyz: NDArrayFloat) -> NDArrayFloat:
+        """Get a collection of 3d points, snap them to the grid, perform the O(1) raster map queries.
+
+        If our z-height is within THRESHOLD of that grid's z-height, then we keep it; otherwise, discard it.
+
+        Args:
+            points_xyz: Numpy array of shape (N,3) representing 3d coordinates of N query locations.
+
+        Returns:
+            subset of original point cloud, with ground points removed
+        """
+        is_ground_boolean_arr = self.get_ground_points_boolean(points_xyz)
+        filtered_points_xyz: NDArrayFloat = points_xyz[~is_ground_boolean_arr]
+        return filtered_points_xyz
+
+    def get_ground_points_boolean(self, points_xyz: NDArrayFloat) -> NDArrayBool:
+        """Check whether each 3d point is likely to be from the ground surface.
+
+        Args:
+            points_xyz: Numpy array of shape (N,3) representing 3d coordinates of N query locations.
+
+        Returns:
+            Numpy array of shape (N,) where ith entry is True if the 3d point
+                (e.g. a LiDAR return) is likely located on the ground surface.
+
+        Raises:
+            ValueError: If `self.raster_ground_height_layer` is `None`.
+        """
+        if self.raster_ground_height_layer is None:
+            raise ValueError("Raster ground height is not loaded!")
+
+        return self.raster_ground_height_layer.get_ground_points_boolean(points_xyz)
+
+    def remove_non_drivable_area_points(self, points_xyz: NDArrayFloat) -> NDArrayFloat:
+        """Decimate the point cloud to the drivable area only.
+
+        Get a 3d point, snap it to the grid, perform the O(1) raster map query.
+
+        Args:
+            points_xyz: Numpy array of shape (N,3) representing 3d coordinates of N query locations.
+
+        Returns:
+            subset of original point cloud, returning only those points lying within the drivable area.
+        """
+        is_da_boolean_arr = self.get_raster_layer_points_boolean(points_xyz, layer_name=RasterLayerType.DRIVABLE_AREA)
+        filtered_points_xyz: NDArrayFloat = points_xyz[is_da_boolean_arr]
+        return filtered_points_xyz
+
+    def remove_non_roi_points(self, points_xyz: NDArrayFloat) -> NDArrayFloat:
+        """Decimate the point cloud to the Region of Interest (ROI) area only.
+
+        Get a 3d point, snap it to the grid, perform the O(1) raster map query.
+
+        Args:
+            points_xyz: Numpy array of shape (N,3) representing 3d coordinates of N query locations.
+
+        Returns:
+            subset of original point cloud, returning only those points lying within the ROI.
+        """
+        is_da_boolean_arr = self.get_raster_layer_points_boolean(points_xyz, layer_name=RasterLayerType.ROI)
+        filtered_points_xyz: NDArrayFloat = points_xyz[is_da_boolean_arr]
+        return filtered_points_xyz
+
+    def get_rasterized_drivable_area(self) -> Tuple[NDArrayByte, Sim2]:
+        """Get the drivable area along with Sim(2) that maps matrix coordinates to city coordinates.
+
+        Returns:
+            da_matrix: Numpy array of shape (M,N) representing binary values for drivable area,
+                or None if `build_raster=False`.
+            array_Sim2_city: Sim(2) that produces takes point in city coordinates to Numpy array coordinates, e.g.
+                    p_array = array_Transformation_city * p_city
+
+        Raises:
+            ValueError: If `self.raster_drivable_area_layer` is `None`.
+        """
+        if self.raster_drivable_area_layer is None:
+            raise ValueError("Raster drivable area is not loaded!")
+
+        raster_drivable_area_layer: NDArrayByte = self.raster_drivable_area_layer.array.astype(np.uint8)
+        return raster_drivable_area_layer, self.raster_drivable_area_layer.array_Sim2_city
+
+    def get_rasterized_roi(self) -> Tuple[NDArrayByte, Sim2]:
+        """Get the drivable area along with Sim(2) that maps matrix coordinates to city coordinates.
+
+        Returns:
+            da_matrix: Numpy array of shape (M,N) representing binary values for drivable area.
+            array_Sim2_city: Sim(2) that produces takes point in city coordinates to numpy image, e.g.
+                    p_npyimage = npyimage_Transformation_city * p_city
+
+        Raises:
+            ValueError: If `self.raster_roi_layer` is `None`.
+        """
+        if self.raster_roi_layer is None:
+            raise ValueError("Raster ROI is not loaded!")
+
+        raster_roi_layer: NDArrayByte = self.raster_roi_layer.array.astype(np.uint8)
+        return raster_roi_layer, self.raster_roi_layer.array_Sim2_city
+
+    def get_raster_layer_points_boolean(self, points_xyz: NDArrayFloat, layer_name: RasterLayerType) -> NDArrayBool:
+        """Query the binary segmentation layers (drivable area and ROI) at specific coordinates, to check values.
+
+        Args:
+            points_xyz: Numpy array of shape (N,3) representing 3d coordinates of N query locations.
+            layer_name: enum indicating layer name, for either region-of-interest or drivable area.
+
+        Returns:
+            Numpy array of shape (N,) where i'th entry is True if binary segmentation is
+                equal to 1 at the i'th point coordinate (i.e. is within the ROI, or within the drivable area,
+                depending upon `layer_name` argument).
+
+        Raises:
+            ValueError: If `self.raster_roi_layer`, `self.raster_drivable_area_layer` is `None`. Additionally,
+                if `layer_name` is not `roi` or `driveable_area`.
+        """
+        if layer_name == RasterLayerType.ROI:
+            if self.raster_roi_layer is None:
+                raise ValueError("Raster ROI is not loaded!")
+            layer_values = self.raster_roi_layer.get_raster_values_at_coords(points_xyz, fill_value=0)
+        elif layer_name == RasterLayerType.DRIVABLE_AREA:
+            if self.raster_drivable_area_layer is None:
+                raise ValueError("Raster drivable area is not loaded!")
+            layer_values = self.raster_drivable_area_layer.get_raster_values_at_coords(points_xyz, fill_value=0)
+        else:
+            raise ValueError("layer_name should be either `roi` or `drivable_area`.")
+
+        is_layer_boolean_arr: NDArrayBool = layer_values == 1.0
+        return is_layer_boolean_arr
+
+    def append_height_to_2d_city_pt_cloud(self, points_xy: NDArrayFloat) -> NDArrayFloat:
+        """Accept 2d point cloud in xy plane and returns a 3d point cloud (xyz) by querying map for ground height.
+
+        Args:
+            points_xy: Numpy array of shape (N,2) representing 2d coordinates of N query locations.
+
+        Returns:
+            Numpy array of shape (N,3) representing 3d coordinates on the ground surface at N (x,y) query locations.
+
+        Raises:
+            ValueError: If `self.raster_ground_height_layer` is `None` or input is not a set of 2d coordinates.
+        """
+        if self.raster_ground_height_layer is None:
+            raise ValueError("Raster ground height is not loaded!")
+
+        if points_xy.shape[1] != 2:
+            raise ValueError("Input query points must have shape (N,2")
+
+        points_z = self.raster_ground_height_layer.get_ground_height_at_xy(points_xy)
+        points_xyz: NDArrayFloat = np.hstack([points_xy, points_z[:, np.newaxis]])
+        return points_xyz
diff --git a/src/av2/map/map_primitives.py b/src/av2/map/map_primitives.py
new file mode 100644
index 00000000..c9f86a8e
--- /dev/null
+++ b/src/av2/map/map_primitives.py
@@ -0,0 +1,83 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Primitive types for vector maps (point, polyline)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, List
+
+import numpy as np
+
+from av2.utils.typing import NDArrayFloat
+
+
+@dataclass
+class Point:
+    """Represents a single 3-d point."""
+
+    x: float
+    y: float
+    z: float
+
+    @property
+    def xyz(self) -> NDArrayFloat:
+        """Return (3,) vector."""
+        return np.array([self.x, self.y, self.z])
+
+    def __eq__(self, other: object) -> bool:
+        """Check for equality with another Point object."""
+        if not isinstance(other, Point):
+            return False
+
+        return all([self.x == other.x, self.y == other.y, self.z == other.z])
+
+
+@dataclass
+class Polyline:
+    """Represents an ordered point set with consecutive adjacency."""
+
+    waypoints: List[Point]
+
+    @property
+    def xyz(self) -> NDArrayFloat:
+        """Return (N,3) array representing ordered waypoint coordinates."""
+        return np.vstack([wpt.xyz for wpt in self.waypoints])
+
+    @classmethod
+    def from_json_data(cls, json_data: List[Dict[str, float]]) -> Polyline:
+        """Generate object instance from list of dictionaries, read from JSON data.
+
+        Args:
+            json_data: JSON formatted data.
+
+        Returns:
+            `Polyline` object.
+        """
+        return cls(waypoints=[Point(x=v["x"], y=v["y"], z=v["z"]) for v in json_data])
+
+    @classmethod
+    def from_array(cls, array: NDArrayFloat) -> Polyline:
+        """Generate object instance from a (N,3) Numpy array.
+
+        Args:
+            array: array of shape (N,3) representing N ordered three-dimensional points.
+
+        Returns:
+            Polyline object.
+        """
+        return cls(waypoints=[Point(*pt.tolist()) for pt in array])
+
+    def __eq__(self, other: object) -> bool:
+        """Check for equality with another Polyline object."""
+        if not isinstance(other, Polyline):
+            return False
+
+        if len(self.waypoints) != len(other.waypoints):
+            return False
+
+        return all([wpt == wpt_ for wpt, wpt_ in zip(self.waypoints, other.waypoints)])
+
+    def __len__(self) -> int:
+        """Return the number of waypoints in the polyline."""
+        return len(self.waypoints)
diff --git a/src/av2/map/pedestrian_crossing.py b/src/av2/map/pedestrian_crossing.py
new file mode 100644
index 00000000..9eabbada
--- /dev/null
+++ b/src/av2/map/pedestrian_crossing.py
@@ -0,0 +1,66 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Class representing a pedestrian crossing (crosswalk)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, Tuple
+
+import numpy as np
+
+from av2.map.map_primitives import Polyline
+from av2.utils.typing import NDArrayFloat
+
+
+@dataclass
+class PedestrianCrossing:
+    """Represents a pedestrian crossing (i.e. crosswalk) as two edges along its principal axis.
+
+    Both lines should be pointing in nominally the same direction and a pedestrian is expected to
+    move either roughly parallel to both lines or anti-parallel to both lines.
+
+    Args:
+        id: unique identifier of this pedestrian crossing.
+        edge1: 3d polyline representing one edge of the crosswalk, with 2 waypoints.
+        edge2: 3d polyline representing the other edge of the crosswalk, with 2 waypoints.
+    """
+
+    id: int
+    edge1: Polyline
+    edge2: Polyline
+
+    def get_edges_2d(self) -> Tuple[NDArrayFloat, NDArrayFloat]:
+        """Retrieve the two principal edges of the crosswalk, in 2d.
+
+        Returns:
+            edge1: array of shape (2,2), a 2d polyline representing one edge of the crosswalk, with 2 waypoints.
+            edge2: array of shape (2,2), a 2d polyline representing the other edge of the crosswalk, with 2 waypoints.
+        """
+        return (self.edge1.xyz[:, :2], self.edge2.xyz[:, :2])
+
+    def __eq__(self, other: object) -> bool:
+        """Check if two pedestrian crossing objects are equal, up to a tolerance."""
+        if not isinstance(other, PedestrianCrossing):
+            return False
+
+        return np.allclose(self.edge1.xyz, other.edge1.xyz) and np.allclose(self.edge2.xyz, other.edge2.xyz)
+
+    @classmethod
+    def from_dict(cls, json_data: Dict[str, Any]) -> PedestrianCrossing:
+        """Generate a PedestrianCrossing object from a dictionary read from JSON data."""
+        edge1 = Polyline.from_json_data(json_data["edge1"])
+        edge2 = Polyline.from_json_data(json_data["edge2"])
+
+        return PedestrianCrossing(id=json_data["id"], edge1=edge1, edge2=edge2)
+
+    @property
+    def polygon(self) -> NDArrayFloat:
+        """Return the vertices of the polygon representing the pedestrian crossing.
+
+        Returns:
+            array of shape (N,3) representing vertices. The first and last vertex that are provided are identical.
+        """
+        v0, v1 = self.edge1.xyz
+        v2, v3 = self.edge2.xyz
+        return np.array([v0, v1, v3, v2, v0])
diff --git a/src/av2/rendering/__init__.py b/src/av2/rendering/__init__.py
new file mode 100644
index 00000000..04a6b6c1
--- /dev/null
+++ b/src/av2/rendering/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Rendering subpackage."""
diff --git a/src/av2/rendering/color.py b/src/av2/rendering/color.py
new file mode 100644
index 00000000..665ef7c0
--- /dev/null
+++ b/src/av2/rendering/color.py
@@ -0,0 +1,47 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Colormap related constants and functions."""
+
+from typing import Final, Sequence, Tuple
+
+import numpy as np
+from matplotlib.colors import LinearSegmentedColormap
+
+from av2.utils.typing import NDArrayFloat
+
+RED_HEX: Final[str] = "#df0101"
+GREEN_HEX: Final[str] = "#31b404"
+
+RED_RGB: Final[Tuple[int, int, int]] = (255, 0, 0)
+RED_BGR: Final[Tuple[int, int, int]] = RED_RGB[::-1]
+
+BLUE_RGB: Final[Tuple[int, int, int]] = (0, 0, 255)
+BLUE_BGR: Final[Tuple[int, int, int]] = BLUE_RGB[::-1]
+
+HANDICAP_BLUE_RGB: Final[Tuple[int, int, int]] = (42, 130, 193)
+HANDICAP_BLUE_BGR: Final[Tuple[int, int, int]] = HANDICAP_BLUE_RGB[::-1]
+
+WHITE_RGB: Final[Tuple[int, int, int]] = (255, 255, 255)
+WHITE_BGR: Final[Tuple[int, int, int]] = WHITE_RGB[::-1]
+
+GRAY_BGR: Final[Tuple[int, int, int]] = (168, 168, 168)
+DARK_GRAY_BGR: Final[Tuple[int, int, int]] = (100, 100, 100)
+
+TRAFFIC_YELLOW1_RGB: Final[Tuple[int, int, int]] = (250, 210, 1)
+TRAFFIC_YELLOW1_BGR: Final[Tuple[int, int, int]] = TRAFFIC_YELLOW1_RGB[::-1]
+
+
+def create_colormap(color_list: Sequence[str], n_colors: int) -> NDArrayFloat:
+    """Create hex colorscale to interpolate between requested colors.
+
+    Args:
+        color_list: list of requested colors, in hex format.
+        n_colors: number of colors in the colormap.
+
+    Returns:
+        array of shape (n_colors, 3) representing a list of RGB colors in [0,1]
+    """
+    cmap = LinearSegmentedColormap.from_list(name="dummy_name", colors=color_list)
+    colorscale: NDArrayFloat = np.array([cmap(k * 1 / n_colors) for k in range(n_colors)])
+    # ignore the 4th alpha channel
+    return colorscale[:, :3]  # type: ignore
diff --git a/src/av2/rendering/map.py b/src/av2/rendering/map.py
new file mode 100644
index 00000000..49452f3e
--- /dev/null
+++ b/src/av2/rendering/map.py
@@ -0,0 +1,267 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Render map in ego-view.
+
+Red represents an implicit lane marking (with no actual paint, merely inferred by road users).
+"""
+
+import copy
+from dataclasses import dataclass
+from typing import Final, Optional, Tuple, Union
+
+import cv2
+import numpy as np
+
+import av2.geometry.interpolate as interp_utils
+import av2.geometry.polyline_utils as polyline_utils
+import av2.utils.depth_map_utils as depth_map_utils
+from av2.geometry.camera.pinhole_camera import PinholeCamera
+from av2.geometry.se3 import SE3
+from av2.map.lane_segment import LaneMarkType, LaneSegment
+from av2.map.map_api import ArgoverseStaticMap
+from av2.rendering.color import HANDICAP_BLUE_BGR, RED_BGR, TRAFFIC_YELLOW1_BGR, WHITE_BGR
+from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat, NDArrayInt
+
+# apply uniform dashes. in reality, there is often a roughly 2:1 ratio between empty space and dashes.
+DASH_INTERVAL_M: Final[float] = 1.0  # every 1 meter
+
+# number of sampled waypoints per polyline.
+N_INTERP_PTS: Final[int] = 1000
+
+
+@dataclass(frozen=True)
+class EgoViewMapRenderer:
+    """Rendering engine for map entities in the ego-view, via perspective projection w/ a pinhole camera.
+
+    Args:
+        depth_map: float array of shape (H,W) representing a depth map.
+        city_SE3_ego: AV pose at a single timestamp.
+        pinhole_cam: parameters of a pinhole camera.
+        avm: map, with vector and raster elements, for querying ground height at arbitrary locations.
+    """
+
+    depth_map: Optional[NDArrayFloat]
+    city_SE3_ego: SE3
+    pinhole_cam: PinholeCamera
+    avm: ArgoverseStaticMap
+
+    @property
+    def ego_SE3_city(self) -> SE3:
+        """Retrieve transformation that brings map elements in city frame into the egovehicle frame."""
+        return self.city_SE3_ego.inverse()
+
+    def render_lane_boundary_egoview(
+        self,
+        img_bgr: NDArrayByte,
+        lane_segment: LaneSegment,
+        side: str,
+        line_width_px: float,
+    ) -> NDArrayByte:
+        """Draw left or right lane boundary (only one is rendered here).
+
+        Double lines are to be understood from the inside out.  e.g. DASHED_SOLID means that the dashed line is adjacent
+        to the lane carrying the property and the solid line is adjacent to the neighbor lane.
+
+        Args:
+            img_bgr: array of shape (H,W,3) representing BGR image (canvas), to render on.
+            lane_segment: vector lane segment object.
+            side: lane side to render. should be "left" or "right"
+            line_width_px: thickness (in pixels) to use when rendering the polyline.
+
+        Returns:
+            array of shape (H,W,3) representing BGR image (canvas), with lane boundary rendered on it.
+
+        Raises:
+            ValueError: If `mark_type` is unknown.
+        """
+        if side == "right":
+            polyline = lane_segment.right_lane_boundary.xyz
+            mark_type = lane_segment.right_mark_type
+        else:
+            polyline = lane_segment.left_lane_boundary.xyz
+            mark_type = lane_segment.left_mark_type
+
+        # interpolation needs to happen before rounded to integer coordinates
+        polyline_city_frame = interp_utils.interp_arc(t=N_INTERP_PTS, points=polyline)
+
+        if "WHITE" in mark_type:
+            bound_color = WHITE_BGR
+        elif "YELLOW" in mark_type:
+            bound_color = TRAFFIC_YELLOW1_BGR
+        elif "BLUE" in mark_type:
+            bound_color = HANDICAP_BLUE_BGR
+        else:
+            bound_color = RED_BGR
+
+        if ("DOUBLE" in mark_type) or ("SOLID_DASH" in mark_type) or ("DASH_SOLID" in mark_type):
+            left, right = polyline_utils.get_double_polylines(polyline_city_frame, width_scaling_factor=0.1)
+
+        if mark_type in [
+            LaneMarkType.SOLID_WHITE,
+            LaneMarkType.SOLID_YELLOW,
+            LaneMarkType.SOLID_BLUE,
+            LaneMarkType.NONE,
+        ]:
+            self.render_polyline_egoview(polyline_city_frame, img_bgr, bound_color, thickness_px=line_width_px)
+
+        elif mark_type in [LaneMarkType.DOUBLE_DASH_YELLOW, LaneMarkType.DOUBLE_DASH_WHITE]:
+            self.draw_dashed_polyline_egoview(
+                left, img_bgr, bound_color, thickness_px=line_width_px, dash_interval_m=DASH_INTERVAL_M
+            )
+            self.draw_dashed_polyline_egoview(
+                right, img_bgr, bound_color, thickness_px=line_width_px, dash_interval_m=DASH_INTERVAL_M
+            )
+
+        elif mark_type in [LaneMarkType.DOUBLE_SOLID_YELLOW, LaneMarkType.DOUBLE_SOLID_WHITE]:
+            self.render_polyline_egoview(left, img_bgr, bound_color, thickness_px=line_width_px)
+            self.render_polyline_egoview(right, img_bgr, bound_color, thickness_px=line_width_px)
+
+        elif mark_type in [LaneMarkType.DASHED_WHITE, LaneMarkType.DASHED_YELLOW]:
+            self.draw_dashed_polyline_egoview(
+                polyline_city_frame,
+                img_bgr,
+                bound_color,
+                thickness_px=line_width_px,
+                dash_interval_m=DASH_INTERVAL_M,
+            )
+
+        elif (mark_type == LaneMarkType.SOLID_DASH_YELLOW and side == "right") or (
+            mark_type == LaneMarkType.DASH_SOLID_YELLOW and side == "left"
+        ):
+            self.render_polyline_egoview(left, img_bgr, bound_color, thickness_px=line_width_px)
+            self.draw_dashed_polyline_egoview(
+                right, img_bgr, bound_color, thickness_px=line_width_px, dash_interval_m=DASH_INTERVAL_M
+            )
+
+        elif (mark_type == LaneMarkType.SOLID_DASH_YELLOW and side == "left") or (
+            mark_type == LaneMarkType.DASH_SOLID_YELLOW and side == "right"
+        ):
+            self.draw_dashed_polyline_egoview(
+                left, img_bgr, bound_color, thickness_px=line_width_px, dash_interval_m=DASH_INTERVAL_M
+            )
+            self.render_polyline_egoview(right, img_bgr, bound_color, thickness_px=line_width_px)
+
+        else:
+            raise ValueError(f"Unknown marking type {mark_type}")
+
+        return img_bgr
+
+    def draw_dashed_polyline_egoview(
+        self,
+        polyline: NDArrayFloat,
+        img_bgr: NDArrayByte,
+        bound_color: Tuple[int, int, int],
+        thickness_px: float,
+        dash_interval_m: float,
+        dash_frequency: int = 3,
+    ) -> None:
+        """Draw a dashed polyline in the ego-view.
+
+        Generate 1 dash every N meters, with equal dash-non-dash spacing.
+        Ignoring residual at ends, since assume lanes quite long.
+
+        Args:
+            polyline: Array of shape (K, 2) representing the coordinates of each line segment
+            img_bgr: Array of shape (M, N, 3), representing a 3-channel BGR image, passed by reference.
+            bound_color: Tuple of shape (3,) with a BGR format color
+            thickness_px: thickness (in pixels) to use when rendering the polyline.
+            dash_interval_m: length of one dash, in meters.
+            dash_frequency: for each dash_interval_m, we will discretize the length into n sections.
+                1 of n sections will contain a marked dash, and the other (n-1) spaces will be empty (non-marked).
+        """
+        interp_polyline, num_waypts = polyline_utils.interp_polyline_by_fixed_waypt_interval(polyline, dash_interval_m)
+        for i in range(num_waypts - 1):
+
+            # every other segment is a gap
+            # (first the next dash interval is a line, and then the dash interval is empty, ...)
+            if (i % dash_frequency) != 0:
+                continue
+
+            dashed_segment_arr = interp_polyline[i : i + 2]
+            self.render_polyline_egoview(
+                polyline_city_frame=dashed_segment_arr,
+                img_bgr=img_bgr,
+                bound_color=bound_color,
+                thickness_px=thickness_px,
+            )
+
+    def render_polyline_egoview(
+        self,
+        polyline_city_frame: NDArrayFloat,
+        img_bgr: NDArrayByte,
+        bound_color: Tuple[int, int, int],
+        thickness_px: float,
+    ) -> None:
+        """Rasterize a polygon onto an image canvas, as if seen from a particular camera.
+
+        Args:
+            polyline_city_frame: array of shape (N,3) representing a polyline in city coordinates
+                (e.g. crosswalk boundary or lane segment boundary).
+            img_bgr: array of shape (H,W,3) representing a BGR image to write to (the canvas).
+            bound_color: tuple of BGR intensities to use as color for rendering the lane boundary (i.e. polyline).
+            thickness_px: thickness (in pixels) to use when rendering the polyline.
+        """
+        # must use interpolated, because otherwise points may lie behind camera, etc, cannot draw
+        interp_polyline_city = interp_utils.interp_arc(t=N_INTERP_PTS, points=polyline_city_frame)
+        polyline_ego_frame = self.ego_SE3_city.transform_point_cloud(interp_polyline_city)
+
+        # no need to motion compensate, since these are points originally from the city frame.
+        uv, points_cam, is_valid_points = self.pinhole_cam.project_ego_to_img(polyline_ego_frame)
+        if is_valid_points.sum() == 0:
+            return
+
+        u: NDArrayInt = np.round(uv[:, 0][is_valid_points]).astype(np.int32)  # type: ignore
+        v: NDArrayInt = np.round(uv[:, 1][is_valid_points]).astype(np.int32)  # type: ignore
+
+        lane_z = points_cam[:, 2][is_valid_points]
+
+        if self.depth_map is not None:
+            allowed_noise = depth_map_utils.compute_allowed_noise_per_point(points_cam[is_valid_points])
+            not_occluded = lane_z <= self.depth_map[v, u] + allowed_noise
+        else:
+            not_occluded = np.ones(lane_z.shape, dtype=bool)
+
+        line_segments_arr: NDArrayInt = np.hstack([u.reshape(-1, 1), v.reshape(-1, 1)])
+
+        # draw non-occluded ones
+        draw_visible_polyline_segments_cv2(
+            copy.deepcopy(line_segments_arr),
+            valid_pts_bool=not_occluded,
+            image=img_bgr,
+            color=bound_color,
+            thickness_px=thickness_px,
+        )
+
+
+def draw_visible_polyline_segments_cv2(
+    line_segments_arr: Union[NDArrayFloat, NDArrayInt],
+    valid_pts_bool: NDArrayBool,
+    image: NDArrayByte,
+    color: Tuple[int, int, int],
+    thickness_px: float = 1,
+) -> None:
+    """Draw a polyline onto an image using given line segments.
+
+    Args:
+        line_segments_arr: Array of shape (K, 2) representing the coordinates of each line segment.
+            Vertices may be out of bounds (outside the image borders) and the line segment will be interpolated.
+        valid_pts_bool: Array of shape (K,) representing which polyline coordinates are valid for rendering.
+            For example, if the coordinate is occluded, a user might specify that it is invalid.
+            Line segments touching an invalid vertex will not be rendered.
+        image: Array of shape (H, W, 3), representing a 3-channel BGR image
+        color: Tuple of shape (3,) with a BGR format color
+        thickness_px: thickness (in pixels) to use when rendering the polyline.
+    """
+    line_segments_arr_int: NDArrayInt = np.round(line_segments_arr).astype(int)  # type: ignore
+    for i in range(len(line_segments_arr_int) - 1):
+
+        if (not valid_pts_bool[i]) or (not valid_pts_bool[i + 1]):
+            continue
+
+        x1 = line_segments_arr_int[i][0]
+        y1 = line_segments_arr_int[i][1]
+        x2 = line_segments_arr_int[i + 1][0]
+        y2 = line_segments_arr_int[i + 1][1]
+
+        # Use anti-aliasing (AA) for curves
+        image = cv2.line(image, pt1=(x1, y1), pt2=(x2, y2), color=color, thickness=thickness_px, lineType=cv2.LINE_AA)
diff --git a/src/av2/rendering/ops/__init__.py b/src/av2/rendering/ops/__init__.py
new file mode 100644
index 00000000..6e97c555
--- /dev/null
+++ b/src/av2/rendering/ops/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Subpackage for fast rendering operations."""
diff --git a/src/av2/rendering/ops/draw.py b/src/av2/rendering/ops/draw.py
new file mode 100644
index 00000000..11c845d6
--- /dev/null
+++ b/src/av2/rendering/ops/draw.py
@@ -0,0 +1,197 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Kernels for fast drawing operations."""
+
+from math import exp
+from typing import Final, Tuple
+
+import numba as nb
+import numpy as np
+
+from av2.utils.constants import NAN
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt, NDArrayNumber
+
+UINT8_MAX: Final[np.uint8] = np.iinfo(np.uint8).max
+UINT8_BITS: Final[np.uint8] = np.log2(UINT8_MAX + 1).astype(np.uint8)
+
+
+@nb.njit
+def integer_linear_interpolation(x: np.uint8, y: np.uint8, alpha: np.uint16, beta: np.uint16) -> np.uint8:
+    """Approximate floating point linear interpolation.
+
+    This function approximates the following:
+        x * alpha + y * beta,
+        s.t. alpha + beta = 1, alpha >=0, beta >= 0.
+
+    Args:
+        x: 8-bit unsigned integer value to be weighted by alpha.
+        y: 8-bit unsigned integer value to be weighted by beta.
+        alpha: 16-bit unsigned integer value in the interval [0, 256].
+        beta: 16-bit unsigned integer value in the interval [0, 256].
+
+    NOTE: Alpha and beta are 16 bit integers to properly interpolate
+    values at beta=0. Unsigned 16-bit integers can express 2**16 - 1
+    values; _HOWEVER_, this function expects alpha and beta to be within
+    the interval [0, 256].
+
+    Returns:
+        The linear interpolant.
+    """
+    # Cast to np.uint8 since python bit-shift operator returns a signed integer.
+    linear_interpolant = np.uint8((x * alpha + y * beta) >> UINT8_BITS)
+    return linear_interpolant
+
+
+@nb.njit
+def alpha_blend_kernel(fg: NDArrayByte, bg: NDArrayByte, alpha: np.uint8) -> Tuple[np.uint8, np.uint8, np.uint8]:
+    """Fast integer alpha blending.
+
+    Reference: https://stackoverflow.com/a/12016968
+
+    Args:
+        fg: (3,) Foreground RGB or BGR pixel intensities between [0, 255].
+        bg: (3,) Background RGB or BGR pixel intensities between [0, 255].
+        alpha: Alpha blending coefficient between [0, 255].
+
+    Returns:
+        (3,) The blended pixel.
+    """
+    r1, g1, b1 = fg[0], fg[1], fg[2]
+    r2, g2, b2 = bg[0], bg[1], bg[2]
+
+    alpha_uint16 = np.uint16(alpha + 1)
+    beta_uint16 = np.uint16(UINT8_MAX + 1 - alpha)
+
+    # Approximate floating point linear interpolation.
+    r = integer_linear_interpolation(r1, r2, alpha_uint16, beta_uint16)
+    g = integer_linear_interpolation(g1, g2, alpha_uint16, beta_uint16)
+    b = integer_linear_interpolation(b1, b2, alpha_uint16, beta_uint16)
+    return r, g, b
+
+
+@nb.njit
+def gaussian_kernel(x: float, mu: float, sigma: float) -> float:
+    """Return a univariate Gaussian kernel.
+
+    Args:
+        x: Value to evaluate the Gaussian.
+        mu: Mean parameter of the Gaussian.
+        sigma: Width of the Gaussian.
+
+    Returns:
+        The Gaussian kernel, N(mu,sigma^2), evaluated at x.
+    """
+    return exp(-0.5 * (x - mu) ** 2 / (sigma**2))
+
+
+@nb.njit
+def draw_points_kernel(
+    img: NDArrayByte,
+    points_uv: NDArrayInt,
+    colors: NDArrayByte,
+    diameter: int = 1,
+    alpha: float = 1.0,
+    with_anti_alias: bool = False,
+    sigma: float = 1.0,
+) -> NDArrayByte:
+    """Draw points onto an image canvas.
+
+    NOTE: Anti-aliasing will apply a Gaussian kernel at each uv -- parameterized
+            as N(uv,sigma^2).
+
+    Args:
+        img: (H,W,3) Image canvas.
+        points_uv: (N,2) Points (u,v) to be drawn.
+        colors: (N,3) BGR colors to be drawn.
+        diameter: Diameter of a drawn point.
+        alpha: Coefficient for alpha blending.
+        with_anti_alias: Boolean flag to enable anti-aliasing.
+        sigma: Gaussian width for anti-aliasing.
+
+    Returns:
+        (M,N,3) Image with points overlaid.
+    """
+    H = img.shape[0]
+    W = img.shape[1]
+    N = len(points_uv)
+
+    left_radius = diameter // 2
+    right_radius = diameter - left_radius
+    for i in nb.prange(N):
+        u = points_uv[i, 0]
+        v = points_uv[i, 1]
+        for j in nb.prange(-left_radius, right_radius):
+            for k in nb.prange(-left_radius, right_radius):
+                vj = v + j
+                uk = u + k
+                if (vj >= 0 and uk >= 0) and (vj < H and uk < W):
+                    alpha_vj_uk = alpha
+                    if with_anti_alias:
+                        alpha_vj_uk *= gaussian_kernel(vj, v, sigma) * gaussian_kernel(uk, u, sigma)
+                    alpha_vj_uk *= float(UINT8_MAX)
+                    alpha_vj_uk_uint8 = np.uint8(alpha_vj_uk)
+                    blend = alpha_blend_kernel(colors[i], img[vj, uk], alpha=alpha_vj_uk_uint8)
+                    img[vj, uk, 0] = blend[0]
+                    img[vj, uk, 1] = blend[1]
+                    img[vj, uk, 2] = blend[2]
+    return img
+
+
+@nb.njit(nogil=True)
+def clip_line_frustum(p1: NDArrayNumber, p2: NDArrayNumber, planes: NDArrayFloat) -> NDArrayFloat:
+    """Iterate over the frustum planes and intersect them with the segment.
+
+    We exploit the fact that in a camera frustum, all plane normals point inside the frustum volume.
+    NOTE: See section "Line-Plane Intersection" for technical details at:
+        http://geomalgorithms.com/a05-_intersect-1.html
+    NOTE: A similar function, written in C, can be found in the Blender source code at:
+        https://fossies.org/dox/blender-2.79b/math__geom_8c_source.html
+
+    Args:
+        p1: 3D vector defining a point to constrain a line segment.
+        p2: 3D vector defining a point to constrain a line segment.
+        planes: Array of shape (5,4), representing the 5 clipping planes for a frustum. Each plane is a 4-tuple
+            representing the equation of a plane, e.g. (a, b, c, d) in ax + by + cz = d.
+
+    Returns:
+        (2,3) The array of clipped points. Points are NAN if the line segment
+            does not lie within the frustum.
+    """
+    dp = np.subtract(p2, p1)
+
+    p1_fac = 0.0
+    p2_fac = 1.0
+    pts: NDArrayFloat = np.full((2, 3), fill_value=NAN, dtype=p1.dtype)
+    for i, _ in enumerate(planes):
+        p = planes[i]
+        div = p[:3].dot(dp)
+
+        # check if line vector and plane normal are perpendicular
+        # if perpendicular, line and plane are parallel
+        if div != 0.0:
+            # if not perpendicular, find intersection
+            # distance we travel along the ray from p1 to p2
+            t = -(p[:3].dot(p1) + p[3])
+            if div > 0.0:  # clip p1 lower bounds
+                if t >= div:
+                    return pts
+                if t > 0.0:
+                    fac = t / div
+                    if fac > p1_fac:
+                        p1_fac = fac
+                        if p1_fac > p2_fac:
+                            # intersection occurs outside of segment
+                            return pts
+            elif div < 0.0:  # clip p2 upper bounds
+                if t > 0.0:
+                    return pts
+                if t > div:
+                    fac = t / div
+                    if fac < p2_fac:
+                        p2_fac = fac
+                        if p1_fac > p2_fac:
+                            return pts
+
+    pts[0] = p1 + (dp * p1_fac)
+    pts[1] = p1 + (dp * p2_fac)
+    return pts
diff --git a/src/av2/rendering/rasterize.py b/src/av2/rendering/rasterize.py
new file mode 100644
index 00000000..746ba82b
--- /dev/null
+++ b/src/av2/rendering/rasterize.py
@@ -0,0 +1,156 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Raster visualization tools."""
+from typing import Dict, Final, Tuple
+
+import numpy as np
+
+from av2.geometry.geometry import crop_points
+from av2.rendering.ops.draw import draw_points_kernel
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt, NDArrayNumber
+
+GREEN_RGB: Final[NDArrayByte] = np.array([0, 255, 0], np.uint8)
+ELECTRIC_LIME_RGB: Final[NDArrayByte] = np.array([192, 255, 0], np.uint8)
+
+AV2_CATEGORY_CMAP: Final[Dict[str, NDArrayByte]] = {
+    "REGULAR_VEHICLE": GREEN_RGB,
+    "PEDESTRIAN": ELECTRIC_LIME_RGB,
+}
+
+
+def align_points_center(points: NDArrayNumber) -> NDArrayFloat:
+    """Align grid coordinates to the center of the grid cells.
+
+    Reference: https://bartwronski.com/2021/02/15/bilinear-down-upsampling-pixel-grids-and-that-half-pixel-offset/
+
+    Args:
+        points: Sequence of points.
+
+    Returns:
+        The aligned points.
+    """
+    aligned_points: NDArrayFloat = np.add(points, 0.5)
+    return aligned_points
+
+
+def xyz_to_bev(
+    xyz: NDArrayFloat,
+    voxel_resolution: Tuple[float, float, float],
+    grid_size_m: Tuple[float, float, float],
+    cmap: NDArrayFloat,
+) -> NDArrayByte:
+    """Convert a set of points in Cartesian space to a bird's-eye-view image.
+
+    Args:
+        xyz: (N,3) List of points in R^3.
+        voxel_resolution: (3,) Number of voxel bins in the (x,y,z) axes.
+        grid_size_m: (3,) Size of the grid in the (x,y,z) axes.
+        cmap: RGB colormap.
+
+    Returns:
+        (H,W,3) RGB image representing a BEV projection onto the xy plane.
+    """
+    cmap /= 255.0
+
+    # If only xyz are provided, then assume intensity is 1.0.
+    # Otherwise, use the provided intensity.
+    if xyz.shape[-1] == 3:
+        intensity: NDArrayByte = np.ones_like(xyz.shape[0], np.uint8)
+    else:
+        intensity = xyz[..., -1].copy()
+
+    # Grab the Cartesian coordinates (xyz).
+    cart = xyz[..., :-1].copy()
+
+    # Move the origin to the center of the image.
+    cart += np.divide(grid_size_m, 2)
+
+    # Scale the Cartesian coordinates by the voxel resolution.
+    indices: NDArrayInt = (cart / voxel_resolution).astype(int)
+
+    # Compute the voxel grid size.
+    voxel_grid_size_m = (
+        int(grid_size_m[0] / voxel_resolution[0]),
+        int(grid_size_m[1] / voxel_resolution[1]),
+        int(grid_size_m[2] / voxel_resolution[2]),
+    )
+
+    # Crop point cloud to the region-of-interest.
+    lower_bound_inclusive = (0.0, 0.0, 0.0)
+    indices_cropped, grid_boundary_reduction = crop_points(
+        indices, lower_bound_inclusive=lower_bound_inclusive, upper_bound_exclusive=grid_size_m
+    )
+
+    # Filter the indices and intensity values.
+    cmap = cmap[grid_boundary_reduction]
+    intensity = intensity[grid_boundary_reduction]
+
+    # Create the raster image.
+    im_dims = (voxel_grid_size_m[0] + 1, voxel_grid_size_m[1] + 1, cmap.shape[1])
+    img: NDArrayByte = np.zeros(im_dims, dtype=np.uint8)
+
+    # Construct uv coordinates.
+    uv = indices_cropped[:, :2]
+
+    npoints = len(indices_cropped)
+    for i in range(npoints):
+        u = uv[i, 0]
+        v = uv[i, 1]
+
+        img[u, v, :3] = cmap[i]
+        img[u, v, 3:4] += intensity[i]
+
+    # Normalize the intensity.
+    img[..., -1] = img[..., -1] / img[..., -1].max()
+
+    # Gamma correction.
+    img[..., -1] = np.power(img[..., -1], 0.05)
+
+    # Scale RGB by intensity.
+    img[..., :3] *= img[..., -1:]
+
+    # Map RGB in [0, 1] -> [0, 255].
+    img[..., :3] = img[..., :3] * 255.0
+    im_rgb: NDArrayByte = img[..., :3]
+    return im_rgb
+
+
+def draw_points_xy_in_img(
+    img: NDArrayByte,
+    points_xy: NDArrayInt,
+    colors: NDArrayByte,
+    diameter: int = 1,
+    alpha: float = 1.0,
+    with_anti_alias: bool = False,
+    sigma: float = 1.0,
+) -> NDArrayByte:
+    """Draw a set of points over an image.
+
+    Args:
+        img: (H,W,3) Image canvas.
+        points_xy: (N,2) Points (x,y) to be drawn.
+        colors: (N,3) BGR colors to be drawn.
+        diameter: Diameter of a drawn point.
+        alpha: Coefficient for alpha blending.
+        with_anti_alias: Boolean flag to enable anti-aliasing.
+        sigma: Gaussian width for anti-aliasing.
+
+    Returns:
+        The (H,W,3) image with points overlaid.
+
+    Raises:
+        ValueError: If `points_xy` is not integer valued.
+    """
+    if not np.issubdtype(points_xy.dtype, np.integer):
+        raise ValueError("Circle centers must be integer-valued coordinates.")
+
+    img = draw_points_kernel(
+        img=img,
+        points_uv=points_xy,
+        colors=colors,
+        diameter=diameter,
+        alpha=alpha,
+        with_anti_alias=with_anti_alias,
+        sigma=sigma,
+    )
+    return img
diff --git a/src/av2/rendering/vector.py b/src/av2/rendering/vector.py
new file mode 100644
index 00000000..8ea7edac
--- /dev/null
+++ b/src/av2/rendering/vector.py
@@ -0,0 +1,136 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utilities for vector graphics rendering."""
+
+from enum import Enum
+from typing import Optional, Tuple, Union
+
+import cv2
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.path import Path as MPath
+
+from av2.geometry.camera.pinhole_camera import PinholeCamera
+from av2.rendering.color import BLUE_BGR
+from av2.rendering.ops.draw import clip_line_frustum
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt, NDArrayNumber
+
+
+def draw_polygon_mpl(
+    ax: plt.Axes,
+    polygon: NDArrayFloat,
+    color: Union[Tuple[float, float, float], str],
+    linewidth: Optional[float] = None,
+) -> None:
+    """Draw a polygon's boundary.
+
+    The polygon's first and last point must be the same (repeated).
+
+    Args:
+        ax: Matplotlib axes instance to draw on
+        polygon: Array of shape (N, 2) or (N, 3)
+        color: Tuple of shape (3,) representing the RGB color or a single character 3-tuple, e.g. 'b'
+        linewidth: Width of the lines.
+    """
+    if linewidth is None:
+        ax.plot(polygon[:, 0], polygon[:, 1], color=color)
+    else:
+        ax.plot(polygon[:, 0], polygon[:, 1], color=color, linewidth=linewidth)
+
+
+def plot_polygon_patch_mpl(
+    polygon_pts: NDArrayFloat,
+    ax: plt.Axes,
+    color: Union[Tuple[float, float, float], str] = "y",
+    alpha: float = 0.3,
+    zorder: int = 1,
+) -> None:
+    """Plot a lane segment polyon using matplotlib's PathPatch object.
+
+    Reference:
+    See Descartes (https://github.com/benjimin/descartes/blob/master/descartes/patch.py)
+    and Matplotlib: https://matplotlib.org/stable/gallery/shapes_and_collections/path_patch.html
+
+    Args:
+        polygon_pts: Array of shape (N, 2) representing the points of the polygon
+        ax: Matplotlib axes.
+        color: Tuple of shape (3,) representing the RGB color or a single character 3-tuple, e.g. 'b'.
+        alpha: the opacity of the lane segment.
+        zorder: Ordering of the plot overlays.
+    """
+    n, _ = polygon_pts.shape
+    codes = np.ones(n, dtype=MPath.code_type) * MPath.LINETO
+    codes[0] = MPath.MOVETO
+
+    vertices = polygon_pts[:, :2]
+    mpath = MPath(vertices, codes)
+    patch = mpatches.PathPatch(mpath, facecolor=color, edgecolor=color, alpha=alpha, zorder=zorder)
+    ax.add_patch(patch)
+
+
+def draw_line_in_img(
+    img: NDArrayByte,
+    p1: NDArrayNumber,
+    p2: NDArrayNumber,
+    color: Tuple[int, int, int] = BLUE_BGR,
+    thickness: int = 3,
+    line_type: Enum = cv2.LINE_AA,
+) -> NDArrayByte:
+    """Draw a line on an image.
+
+    Args:
+        img: (H,W,3) 3-channel image.
+        p1: (2,) Starting point of the line segment.
+        p2: (2,) Ending point of the line segment.
+        color: 3-channel color corresponding to the channel order of the input image.
+        thickness: Thickness of the line to be rendered.
+        line_type: OpenCV line style.
+
+    Returns:
+        The image with a line drawn.
+    """
+    img_line: NDArrayByte = cv2.line(
+        img,
+        p1,
+        p2,
+        color=color,
+        thickness=thickness,
+        lineType=line_type,
+    )
+    return img_line
+
+
+def draw_line_frustum(
+    img: NDArrayByte,
+    p1: NDArrayNumber,
+    p2: NDArrayNumber,
+    cam_model: PinholeCamera,
+    color: Tuple[int, int, int],
+    thickness: int = 3,
+    line_type: Enum = cv2.LINE_AA,
+) -> NDArrayByte:
+    """Draw a line inside of the camera frustum.
+
+    Args:
+        img: (H,W,3) 3-channel image.
+        p1: (3,) Starting point of the line segment.
+        p2: (3,) Ending points of the line segment.
+        cam_model: Camera model that contains intrinsics and the size of the pixel coordinate frame.
+        color: 3-channel color corresponding to the channel order of the input image.
+        thickness: Thickness of the line to be rendered.
+        line_type: OpenCV line style.
+
+    Returns:
+        The image with a line clipped and drawn inside the frustum (if it's visible).
+    """
+    clipped_points = clip_line_frustum(p1, p2, cam_model.frustum_planes())
+    intersects_with_frustum = not bool(np.isnan(clipped_points).any())
+    if intersects_with_frustum:
+        # After clipping the line segment to the view frustum in the camera coordinate frame, we obtain
+        # the final set of line segment vertices, and then project these into the image.
+        uv, _, _ = cam_model.project_cam_to_img(clipped_points)
+        uv_int: NDArrayInt = np.round(uv).astype(int)  # type: ignore
+        p1, p2 = uv_int[0], uv_int[1]
+        img = draw_line_in_img(img, p1, p2, color=color, thickness=thickness, line_type=line_type)
+    return img
diff --git a/src/av2/rendering/video.py b/src/av2/rendering/video.py
new file mode 100644
index 00000000..48e964aa
--- /dev/null
+++ b/src/av2/rendering/video.py
@@ -0,0 +1,137 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Rendering tools for video visualizations."""
+
+from pathlib import Path
+from typing import Dict, Final, Union
+
+import av
+import cv2
+import numpy as np
+import pandas as pd
+
+from av2.utils.typing import NDArrayByte
+
+FFMPEG_OPTIONS: Final[Dict[str, str]] = {"crf": "27"}
+
+
+def tile_cameras(named_sensors: Dict[str, Union[NDArrayByte, pd.DataFrame]]) -> NDArrayByte:
+    """Combine ring cameras into a tiled image.
+
+    NOTE: Images are expected in BGR ordering.
+
+    Layout:
+
+        ##########################################################
+        # ring_front_left # ring_front_center # ring_front_right #
+        ##########################################################
+        # ring_side_left  #                   #  ring_side_right #
+        ##########################################################
+        ############ ring_rear_left # ring_rear_right ############
+        ##########################################################
+
+    Args:
+        named_sensors: Dictionary of camera names to the (width, height, 3) images.
+
+    Returns:
+        Tiled image.
+    """
+    landscape_width = 2048
+    landscape_height = 1550
+
+    height = landscape_height + landscape_height + landscape_height
+    width = landscape_width + landscape_height + landscape_width
+    tiled_im_bgr: NDArrayByte = np.zeros((height, width, 3), dtype=np.uint8)
+
+    ring_rear_left = named_sensors["ring_rear_left"]
+    ring_side_left = named_sensors["ring_side_left"]
+    ring_front_center = named_sensors["ring_front_center"]
+    ring_front_left = named_sensors["ring_front_left"]
+    ring_front_right = named_sensors["ring_front_right"]
+    ring_side_right = named_sensors["ring_side_right"]
+    ring_rear_right = named_sensors["ring_rear_right"]
+
+    tiled_im_bgr[:landscape_height, :landscape_width] = ring_front_left
+    tiled_im_bgr[:landscape_width, landscape_width : landscape_width + landscape_height] = ring_front_center
+    tiled_im_bgr[:landscape_height, landscape_width + landscape_height :] = ring_front_right
+
+    tiled_im_bgr[landscape_height:3100, :landscape_width] = ring_side_left
+    tiled_im_bgr[landscape_height:3100, landscape_width + landscape_height :] = ring_side_right
+
+    start = (width - 4096) // 2
+    tiled_im_bgr[3100:4650, start : start + landscape_width] = np.fliplr(ring_rear_left)  # type: ignore
+    tiled_im_bgr[3100:4650, start + landscape_width : start + 4096] = np.fliplr(ring_rear_right)  # type: ignore
+    tiled_im_rgb: NDArrayByte = cv2.cvtColor(tiled_im_bgr, cv2.COLOR_BGR2RGB)
+    return tiled_im_rgb
+
+
+def write_video(
+    video: NDArrayByte,
+    dst: Path,
+    codec: str = "libx264",
+    fps: int = 10,
+    crf: int = 27,
+    preset: str = "veryfast",
+) -> None:
+    """Use the FFMPEG Python bindings to encode a video from a sequence of RGB frames.
+
+    Reference: https://github.com/PyAV-Org/PyAV
+
+    Args:
+        video: (N,H,W,3) array representing N RGB frames of identical dimensions.
+        dst: path to save folder.
+        codec: the name of a codec.
+        fps: the frame rate for video.
+        crf: constant rate factor (CRF) parameter of video, controlling the quality.
+            Lower values would result in better quality, at the expense of higher file sizes.
+            For x264, the valid Constant Rate Factor (crf) range is 0-51.
+        preset: file encoding speed. Options range from "ultrafast", ..., "fast", ..., "medium", ..., "slow", ...
+            Higher compression efficiency often translates to slower video encoding speed, at file write time.
+    """
+    _, H, W, _ = video.shape
+
+    # crop, if the height or width is odd (avoid "height not divisible by 2" error)
+    if H % 2 != 0 or W % 2 != 0:
+        video = crop_video_to_even_dims(video)
+        _, H, W, _ = video.shape
+
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    with av.open(str(dst), "w") as output:
+        stream = output.add_stream(codec, fps)
+        stream.width = W
+        stream.height = H
+        stream.options = {
+            "crf": str(crf),
+            "hwaccel": "auto",
+            "movflags": "+faststart",
+            "preset": preset,
+            "profile:v": "main",
+            "tag": "hvc1",
+        }
+        for _, img in enumerate(video):
+            frame = av.VideoFrame.from_ndarray(img)
+            output.mux(stream.encode(frame))
+        output.mux(stream.encode(None))
+
+
+def crop_video_to_even_dims(video: NDArrayByte) -> NDArrayByte:
+    """Crop a video tensor (4d nd-array) along the height and width dimensions to assure even dimensions.
+
+    Note: typical "pad" or "crop" filters do not function properly with pypi AV's stream configuration options.
+
+    Args:
+        video: (N,H1,W1,3) array representing N RGB frames of identical dimensions, where H1 and W1 may be odd.
+
+    Returns:
+        (N,H2,W2,3) array representing N RGB frames of identical dimensions, where H2 and W2 are even.
+            The crop is performed on the far right column and/or bottom row of each frame.
+    """
+    _, H1, W1, _ = video.shape
+    height_crop_sz = H1 % 2
+    width_crop_sz = W1 % 2
+
+    H2 = H1 - height_crop_sz
+    W2 = W1 - width_crop_sz
+
+    cropped_video: NDArrayByte = video[:, :H2, :W2, :]
+    return cropped_video
diff --git a/src/av2/structures/__init__.py b/src/av2/structures/__init__.py
new file mode 100644
index 00000000..b0e05f1a
--- /dev/null
+++ b/src/av2/structures/__init__.py
@@ -0,0 +1,3 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Common structures for manipulating sensor data."""
diff --git a/src/av2/structures/cuboid.py b/src/av2/structures/cuboid.py
new file mode 100644
index 00000000..0dd6f867
--- /dev/null
+++ b/src/av2/structures/cuboid.py
@@ -0,0 +1,382 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Structure which represents ground truth annotations in R^3."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from functools import cached_property
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+from av2.geometry import geometry as geometry_utils
+from av2.geometry.camera.pinhole_camera import PinholeCamera
+from av2.geometry.geometry import compute_interior_points_mask, quat_to_mat
+from av2.geometry.se3 import SE3
+from av2.rendering.color import BLUE_BGR, TRAFFIC_YELLOW1_BGR
+from av2.rendering.vector import draw_line_frustum
+from av2.utils.io import read_feather
+from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat
+
+
+@dataclass
+class Cuboid:
+    """Models a cuboid annotated in a single lidar sweep.
+
+    Reference: https://en.wikipedia.org/wiki/Cuboid
+
+    Args:
+        dst_SE3_object: Pose of the object in the destination reference frame. Translation component is in meters.
+        length_m: Object extent along the x-axis in meters.
+        width_m: Object extent along the y-axis in meters.
+        height_m: Object extent along the z-axis in meters.
+        category: Object category.
+        timestamp_ns: Vehicle nanosecond timestamp. Corresponds 1-to-1 with a lidar sweep.
+    """
+
+    dst_SE3_object: SE3
+    length_m: float
+    width_m: float
+    height_m: float
+    category: Enum
+    timestamp_ns: int
+
+    @property
+    def xyz_center_m(self) -> NDArrayFloat:
+        """Cartesian coordinate center (x,y,z) in the destination reference frame."""
+        return self.dst_SE3_object.translation
+
+    @property
+    def dims_lwh_m(self) -> NDArrayFloat:
+        """Object extents (length,width,height) along the (x,y,z) axes respectively in meters."""
+        dims_lwh: NDArrayFloat = np.stack([self.length_m, self.width_m, self.height_m])
+        return dims_lwh
+
+    @cached_property
+    def vertices_m(self) -> NDArrayFloat:
+        r"""Return the cuboid vertices in the destination reference frame.
+
+            5------4
+            |\\    |\\
+            | \\   | \\
+            6--\\--7  \\
+            \\  \\  \\ \\
+        l    \\  1-------0    h
+         e    \\ ||   \\ ||   e
+          n    \\||    \\||   i
+           g    \\2------3    g
+            t      width.     h
+             h.               t.
+
+        Returns:
+            (8,3) array of cuboid vertices.
+        """
+        unit_vertices_obj_xyz_m: NDArrayFloat = np.array(
+            [
+                [+1, +1, +1],  # 0
+                [+1, -1, +1],  # 1
+                [+1, -1, -1],  # 2
+                [+1, +1, -1],  # 3
+                [-1, +1, +1],  # 4
+                [-1, -1, +1],  # 5
+                [-1, -1, -1],  # 6
+                [-1, +1, -1],  # 7
+            ],
+        )
+        dims_lwh_m = self.dims_lwh_m
+
+        # Transform unit polygons.
+        vertices_obj_xyz_m: NDArrayFloat = (dims_lwh_m / 2.0) * unit_vertices_obj_xyz_m
+        vertices_dst_xyz_m = self.dst_SE3_object.transform_point_cloud(vertices_obj_xyz_m)
+
+        # Finally, return the polygons.
+        return vertices_dst_xyz_m
+
+    def compute_interior_points(self, points_xyz_m: NDArrayFloat) -> Tuple[NDArrayFloat, NDArrayBool]:
+        """Given a query point cloud, filter to points interior to the cuboid, and provide mask.
+
+        Note: comparison is to cuboid vertices in the destination reference frame.
+
+        Args:
+            points_xyz_m: (N,3) Points to filter.
+
+        Returns:
+            The interior points and the boolean array indicating which points are interior.
+        """
+        vertices_dst_xyz_m = self.vertices_m
+        is_interior = compute_interior_points_mask(points_xyz_m, vertices_dst_xyz_m)
+        return points_xyz_m[is_interior], is_interior
+
+    def transform(self, target_SE3_dst: SE3) -> Cuboid:
+        """Apply an SE(3) transformation to the cuboid.
+
+        Mathematically written as:
+            cuboid_target = target_SE3_dst * cuboid_dst
+
+        Args:
+            target_SE3_dst: Transformation from the destination to the target reference frame.
+
+        Returns:
+            Transformed cuboid.
+        """
+        target_SE3_object = target_SE3_dst.compose(self.dst_SE3_object)
+        return Cuboid(
+            dst_SE3_object=target_SE3_object,
+            length_m=self.length_m,
+            width_m=self.width_m,
+            height_m=self.height_m,
+            category=self.category,
+            timestamp_ns=self.timestamp_ns,
+        )
+
+    @classmethod
+    def from_series(cls, data: pd.Series) -> Cuboid:
+        """Convert a Pandas series to a Cuboid.
+
+        Args:
+            data: Row from a Pandas DataFrame.
+
+        Returns:
+            Constructed cuboid.
+        """
+        translation = data.loc[["tx_m", "ty_m", "tz_m"]].to_numpy()
+        quat_wxyz = data.loc[["qw", "qx", "qy", "qz"]].to_numpy()
+        length_m = data["length_m"]
+        width_m = data["width_m"]
+        height_m = data["height_m"]
+
+        rotation = geometry_utils.quat_to_mat(quat_wxyz)
+        ego_SE3_object = SE3(rotation=rotation, translation=translation)
+        category = data["category"]
+        timestamp_ns = data["timestamp_ns"]
+        return cls(
+            dst_SE3_object=ego_SE3_object,
+            length_m=length_m,
+            width_m=width_m,
+            height_m=height_m,
+            category=category,
+            timestamp_ns=timestamp_ns,
+        )
+
+
+@dataclass
+class CuboidList:
+    """Models a list of cuboids annotated in a single lidar sweep.
+
+    Args:
+        cuboids: List of `Cuboid` objects.
+    """
+
+    cuboids: List[Cuboid]
+
+    @property
+    def xyz_center_m(self) -> NDArrayFloat:
+        """Cartesian coordinate centers (x,y,z) in the destination reference frame."""
+        center_xyz_m: NDArrayFloat = np.stack([cuboid.dst_SE3_object.translation for cuboid in self.cuboids])
+        return center_xyz_m
+
+    @property
+    def dims_lwh_m(self) -> NDArrayFloat:
+        """Object extents (length,width,height) along the (x,y,z) axes respectively in meters."""
+        dims_lwh: NDArrayFloat = np.stack([cuboid.dims_lwh_m for cuboid in self.cuboids])
+        return dims_lwh
+
+    @cached_property
+    def vertices_m(self) -> NDArrayFloat:
+        r"""Return the cuboid vertices in the destination reference frame.
+        
+            5------4
+            |\\    |\\
+            | \\   | \\
+            6--\\--7  \\
+            \\  \\  \\ \\
+        l    \\  1-------0    h
+         e    \\ ||   \\ ||   e
+          n    \\||    \\||   i
+           g    \\2------3    g
+            t      width.     h
+             h.               t.
+
+        Returns:
+            (N,8,3) array of cuboid vertices.
+        """
+        vertices_m: NDArrayFloat = np.stack([cuboid.vertices_m for cuboid in self.cuboids])
+        return vertices_m
+
+    @property
+    def categories(self) -> List[str]:
+        """Return the object category names."""
+        return [str(cuboid.category) for cuboid in self.cuboids]
+
+    def __len__(self) -> int:
+        """Return the number of cuboids."""
+        return len(self.cuboids)
+
+    def __getitem__(self, idx: int) -> Cuboid:
+        """Return the cuboid at index `idx`.
+
+        Args:
+            idx: index of cuboid to return from the CuboidList.
+
+        Returns:
+            Cuboid object present at index `idx`.
+
+        Raises:
+            IndexError: if index is invalid (i.e. out of bounds).
+        """
+        if idx < 0 or idx >= len(self):
+            raise IndexError(f"Attempted to access cuboid {idx}, but only indices [0,{len(self)-1}] are valid")
+        return self.cuboids[idx]
+
+    def transform(self, target_SE3_dst: SE3) -> CuboidList:
+        """Apply an SE(3) transformation to each cuboid in the list.
+
+        Mathematically written as:
+            cuboid_target = target_SE3_dst * cuboid_dst
+
+        Args:
+            target_SE3_dst: Transformation from the destination to the target reference frame.
+
+        Returns:
+            Transformed cuboids.
+        """
+        transformed_cuboids: List[Cuboid] = []
+        for cuboid in self.cuboids:
+            target_SE3_object = target_SE3_dst.compose(cuboid.dst_SE3_object)
+            transformed_cuboid = Cuboid(
+                dst_SE3_object=target_SE3_object,
+                length_m=cuboid.length_m,
+                width_m=cuboid.width_m,
+                height_m=cuboid.height_m,
+                category=cuboid.category,
+                timestamp_ns=cuboid.timestamp_ns,
+            )
+            transformed_cuboids.append(transformed_cuboid)
+        return CuboidList(cuboids=transformed_cuboids)
+
+    def project_to_cam(
+        self,
+        img: NDArrayByte,
+        cam_model: PinholeCamera,
+        city_SE3_ego_cam_t: Optional[SE3] = None,
+        city_SE3_ego_lidar_t: Optional[SE3] = None,
+    ) -> NDArrayByte:
+        """Project the cuboids to the camera by clipping cuboid line segments against the camera view frustum.
+
+        NOTE: The front face of each cuboid is drawn in yellow, and all other line segments are drawn in blue.
+
+        Args:
+            img: (H,W,3) BGR image.
+            cam_model: Camera model class.
+            city_SE3_ego_cam_t: City egovehicle pose at the camera timestamp.
+            city_SE3_ego_lidar_t: City egovehicle pose at the lidar timestamp.
+
+        Returns:
+            (H,W,3) BGR image with projected cuboids overlaid.
+        """
+        # Return original image if no annotations exist.
+        if len(self) == 0:
+            return img
+
+        #  Number of cuboids, number of vertices, number of dimensions in a vertex.
+        cuboids_vertices_ego = self.vertices_m
+        N, V, D = cuboids_vertices_ego.shape
+
+        # Collapse first dimension to allow for vectorization.
+        cuboids_vertices_ego = cuboids_vertices_ego.reshape(-1, D)
+        if city_SE3_ego_cam_t is not None and city_SE3_ego_lidar_t is not None:
+            _, cuboids_vertices_cam, _ = cam_model.project_ego_to_img_motion_compensated(
+                cuboids_vertices_ego, city_SE3_ego_cam_t=city_SE3_ego_cam_t, city_SE3_ego_lidar_t=city_SE3_ego_lidar_t
+            )
+        else:
+            _, cuboids_vertices_cam, _ = cam_model.project_ego_to_img(cuboids_vertices_ego)
+
+        cuboids_vertices_cam = cuboids_vertices_cam[:, :3].reshape(N, V, D)  # Unravel collapsed dimension.
+
+        # Compute depth of each cuboid center (mean of the cuboid's vertices).
+        cuboid_centers = cuboids_vertices_cam.mean(axis=1)
+        z_buffer = cuboid_centers[..., -1]
+
+        # Sort by z-order to respect visibility in the scene.
+        # i.e, closer objects cuboids should be drawn on top of farther objects.
+        z_orders: NDArrayFloat = np.argsort(-z_buffer)
+
+        cuboids_vertices_cam = cuboids_vertices_cam[z_orders]
+        front_face_indices = [0, 1, 2, 3, 0]
+        back_face_indices = [4, 5, 6, 7, 4]
+        line_segment_indices_list = [[0, 4], [1, 5], [2, 6], [3, 7]]
+
+        # Iterate over the vertices for each cuboid in the cuboid list.
+        # Draw 6 cuboid faces in the inner loop.
+        for i, cuboid_vertices_cam in enumerate(cuboids_vertices_cam):
+            cuboid_front_face = cuboid_vertices_cam[front_face_indices]
+            cuboid_back_face = cuboid_vertices_cam[back_face_indices]
+
+            # Iterate over the edges of the faces.
+            num_line_segments = len(cuboid_front_face) - 1
+            for i in range(num_line_segments):
+                draw_line_frustum(
+                    img,
+                    cuboid_front_face[i],
+                    cuboid_front_face[i + 1],
+                    cam_model=cam_model,
+                    color=TRAFFIC_YELLOW1_BGR,
+                )
+                draw_line_frustum(
+                    img,
+                    cuboid_back_face[i],
+                    cuboid_back_face[i + 1],
+                    cam_model=cam_model,
+                    color=BLUE_BGR,
+                )
+
+            # Draw the line segments that connect the front and back faces.
+            for line_segment_indices in line_segment_indices_list:
+                line_segment = cuboid_vertices_cam[line_segment_indices]
+                draw_line_frustum(
+                    img,
+                    line_segment[0],
+                    line_segment[1],
+                    cam_model=cam_model,
+                    color=BLUE_BGR,
+                )
+        return img
+
+    @classmethod
+    def from_feather(cls, annotations_feather_path: Path) -> CuboidList:
+        """Read annotations from a feather file.
+
+        Args:
+            annotations_feather_path: Feather file path.
+
+        Returns:
+            Constructed cuboids.
+        """
+        data = read_feather(annotations_feather_path)
+
+        rotation = quat_to_mat(data.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy())
+        translation_m = data.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy()
+        length_m = data.loc[:, "length_m"].to_numpy()
+        width_m = data.loc[:, "width_m"].to_numpy()
+        height_m = data.loc[:, "height_m"].to_numpy()
+        category = data.loc[:, "category"].to_numpy()
+        timestamp_ns = data.loc[:, "timestamp_ns"].to_numpy()
+        N = len(data)
+
+        cuboid_list: List[Cuboid] = []
+        for i in range(N):
+            ego_SE3_object = SE3(rotation=rotation[i], translation=translation_m[i])
+            cuboid = Cuboid(
+                dst_SE3_object=ego_SE3_object,
+                length_m=length_m[i],
+                width_m=width_m[i],
+                height_m=height_m[i],
+                category=category[i],
+                timestamp_ns=timestamp_ns[i],
+            )
+            cuboid_list.append(cuboid)
+        return cls(cuboids=cuboid_list)
diff --git a/src/av2/structures/ndgrid.py b/src/av2/structures/ndgrid.py
new file mode 100644
index 00000000..0bab67d9
--- /dev/null
+++ b/src/av2/structures/ndgrid.py
@@ -0,0 +1,157 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""N-dimensional grid."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import cached_property
+from typing import Tuple
+
+import numpy as np
+
+from av2.geometry.geometry import crop_points
+from av2.rendering.color import GRAY_BGR
+from av2.rendering.rasterize import align_points_center, draw_points_xy_in_img
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt, NDArrayNumber
+
+
+@dataclass(frozen=True)
+class NDGrid:
+    """Models an N-dimensional grid.
+
+    Args:
+        min_range_m: (N,) Minimum coordinates (in meters).
+        max_range_m: (N,) Maximum coordinates (in meters).
+        resolution_m_per_cell: (N,) Ratio of meters to cell in each dimension.
+    """
+
+    min_range_m: Tuple[float, ...]
+    max_range_m: Tuple[float, ...]
+    resolution_m_per_cell: Tuple[float, ...]
+
+    def __post_init__(self) -> None:
+        """Check validity of variables.
+
+        Raises:
+            ValueError: If the minimum range is greater than the maximum range (in any dimension) or
+                the resolution is not positive.
+        """
+        if not all(x < y for x, y in zip(self.min_range_m, self.max_range_m)):
+            raise ValueError("All minimum ranges must be less than their corresponding max ranges!")
+        if not all(x > 0 for x in self.resolution_m_per_cell):
+            raise ValueError("Resolution per cell must be positive!")
+
+    @cached_property
+    def dims(self) -> Tuple[int, ...]:
+        """Size of the grid _after_ bucketing."""
+        range_m: NDArrayFloat = np.array(self.range_m)
+        dims = self.scale_and_quantize_points(range_m)
+        return tuple(dims.tolist())
+
+    @cached_property
+    def range_m(self) -> Tuple[float, ...]:
+        """Size of the grid _before_ bucketing."""
+        range_m = np.subtract(self.max_range_m, self.min_range_m)
+        return tuple(range_m.tolist())
+
+    def scale_points(self, points: NDArrayNumber) -> NDArrayNumber:
+        """Scale points by the (1/`resolution_m_per_cell`).
+
+        Args:
+            points: (N,D) list of points.
+
+        Returns:
+            (N,D) list of scaled points.
+        """
+        scaled_points: NDArrayNumber = np.divide(points, self.resolution_m_per_cell)
+        return scaled_points
+
+    def quantize_points(self, points: NDArrayNumber) -> NDArrayInt:
+        """Quantize the points to integer coordinates.
+
+        Args:
+            points: (N,D) Array of points.
+
+        Returns:
+            (N,D) The array of quantized points.
+        """
+        # Add half-bucket offset.
+        centered_points = align_points_center(points)
+        quantized_points: NDArrayInt = np.floor(centered_points).astype(int)
+        return quantized_points
+
+    def scale_and_quantize_points(self, points: NDArrayNumber) -> NDArrayInt:
+        """Scale and quantize the points.
+
+        Args:
+            points: (N,D) Array of points.
+
+        Returns:
+            (N,D) The array of quantized points.
+        """
+        scaled_points = self.scale_points(points)
+        quantized_points: NDArrayInt = self.quantize_points(scaled_points)
+        return quantized_points
+
+    def transform_to_grid_coordinates(self, points_m: NDArrayFloat) -> NDArrayInt:
+        """Transform points to grid coordinates (in meters).
+
+        Args:
+            points_m: (N,D) list of points.
+
+        Returns:
+            (N,D) list of quantized grid coordinates.
+        """
+        offset_m = np.abs(self.min_range_m)
+
+        # points_m * (# grid cells / resolution_m_per_cell) = points_grid
+        quantized_points_grid = self.scale_and_quantize_points(points_m + offset_m)
+        return quantized_points_grid
+
+
+@dataclass(frozen=True)
+class BEVGrid(NDGrid):
+    """Models an 2-dimensional grid in the Bird's-eye view.
+
+    Args:
+        min_range_m: (2,) Minimum coordinates in the (x,y) axes (in meters).
+        max_range_m: (2,) Minimum coordinates in the (x,y) axes (in meters).
+        resolution_m_per_cell: (2,) Bucket size in the (x,y) axes (in meters).
+    """
+
+    def points_to_bev_img(
+        self, points: NDArrayFloat, color: Tuple[int, int, int] = GRAY_BGR, diameter: int = 2
+    ) -> NDArrayByte:
+        """Convert a set of points in Cartesian space to a bird's-eye-view image.
+
+        Args:
+            points: (N,D) List of points in R^D.
+            color: RGB color.
+            diameter: Point diameter for the drawn points.
+
+        Returns:
+            (H,W,3) RGB image representing a BEV projection onto the xy plane.
+
+        Raises:
+            ValueError: If points are less than 2-dimensional.
+        """
+        D = points.shape[-1]
+        if D < 2:
+            raise ValueError("Points must be at least 2d!")
+
+        points_xy = points[..., :2].copy()  # Prevent modifying input.
+        indices = self.transform_to_grid_coordinates(points_xy)
+        indices_int, _ = crop_points(indices, lower_bound_inclusive=(0.0, 0.0), upper_bound_exclusive=self.dims)
+
+        # Construct uv coordinates.
+        H, W = (self.dims[0], self.dims[1])
+        uv = indices_int[..., :2]
+
+        C = len(color)
+        shape = (H, W, C)
+        img: NDArrayByte = np.zeros(shape, dtype=np.uint8)
+
+        colors: NDArrayByte = np.array([color for _ in range(len(points_xy))], dtype=np.uint8)
+        img = draw_points_xy_in_img(img, uv, colors, diameter=diameter)
+        return img
diff --git a/src/av2/structures/sweep.py b/src/av2/structures/sweep.py
new file mode 100644
index 00000000..acb6802e
--- /dev/null
+++ b/src/av2/structures/sweep.py
@@ -0,0 +1,91 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Sweep representation of lidar sensor data."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+from av2.geometry.se3 import SE3
+from av2.utils.io import read_ego_SE3_sensor, read_feather
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt
+
+
+@dataclass
+class Sweep:
+    """Models a lidar sweep from a lidar sensor.
+
+    A sweep refers to a set of points which were captured in a fixed interval [t,t+delta), where delta ~= (1/sensor_hz).
+    Reference: https://en.wikipedia.org/wiki/Lidar
+
+    NOTE: Argoverse 2 distributes sweeps which are from two, stacked Velodyne 32 beam sensors.
+        These sensors each have different, overlapping fields-of-view.
+        Both lidars have their own reference frame: up_lidar and down_lidar, respectively.
+        We have egomotion-compensated the lidar sensor data to the egovehicle reference timestamp (`timestamp_ns`).
+
+    Args:
+        xyz: (N,3) Points in Cartesian space (x,y,z) in meters.
+        intensity: (N,1) Intensity values in the interval [0,255] corresponding to each point.
+        laser_number: (N,1) Laser numbers in the interval [0,63] corresponding to the beam which generated the point.
+        offset_ns: (N,1) Nanosecond offsets _from_ the start of the sweep.
+        timestamp_ns: Nanosecond timestamp _at_ the start of the sweep.
+        ego_SE3_up_lidar: Pose of the up lidar in the egovehicle reference frame. Translation is in meters.
+        ego_SE3_down_lidar: Pose of the down lidar in the egovehicle reference frame. Translation is in meters.
+    """
+
+    xyz: NDArrayFloat
+    intensity: NDArrayByte
+    laser_number: NDArrayByte
+    offset_ns: NDArrayInt
+    timestamp_ns: int
+    ego_SE3_up_lidar: SE3
+    ego_SE3_down_lidar: SE3
+
+    def __len__(self) -> int:
+        """Return the number of LiDAR returns in the aggregated sweep."""
+        return int(self.xyz.shape[0])
+
+    @classmethod
+    def from_feather(cls, lidar_feather_path: Path) -> Sweep:
+        """Load a lidar sweep from a feather file.
+
+        NOTE: The feather file is expected in AV2 format.
+        NOTE: The sweep is in the _ego_ reference frame.
+
+        The file should be a Apache Feather file and contain the following columns:
+            x: Coordinate of each lidar return along the x-axis.
+            y: Coordinate of each lidar return along the y-axis.
+            z: Coordinate of each lidar return along the z-axis.
+            intensity: Measure of radiant power per unit solid angle.
+            laser_number: Laser which emitted the point return.
+            offset_ns: Nanosecond delta from the sweep timestamp for the point return.
+
+        Args:
+            lidar_feather_path: Path to the lidar sweep feather file.
+
+        Returns:
+            Sweep object.
+        """
+        timestamp_ns = int(lidar_feather_path.stem)
+        lidar = read_feather(lidar_feather_path)
+
+        xyz = lidar.loc[:, ["x", "y", "z"]].to_numpy().astype(float)
+        intensity = lidar.loc[:, ["intensity"]].to_numpy().squeeze()
+        laser_number = lidar.loc[:, ["laser_number"]].to_numpy().squeeze()
+        offset_ns = lidar.loc[:, ["offset_ns"]].to_numpy().squeeze()
+
+        log_dir = lidar_feather_path.parent.parent.parent
+        sensor_name_to_pose = read_ego_SE3_sensor(log_dir=log_dir)
+        ego_SE3_up_lidar = sensor_name_to_pose["up_lidar"]
+        ego_SE3_down_lidar = sensor_name_to_pose["down_lidar"]
+
+        return cls(
+            xyz=xyz,
+            intensity=intensity,
+            laser_number=laser_number,
+            offset_ns=offset_ns,
+            timestamp_ns=timestamp_ns,
+            ego_SE3_up_lidar=ego_SE3_up_lidar,
+            ego_SE3_down_lidar=ego_SE3_down_lidar,
+        )
diff --git a/src/av2/structures/timestamped_image.py b/src/av2/structures/timestamped_image.py
new file mode 100644
index 00000000..41cb1c51
--- /dev/null
+++ b/src/av2/structures/timestamped_image.py
@@ -0,0 +1,24 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Timestamped image class with camera model for synchronization."""
+
+from dataclasses import dataclass
+
+from av2.geometry.camera.pinhole_camera import PinholeCamera
+from av2.utils.typing import NDArrayByte
+
+
+@dataclass(frozen=True)
+class TimestampedImage:
+    """Timestamped image with an accompanying camera model.
+
+    Args:
+        img: (H,W,C) image.
+        camera_model: Pinhole camera model with intrinsics and extrinsics.
+        timestamp_ns: Nanosecond timestamp.
+
+    """
+
+    img: NDArrayByte
+    camera_model: PinholeCamera
+    timestamp_ns: int
diff --git a/src/av2/utils/__init__.py b/src/av2/utils/__init__.py
new file mode 100644
index 00000000..20dbcf69
--- /dev/null
+++ b/src/av2/utils/__init__.py
@@ -0,0 +1,2 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Various utilities used throughout av2."""
diff --git a/src/av2/utils/constants.py b/src/av2/utils/constants.py
new file mode 100644
index 00000000..f6de7e93
--- /dev/null
+++ b/src/av2/utils/constants.py
@@ -0,0 +1,19 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Common mathematical and computing system constants used in AV2."""
+
+import math
+import multiprocessing as mp
+from pathlib import Path
+from typing import Final
+
+# 3.14159 ...
+PI: Final[float] = math.pi
+
+# Not a number.
+NAN: Final[float] = math.nan
+
+# System home directory.
+HOME: Final[Path] = Path.home()
+
+# Max number of logical cores available on the current machine.
+MAX_CPUS: Final[int] = mp.cpu_count()
diff --git a/src/av2/utils/dataclass.py b/src/av2/utils/dataclass.py
new file mode 100644
index 00000000..98a04b3c
--- /dev/null
+++ b/src/av2/utils/dataclass.py
@@ -0,0 +1,72 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utils to assist with dataclass-related operations."""
+
+import itertools
+from dataclasses import is_dataclass
+
+import numpy as np
+import pandas as pd
+
+
+def dataclass_eq(base_dataclass: object, other: object) -> bool:
+    """Check if base_dataclass is equal to the other object, with proper handling for numpy array fields.
+
+    Args:
+        base_dataclass: Base dataclass to compare against.
+        other: Other object to compare against the base dataclass.
+
+    Raises:
+        ValueError: If base_dataclass is not an instance of a dataclass.
+
+    Returns:
+        Flag indicating whether base_dataclass and the other object are considered equal.
+    """
+    if not is_dataclass(base_dataclass):
+        raise ValueError(f"'{base_dataclass.__class__.__name__}' is not a dataclass!")
+
+    # Check whether the two objects point to the same instance
+    if base_dataclass is other:
+        return True
+
+    # Check whether the two objects are both dataclasses of the same type
+    if base_dataclass.__class__ is not other.__class__:
+        return False
+
+    # Check whether the dataclasses have equal values in all members
+    base_tuple = vars(base_dataclass).values()
+    other_tuple = vars(other).values()
+    return all(_dataclass_member_eq(base_mem, other_mem) for base_mem, other_mem in zip(base_tuple, other_tuple))
+
+
+def _dataclass_member_eq(base: object, other: object) -> bool:
+    """Check if dataclass members base and other are equal, with proper handling for numpy arrays.
+
+    Args:
+        base: Base object to compare against.
+        other: Other object to compare against the base object.
+
+    Returns:
+        Bool flag indicating whether objects a and b are equal.
+    """
+    # Objects are equal if they point to the same instance
+    if base is other:
+        return True
+
+    # If both objects are lists, check equality for all members
+    if isinstance(base, list) and isinstance(other, list):
+        return all(_dataclass_member_eq(base_i, other_i) for base_i, other_i in itertools.zip_longest(base, other))
+
+    # If both objects are np arrays, delegate equality check to numpy's built-in operation
+    if isinstance(base, np.ndarray) and isinstance(other, np.ndarray):
+        return bool(np.array_equal(base, other))
+
+    # If both objects are pd dataframes, delegate equality check to pandas' built-in operation
+    if isinstance(base, pd.DataFrame) and isinstance(other, pd.DataFrame):
+        return bool(pd.DataFrame.equals(base, other))
+
+    # Equality checks for all other types are delegated to the standard equality check
+    try:
+        return bool(base == other)
+    except (TypeError, ValueError):
+        return False
diff --git a/src/av2/utils/dense_grid_interpolation.py b/src/av2/utils/dense_grid_interpolation.py
new file mode 100644
index 00000000..af8ca7da
--- /dev/null
+++ b/src/av2/utils/dense_grid_interpolation.py
@@ -0,0 +1,64 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utility for interpolating a dense 2d grid from sparse values."""
+
+from typing import Final, Union
+
+import numpy as np
+import scipy.interpolate
+
+import av2.geometry.mesh_grid as mesh_grid
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt
+
+# minimum number of points required by QHull to construct an initial simplex, for interpolation.
+MIN_REQUIRED_POINTS_SIMPLEX: Final[int] = 4
+
+
+def interp_dense_grid_from_sparse(
+    grid_img: Union[NDArrayByte, NDArrayFloat],
+    points: NDArrayInt,
+    values: Union[NDArrayByte, NDArrayFloat],
+    grid_h: int,
+    grid_w: int,
+    interp_method: str,
+) -> Union[NDArrayByte, NDArrayFloat]:
+    """Interpolate a dense 2d grid from sparse values.
+
+    We interpolate (y,x) points instead of (x,y). Then we get
+    not quite the same as `matplotlib.mlab.griddata
+
+    Args:
+        grid_img: array of shape (grid_h, grid_w, 3) representing empty image to populate
+        points: array of shape (K,2) representing 2d coordinates.
+        values: array of shape (K,D), representing values at the K input points. For example,
+            D=3 for interpolation of RGB values in the BEV, or D=1 for depth values in an ego-view depth map.
+        grid_h: height of the dense grid, in pixels.
+        grid_w: width of the dense grid, in pixels.
+        interp_method: interpolation method, either "linear" or "nearest"
+
+    Returns:
+        grid_img: array of shape (grid_h, grid_w, 3) representing densely interpolated image.
+            Data type of output array is determined by the dtype of `values`, e.g. uint8 for RGB values.
+
+    Raises:
+        ValueError: If requested interpolation method is invalid.
+    """
+    if interp_method not in ["linear", "nearest"]:
+        raise ValueError("Unknown interpolation method.")
+
+    if points.shape[0] < MIN_REQUIRED_POINTS_SIMPLEX:
+        # return the empty grid, since we can't interpolate.
+        return grid_img
+
+    output_dtype = values.dtype
+
+    # get (x,y) tuples back
+    grid_coords = mesh_grid.get_mesh_grid_as_point_cloud(min_x=0, max_x=grid_w - 1, min_y=0, max_y=grid_h - 1)
+    # make RGB a function of (dim0=x,dim1=y)
+    interp_vals = scipy.interpolate.griddata(points, values, grid_coords, method=interp_method)
+
+    u = grid_coords[:, 0].astype(np.int32)
+    v = grid_coords[:, 1].astype(np.int32)
+    # Now index in at (y,x) locations
+    grid_img[v, u] = interp_vals
+    return grid_img.astype(output_dtype)
diff --git a/src/av2/utils/depth_map_utils.py b/src/av2/utils/depth_map_utils.py
new file mode 100644
index 00000000..55726253
--- /dev/null
+++ b/src/av2/utils/depth_map_utils.py
@@ -0,0 +1,71 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Provides utilities for working with and visualizing ego-view depth maps."""
+
+from typing import Final
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+MIN_DISTANCE_AWAY_M: Final[float] = 30.0  # assume max noise starting at this distance (meters)
+MAX_ALLOWED_NOISE_M: Final[float] = 3.0  # meters
+
+
+def compute_allowed_noise_per_point(points_cam: NDArrayFloat) -> NDArrayFloat:
+    """Compute allowed noise values when comparing depth maps.
+
+    For example, when rendering only objects that are visible within a frame, one can compare
+    z-order against a known depth map. However, depth maps can be noisy, and we wish to be
+    robust to this. Accordingly, we assume noise is proportional to distance away from the camera,
+    and thus we compensate for noisier depth values at range.
+
+    Args:
+        points_cam: array of shape (N,3) representing 3d points in the camera coordinate frame.
+            We extract z-values from the camera frame coordinates.
+
+    Returns:
+        array of shape (N,) representing allowed amount of noise, in meters, for entries in a depth map.
+    """
+    dists_away: NDArrayFloat = np.linalg.norm(points_cam, axis=1)  # type: ignore
+
+    max_dist_away = dists_away.max()
+    max_dist_away = max(max_dist_away, MIN_DISTANCE_AWAY_M)
+
+    allowed_noise: NDArrayFloat = (dists_away / max_dist_away) * MAX_ALLOWED_NOISE_M
+    return allowed_noise
+
+
+def vis_depth_map(
+    img_rgb: NDArrayByte, depth_map: NDArrayFloat, interp_depth_map: bool, num_dilation_iters: int = 10
+) -> None:
+    """Visualize a depth map using Matplotlib's `inferno` colormap side by side with an RGB image.
+
+    Args:
+        img_rgb: array of shape (H,W,3) representing an RGB image.
+        depth_map: array of shape (H,W) representing a depth map. Only values in [0,255] will be visible.
+        interp_depth_map: whether the depth map represents a densely interpolated grid. If not, the zero values
+            are not meaningful, and we set them to a maximum value, so that matplotlib will ignore them.
+        num_dilation_iters: number of iterations to use for dilating with box kernel, to make sparse value
+            more visible.
+    """
+    if not interp_depth_map:
+        # fix the zero values?
+        depth_map[depth_map == 0] = np.finfo(np.float32).max
+
+    # purely for visualization
+    depth_map = cv2.dilate(
+        depth_map,
+        kernel=np.ones((2, 2), np.uint8),
+        iterations=num_dilation_iters,
+    )
+
+    # prevent too dark in the foreground, clip far away
+    depth_map = np.clip(depth_map, 0, 50)
+
+    plt.subplot(1, 2, 1)
+    plt.imshow(img_rgb)
+    plt.subplot(1, 2, 2)
+    plt.imshow((depth_map * 3).astype(np.uint8), cmap="inferno")  # type: ignore
diff --git a/src/av2/utils/dilation_utils.py b/src/av2/utils/dilation_utils.py
new file mode 100644
index 00000000..0b14beb3
--- /dev/null
+++ b/src/av2/utils/dilation_utils.py
@@ -0,0 +1,41 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Utility functions for dilation of a binary mask."""
+
+
+import cv2
+import numpy as np
+
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+
+def dilate_by_l2(img: NDArrayByte, dilation_thresh: float = 5.0) -> NDArrayByte:
+    """Dilate a mask using the L2 distance from a zero pixel.
+
+    OpenCV's distance transform calculates the DISTANCE TO THE CLOSEST ZERO PIXEL for each
+    pixel of the source image. Although the distance type could be L1, L2, etc, we use L2.
+
+    We specify the "maskSize", which represents the size of the distance transform mask. It can
+    be 3, 5, or CV_DIST_MASK_PRECISE (the latter option is only supported by the first function).
+
+    For us, foreground values are 1 and background values are 0.
+
+    Args:
+        img: Array of shape (M, N) representing an 8-bit single-channel (binary) source image
+        dilation_thresh: Threshold for how far away a zero pixel can be
+
+    Returns:
+        An image with the same size with the dilated mask
+
+    Raises:
+        ValueError: If distance transform isn't a uint8 array.
+    """
+    if img.dtype != np.dtype(np.uint8):
+        raise ValueError("Input to distance transform must be a uint8 array.")
+
+    mask_diff: NDArrayByte = np.ones_like(img, dtype=np.uint8) - img
+    distance_mask: NDArrayFloat = cv2.distanceTransform(
+        mask_diff, distanceType=cv2.DIST_L2, maskSize=cv2.DIST_MASK_PRECISE
+    )
+    dilated_img: NDArrayByte = np.less_equal(distance_mask.astype(np.float32), dilation_thresh).astype(np.uint8)
+    return dilated_img
diff --git a/src/av2/utils/helpers.py b/src/av2/utils/helpers.py
new file mode 100644
index 00000000..6167d000
--- /dev/null
+++ b/src/av2/utils/helpers.py
@@ -0,0 +1,24 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Assortment of helper methods."""
+
+from typing import Optional, Sequence, Union
+
+from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat, NDArrayInt
+
+
+def assert_np_array_shape(
+    array: Union[NDArrayBool, NDArrayByte, NDArrayFloat, NDArrayInt], target_shape: Sequence[Optional[int]]
+) -> None:
+    """Check for shape correctness.
+
+    Args:
+        array: array to check dimensions of.
+        target_shape: desired shape. use None for unknown dimension sizes.
+
+    Raises:
+        ValueError: if array's shape does not match target_shape for any of the specified dimensions.
+    """
+    for index_dim, (array_shape_dim, target_shape_dim) in enumerate(zip(array.shape, target_shape)):
+        if target_shape_dim and array_shape_dim != target_shape_dim:
+            raise ValueError(f"array.shape[{index_dim}]: {array_shape_dim} != {target_shape_dim}.")
diff --git a/src/av2/utils/io.py b/src/av2/utils/io.py
new file mode 100644
index 00000000..5e024308
--- /dev/null
+++ b/src/av2/utils/io.py
@@ -0,0 +1,233 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Helper functions for deserializing AV2 data."""
+
+import json
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import cv2
+import numpy as np
+import pandas as pd
+from pyarrow import feather
+
+import av2.geometry.geometry as geometry_utils
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+# Mapping from egovehicle time in nanoseconds to egovehicle pose.
+TimestampedCitySE3EgoPoses = Dict[int, SE3]
+
+# Mapping from sensor name to sensor pose.
+SensorPosesMapping = Dict[str, SE3]
+
+
+def read_feather(path: Path, columns: Optional[Tuple[str, ...]] = None) -> pd.DataFrame:
+    """Read Apache Feather data from a .feather file.
+
+    AV2 uses .feather to serialize much of its data. This function handles the deserialization
+    process and returns a `pandas` DataFrame with rows corresponding to the records and the
+    columns corresponding to the record attributes.
+
+    Args:
+        path: Source data file (e.g., 'lidar.feather', 'calibration.feather', etc.)
+        columns: Tuple of columns to load for the given record. Defaults to None.
+
+    Returns:
+        (N,len(columns)) Apache Feather data represented as a `pandas` DataFrame.
+    """
+    data: pd.DataFrame = feather.read_feather(path, columns=columns)
+    return data
+
+
+def read_lidar_sweep(fpath: Path, attrib_spec: str = "xyz") -> NDArrayFloat:
+    """Load a point cloud file from a filepath.
+
+    Args:
+        fpath: path to a .feather file
+        attrib_spec: string of C characters, each char representing a desired point attribute
+            x -> point x-coord
+            y -> point y-coord
+            z -> point z-coord
+
+        The following attributes are not loaded:
+            intensity -> point intensity/reflectance
+            laser_number -> laser number of laser from which point was returned
+            offset_ns -> nanosecond timestamp offset per point, from sweep timestamp.
+
+    Returns:
+        Array of shape (N, C). If attrib_str is invalid, `None` will be returned
+
+    Raises:
+        ValueError: If any element of `attrib_spec` is not in (x, y, z, intensity, laser_number, offset_ns).
+    """
+    possible_attributes = ["x", "y", "z"]
+    if not all([a in possible_attributes for a in attrib_spec]):
+        raise ValueError("Attributes must be in (x, y, z, intensity, laser_number, offset_ns).")
+
+    sweep_df = read_feather(fpath)
+
+    # return only the requested point attributes
+    sweep: NDArrayFloat = sweep_df[list(attrib_spec)].to_numpy().astype(np.float64)
+    return sweep
+
+
+def read_ego_SE3_sensor(log_dir: Path) -> SensorPosesMapping:
+    """Read the sensor poses for the given log.
+
+    The sensor pose defines an SE3 transformation from the sensor reference frame to the egovehicle reference frame.
+    Mathematically we define this transformation as: $$ego_SE3_sensor$$.
+
+    In other words, when this transformation is applied to a set of points in the sensor reference frame, they
+    will be transformed to the egovehicle reference frame.
+
+    Example (1).
+        points_ego = ego_SE3_sensor(points_sensor) apply the SE3 transformation to points in the sensor reference frame.
+
+    Example (2).
+        sensor_SE3_ego = ego_SE3_sensor^{-1} take the inverse of the SE3 transformation.
+        points_sensor = sensor_SE3_ego(points_ego) apply the SE3 transformation to points in the ego reference frame.
+
+    Extrinsics:
+        sensor_name: Name of the sensor.
+        qw: scalar component of a quaternion.
+        qx: X-axis coefficient of a quaternion.
+        qy: Y-axis coefficient of a quaternion.
+        qz: Z-axis coefficient of a quaternion.
+        tx_m: X-axis translation component.
+        ty_m: Y-axis translation component.
+        tz_m: Z-axis translation component.
+
+    Args:
+        log_dir: Path to the log directory.
+
+    Returns:
+        Mapping from sensor name to sensor pose.
+    """
+    ego_SE3_sensor_path = Path(log_dir, "calibration", "egovehicle_SE3_sensor.feather")
+    ego_SE3_sensor = read_feather(ego_SE3_sensor_path)
+    rotations = geometry_utils.quat_to_mat(ego_SE3_sensor.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy())
+    translations = ego_SE3_sensor.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy()
+    sensor_names = ego_SE3_sensor.loc[:, "sensor_name"].to_numpy()
+
+    sensor_name_to_pose: SensorPosesMapping = {
+        name: SE3(rotation=rotations[i], translation=translations[i]) for i, name in enumerate(sensor_names)
+    }
+    return sensor_name_to_pose
+
+
+def read_city_SE3_ego(log_dir: Path) -> TimestampedCitySE3EgoPoses:
+    """Read the egovehicle poses in the city reference frame.
+
+    The egovehicle city pose defines an SE3 transformation from the egovehicle reference frame to the city ref. frame.
+    Mathematically we define this transformation as: $$city_SE3_ego$$.
+
+    In other words, when this transformation is applied to a set of points in the egovehicle reference frame, they
+    will be transformed to the city reference frame.
+
+    Example (1).
+        points_city = city_SE3_ego(points_ego) applying the SE3 transformation to points in the egovehicle ref. frame.
+
+    Example (2).
+        ego_SE3_city = city_SE3_ego^{-1} take the inverse of the SE3 transformation.
+        points_ego = ego_SE3_city(points_city) applying the SE3 transformation to points in the city ref. frame.
+
+    Extrinsics:
+        timestamp_ns: Egovehicle nanosecond timestamp.
+        qw: scalar component of a quaternion.
+        qx: X-axis coefficient of a quaternion.
+        qy: Y-axis coefficient of a quaternion.
+        qz: Z-axis coefficient of a quaternion.
+        tx_m: X-axis translation component.
+        ty_m: Y-axis translation component.
+        tz_m: Z-axis translation component.
+
+    Args:
+        log_dir: Path to the log directory.
+
+    Returns:
+        Mapping from egovehicle time (in nanoseconds) to egovehicle pose in the city reference frame.
+    """
+    city_SE3_ego_path = Path(log_dir, "city_SE3_egovehicle.feather")
+    city_SE3_ego = read_feather(city_SE3_ego_path)
+
+    quat_wxyz = city_SE3_ego.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy()
+    translation_xyz_m = city_SE3_ego.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy()
+    timestamps_ns = city_SE3_ego["timestamp_ns"].to_numpy()
+
+    rotation = geometry_utils.quat_to_mat(quat_wxyz)
+    timestamp_city_SE3_ego_dict: TimestampedCitySE3EgoPoses = {
+        ts: SE3(rotation=rotation[i], translation=translation_xyz_m[i]) for i, ts in enumerate(timestamps_ns)
+    }
+    return timestamp_city_SE3_ego_dict
+
+
+def read_img(img_path: Path, channel_order: str = "RGB") -> NDArrayByte:
+    """Return a RGB or BGR image array, given an image file path.
+
+    Args:
+        img_path: Source path to load the image.
+        channel_order: color channel ordering for 3-channel images.
+
+    Returns:
+        (H,W,3) RGB or BGR image.
+
+    Raises:
+        ValueError: If `channel_order` isn't 'RGB' or 'BGR'.
+    """
+    if channel_order not in ["RGB", "BGR"]:
+        raise ValueError("Unsupported channel order (must be BGR or RGB).")
+
+    img_bgr: NDArrayByte = cv2.imread(str(img_path))
+    if channel_order == "BGR":
+        return img_bgr
+
+    img_rgb: NDArrayByte = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+    return img_rgb
+
+
+def write_img(img_path: Path, img: NDArrayByte, channel_order: str = "RGB") -> None:
+    """Save image to disk.
+
+    Args:
+        img_path: Destination path to write the image.
+        img: (H,W,3) image.
+        channel_order: color channel ordering for 3-channel images.
+
+    Raises:
+        ValueError: If `channel_order` isn't 'RGB' or 'BGR'.
+    """
+    if channel_order not in ["RGB", "BGR"]:
+        raise ValueError("Unsupported channel order (must be BGR or RGB).")
+
+    if channel_order == "RGB":
+        img: NDArrayByte = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)  # type: ignore
+
+    cv2.imwrite(str(img_path), img)
+
+
+def read_json_file(fpath: Path) -> Any:
+    """Load dictionary from JSON file.
+
+    Args:
+        fpath: Path to JSON file.
+
+    Returns:
+        Deserialized Python dictionary.
+    """
+    with open(fpath, "rb") as f:
+        return json.load(f)
+
+
+def save_json_dict(
+    json_fpath: Path,
+    dictionary: Union[Dict[Any, Any], List[Any]],
+) -> None:
+    """Save a Python dictionary to a JSON file.
+
+    Args:
+        json_fpath: Path to file to create.
+        dictionary: Python dictionary to be serialized.
+    """
+    with open(json_fpath, "w") as f:
+        json.dump(dictionary, f)
diff --git a/src/av2/utils/metric_time.py b/src/av2/utils/metric_time.py
new file mode 100644
index 00000000..911761f2
--- /dev/null
+++ b/src/av2/utils/metric_time.py
@@ -0,0 +1,31 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Time units utilities."""
+
+from enum import Enum
+from typing import Union
+
+
+class TimeUnit(str, Enum):
+    """Metric time units."""
+
+    Second = "SECOND"
+    Millisecond = "MILLISECOND"
+    Microsecond = "MICROSECOND"
+    Nanosecond = "NANOSECOND"
+
+
+def to_metric_time(ts: Union[int, float], src: TimeUnit, dst: TimeUnit) -> float:
+    """Convert a timestamp from src units of metric time, to dst units.
+
+    Args:
+        ts: timestamp, expressed either as an integer or float, measured in `src` units of metric time
+        src: source unit of metric time
+        dst: destination/target unit of metric time
+
+    Returns:
+        timestamp expressed now in `dst` units of metric time
+    """
+    units_per_sec = {TimeUnit.Second: 1, TimeUnit.Millisecond: 1e3, TimeUnit.Microsecond: 1e6, TimeUnit.Nanosecond: 1e9}
+    # ts is in units of `src`, which will cancel with the denominator
+    return ts * (units_per_sec[dst] / units_per_sec[src])
diff --git a/src/av2/utils/raster.py b/src/av2/utils/raster.py
new file mode 100644
index 00000000..ebcdee77
--- /dev/null
+++ b/src/av2/utils/raster.py
@@ -0,0 +1,49 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Rasterization related utilities."""
+
+from typing import List
+
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw
+
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+
+def get_mask_from_polygons(polygons: List[NDArrayFloat], img_h: int, img_w: int) -> NDArrayByte:
+    """Rasterize multiple polygons onto a single 2d grid/array.
+
+    NOTE: Pillow can gracefully handle the scenario when a polygon has coordinates outside of the grid,
+    and also when the first and last vertex of a polygon's coordinates is repeated.
+
+    Args:
+        polygons: list of (N,2) numpy float arrays, where N is variable per polygon.
+        img_h: height of the image grid to generate, in pixels
+        img_w: width of the image grid to generate, in pixels
+
+    Returns:
+        2d array with 0/1 values representing a binary segmentation mask
+    """
+    mask_img = Image.new("L", size=(img_w, img_h), color=0)
+    for polygon in polygons:
+        vert_list = [(x, y) for x, y in polygon]
+        ImageDraw.Draw(mask_img).polygon(vert_list, outline=1, fill=1)
+
+    mask: NDArrayByte = np.array(mask_img)
+    return mask
+
+
+def blend_images(img0: NDArrayByte, img1: NDArrayByte, alpha: float = 0.7) -> NDArrayByte:
+    """Alpha-blend two images together.
+
+    Args:
+        img0: uint8 array of shape (H,W,3)
+        img1: uint8 array of shape (H,W,3)
+        alpha: Alpha blending coefficient.
+
+    Returns:
+        uint8 array of shape (H,W,3)
+    """
+    blended: NDArrayByte = cv2.addWeighted(img0, alpha, img1, (1 - alpha), gamma=0)
+    return blended
diff --git a/src/av2/utils/synchronization_database.py b/src/av2/utils/synchronization_database.py
new file mode 100644
index 00000000..8bcd5e63
--- /dev/null
+++ b/src/av2/utils/synchronization_database.py
@@ -0,0 +1,224 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Database to synchronize timestamped sensor data."""
+
+import glob
+import logging
+from pathlib import Path
+from typing import Dict, Final, Iterable, Optional, Tuple, cast
+
+import numpy as np
+
+from av2.datasets.sensor.constants import RingCameras, StereoCameras
+from av2.utils.metric_time import TimeUnit, to_metric_time
+from av2.utils.typing import NDArrayInt
+
+logger = logging.getLogger(__name__)
+
+Millisecond = TimeUnit.Millisecond
+Nanosecond = TimeUnit.Nanosecond
+Second = TimeUnit.Second
+
+RING_CAM_FPS: Final[int] = 20  # ArgoverseConfig.ring_cam_fps
+STEREO_CAM_FPS: Final[int] = 20  # ArgoverseConfig.stereo_cam_fps
+LIDAR_FRAME_RATE_HZ: Final[int] = 10
+
+# constants defined in milliseconds
+# below evaluates to 33.3 ms
+RING_CAM_SHUTTER_INTERVAL_MS: Final[float] = to_metric_time(ts=1 / RING_CAM_FPS, src=Second, dst=Millisecond)
+
+# below evaluates to 200 ms
+STEREO_CAM_SHUTTER_INTERVAL_MS: Final[float] = to_metric_time(ts=1 / STEREO_CAM_FPS, src=Second, dst=Millisecond)
+
+# below evaluates to 100 ms
+LIDAR_SWEEP_INTERVAL_MS: Final[float] = to_metric_time(ts=1 / LIDAR_FRAME_RATE_HZ, src=Second, dst=Millisecond)
+
+ALLOWED_TIMESTAMP_BUFFER_MS: Final[int] = 2  # allow 2 ms of buffer
+LIDAR_SWEEP_INTERVAL_W_BUFFER_MS: Final[float] = LIDAR_SWEEP_INTERVAL_MS + ALLOWED_TIMESTAMP_BUFFER_MS
+
+
+def get_timestamps_from_sensor_folder(sensor_folder_wildcard: str) -> NDArrayInt:
+    """Timestamp always lies at end of filename.
+
+    Args:
+        sensor_folder_wildcard: string to glob to find all filepaths for a particular
+                    sensor files within a single log run
+
+    Returns:
+        Numpy array of integers, representing timestamps
+    """
+    path_generator = glob.glob(sensor_folder_wildcard)
+    path_generator.sort()
+
+    timestamps: NDArrayInt = np.array([int(Path(jpg_fpath).stem.split("_")[-1]) for jpg_fpath in path_generator])
+    return timestamps
+
+
+def find_closest_integer_in_ref_arr(query_int: int, ref_arr: NDArrayInt) -> Tuple[int, int]:
+    """Find the closest integer to any integer inside a reference array, and the corresponding difference.
+
+    In our use case, the query integer represents a nanosecond-discretized timestamp, and the
+    reference array represents a numpy array of nanosecond-discretized timestamps.
+
+    Instead of sorting the whole array of timestamp differences, we just
+    take the minimum value (to speed up this function).
+
+    Args:
+        query_int: query integer,
+        ref_arr: Numpy array of integers
+
+    Returns:
+        integer, representing the closest integer found in a reference array to a query
+        integer, representing the integer difference between the match and query integers
+    """
+    closest_ind = np.argmin(np.absolute(ref_arr - query_int))
+    closest_int = cast(int, ref_arr[closest_ind])  # mypy does not understand numpy arrays
+    int_diff = np.absolute(query_int - closest_int)
+    return closest_int, int_diff
+
+
+class SynchronizationDB:
+    """Structure to synchronize sensor data from vehicle timestamps."""
+
+    # Max difference between camera and LiDAR observation would be if the LiDAR timestamp is halfway between
+    # two camera observations (i.e. RING_CAM_SHUTTER_INTERVAL_MS / 2 milliseconds on either side)
+    # then convert milliseconds to nanoseconds
+    MAX_LIDAR_RING_CAM_TIMESTAMP_DIFF = to_metric_time(
+        ts=RING_CAM_SHUTTER_INTERVAL_MS / 2, src=Millisecond, dst=Nanosecond
+    )
+
+    # Since Stereo is more sparse, we look at (STEREO_CAM_SHUTTER_INTERVAL_MS / 2) milliseconds on either side
+    # then convert milliseconds to nanoseconds
+    MAX_LIDAR_STEREO_CAM_TIMESTAMP_DIFF = to_metric_time(
+        ts=STEREO_CAM_SHUTTER_INTERVAL_MS / 2, src=Millisecond, dst=Nanosecond
+    )
+
+    # LiDAR is 10 Hz (once per 100 milliseconds)
+    # We give an extra 2 ms buffer for the message to arrive, totaling 102 ms.
+    # At any point we sample, we shouldn't be more than 51 ms away.
+    # then convert milliseconds to nanoseconds
+    MAX_LIDAR_ANYCAM_TIMESTAMP_DIFF = to_metric_time(
+        ts=LIDAR_SWEEP_INTERVAL_W_BUFFER_MS / 2, src=Millisecond, dst=Nanosecond
+    )
+
+    def __init__(self, dataset_dir: str, collect_single_log_id: Optional[str] = None) -> None:
+        """Build the SynchronizationDB.
+
+        Note that the timestamps for each camera channel are not identical, but they are clustered together.
+
+        Args:
+            dataset_dir: path to dataset.
+            collect_single_log_id: log id to process. (All if not set)
+        """
+        logger.info("Building SynchronizationDB")
+
+        if collect_single_log_id is None:
+            log_fpaths = glob.glob(f"{dataset_dir}/*")
+        else:
+            log_fpaths = [f"{dataset_dir}/{collect_single_log_id}"]
+
+        self.per_log_cam_timestamps_index: Dict[str, Dict[str, NDArrayInt]] = {}
+        self.per_log_lidar_timestamps_index: Dict[str, NDArrayInt] = {}
+
+        for log_fpath in log_fpaths:
+            log_id = Path(log_fpath).name
+
+            self.per_log_cam_timestamps_index[log_id] = {}
+            for cam_name in list(RingCameras) + list(StereoCameras):
+                sensor_folder_wildcard = f"{dataset_dir}/{log_id}/sensors/cameras/{cam_name}/*.jpg"
+                cam_timestamps = get_timestamps_from_sensor_folder(sensor_folder_wildcard)
+                self.per_log_cam_timestamps_index[log_id][cam_name] = cam_timestamps
+
+            sensor_folder_wildcard = f"{dataset_dir}/{log_id}/sensors/lidar/*.feather"
+            lidar_timestamps = get_timestamps_from_sensor_folder(sensor_folder_wildcard)
+            self.per_log_lidar_timestamps_index[log_id] = lidar_timestamps
+
+    def get_valid_logs(self) -> Iterable[str]:
+        """Return the log_ids for which the SynchronizationDatabase contains pose information."""
+        return self.per_log_cam_timestamps_index.keys()
+
+    def get_closest_lidar_timestamp(self, cam_timestamp_ns: int, log_id: str) -> Optional[int]:
+        """Given an image timestamp, find the synchronized corresponding LiDAR timestamp.
+
+        This LiDAR timestamp should have the closest absolute timestamp to the image timestamp.
+
+        Args:
+            cam_timestamp_ns: integer
+            log_id: string
+
+        Returns:
+            closest_lidar_timestamp: closest timestamp
+        """
+        if log_id not in self.per_log_lidar_timestamps_index:
+            return None
+
+        lidar_timestamps = self.per_log_lidar_timestamps_index[log_id]
+        # catch case if no files were loaded for a particular sensor
+        if not lidar_timestamps.tolist():
+            return None
+
+        closest_lidar_timestamp, timestamp_diff = find_closest_integer_in_ref_arr(cam_timestamp_ns, lidar_timestamps)
+        if timestamp_diff > self.MAX_LIDAR_ANYCAM_TIMESTAMP_DIFF:
+            # convert to nanoseconds->milliseconds for readability
+            logger.warning(
+                "No corresponding LiDAR sweep: %s > %s ms",
+                to_metric_time(ts=timestamp_diff, src=Nanosecond, dst=Millisecond),
+                to_metric_time(ts=self.MAX_LIDAR_ANYCAM_TIMESTAMP_DIFF, src=Nanosecond, dst=Millisecond),
+            )
+            return None
+        return closest_lidar_timestamp
+
+    def get_closest_cam_channel_timestamp(self, lidar_timestamp: int, cam_name: str, log_id: str) -> Optional[int]:
+        """Given a LiDAR timestamp, find the synchronized corresponding image timestamp for a particular camera.
+
+        This image timestamp should have the closest absolute timestamp.
+
+        Args:
+            lidar_timestamp: integer
+            cam_name: string, representing path to log directories
+            log_id: string
+
+        Returns:
+            closest_cam_ch_timestamp: closest timestamp
+        """
+        if log_id not in self.per_log_cam_timestamps_index or cam_name not in self.per_log_cam_timestamps_index[log_id]:
+            return None
+
+        cam_timestamps = self.per_log_cam_timestamps_index[log_id][cam_name]
+        # catch case if no files were loaded for a particular sensor
+        if not cam_timestamps.tolist():
+            return None
+
+        closest_cam_ch_timestamp, timestamp_diff = find_closest_integer_in_ref_arr(lidar_timestamp, cam_timestamps)
+        if timestamp_diff > self.MAX_LIDAR_RING_CAM_TIMESTAMP_DIFF and cam_name in list(RingCameras):
+            # convert to nanoseconds->milliseconds for readability
+            logger.warning(
+                "No corresponding ring image at %s: %.1f > %s ms",
+                lidar_timestamp,
+                to_metric_time(ts=timestamp_diff, src=Nanosecond, dst=Millisecond),
+                to_metric_time(ts=self.MAX_LIDAR_RING_CAM_TIMESTAMP_DIFF, src=Nanosecond, dst=Millisecond),
+            )
+            return None
+        elif timestamp_diff > self.MAX_LIDAR_STEREO_CAM_TIMESTAMP_DIFF and cam_name in list(StereoCameras):
+            # convert to nanoseconds->milliseconds for readability
+            logger.warning(
+                "No corresponding stereo image at %s: %.1f > %s ms",
+                lidar_timestamp,
+                to_metric_time(ts=timestamp_diff, src=Nanosecond, dst=Millisecond),
+                to_metric_time(ts=self.MAX_LIDAR_STEREO_CAM_TIMESTAMP_DIFF, src=Nanosecond, dst=Millisecond),
+            )
+            return None
+        return closest_cam_ch_timestamp
+
+
+if __name__ == "__main__":
+    # example usage
+
+    root_dir = "./"
+    camera = "ring_front_center"
+    id = "c6911883-1843-3727-8eaa-41dc8cda8993"
+
+    db = SynchronizationDB(root_dir)
+    get_timestamps_from_sensor_folder(root_dir)
+    db.get_valid_logs()
+    db.get_closest_cam_channel_timestamp(0, camera, id)
diff --git a/src/av2/utils/typing.py b/src/av2/utils/typing.py
new file mode 100644
index 00000000..7277d9e3
--- /dev/null
+++ b/src/av2/utils/typing.py
@@ -0,0 +1,16 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Types used throughout the package."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+NDArrayNumber = npt.NDArray["np.number[Any]"]
+NDArrayBool = npt.NDArray[np.bool_]
+NDArrayFloat = npt.NDArray[np.float64]
+NDArrayByte = npt.NDArray[np.uint8]
+NDArrayInt = npt.NDArray[np.int64]
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 00000000..68b1151e
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,13 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Constants used for unit tests."""
+
+from pathlib import Path
+
+import pytest
+
+
+@pytest.fixture(scope="session")
+def test_data_root_dir() -> Path:
+    """Return the absolute path to the root directory for test data."""
+    return Path(__file__).resolve().parent / "test_data"
diff --git a/tests/datasets/motion_forecasting/eval/test_metrics.py b/tests/datasets/motion_forecasting/eval/test_metrics.py
new file mode 100644
index 00000000..4a4f12e1
--- /dev/null
+++ b/tests/datasets/motion_forecasting/eval/test_metrics.py
@@ -0,0 +1,116 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for motion forecasting metrics."""
+
+from typing import Final
+
+import numpy as np
+import pytest
+
+import av2.datasets.motion_forecasting.eval.metrics as metrics
+from av2.utils.typing import NDArrayFloat, NDArrayNumber
+
+# Build stationary GT trajectory at (0, 0)
+test_N: Final[int] = 10
+_STATIONARY_GT_TRAJ = np.zeros((test_N, 2))
+
+# Case 1: K=1 forecast stationary at (1, 1)
+forecasted_trajectories_stationary_k1 = np.ones((1, test_N, 2))
+expected_ade_stationary_k1 = np.full((1,), np.sqrt(2))
+expected_fde_stationary_k1 = np.full((1,), np.sqrt(2))
+
+# Case 2: K=6 forecasts stationary at (1, 1)
+forecasted_trajectories_stationary_k6 = np.ones((6, test_N, 2))
+expected_ade_stationary_k6 = np.full((6,), np.sqrt(2))
+expected_fde_stationary_k6 = np.full((6,), np.sqrt(2))
+
+# Case 3: K=1 forecast in straight line on X axis
+forecasted_trajectories_straight_k1 = np.stack([np.arange(test_N), np.zeros(test_N)], axis=1)[np.newaxis, ...]  # 1xNx2
+expected_ade_straight_k1 = np.full((1,), np.arange(test_N).mean())
+expected_fde_straight_k1 = np.full((1,), test_N - 1)
+
+# Case 4: K=6 forecasts in straight line on X axis
+forecasted_trajectories_straight_k6: NDArrayFloat = np.concatenate(  # type: ignore
+    [forecasted_trajectories_straight_k1] * 6, axis=0
+)  # 6xNx2
+expected_ade_straight_k6 = np.full((6,), np.arange(test_N).mean())
+expected_fde_straight_k6 = np.full((6,), test_N - 1)
+
+# Case 5: K=1 forecast in diagonal line
+forecasted_trajectories_diagonal_k1 = np.stack([np.arange(test_N), np.arange(test_N)], axis=1)[np.newaxis, ...]  # 1xNx2
+expected_ade_diagonal_k1 = np.full((1,), 6.36396103)
+expected_fde_diagonal_k1 = np.full((1,), np.hypot(test_N - 1, test_N - 1))
+
+
+@pytest.mark.parametrize(
+    "forecasted_trajectories, expected_ade",
+    [
+        (forecasted_trajectories_stationary_k1, expected_ade_stationary_k1),
+        (forecasted_trajectories_stationary_k6, expected_ade_stationary_k6),
+        (forecasted_trajectories_straight_k1, expected_ade_straight_k1),
+        (forecasted_trajectories_straight_k6, expected_ade_straight_k6),
+        (forecasted_trajectories_diagonal_k1, expected_ade_diagonal_k1),
+    ],
+    ids=["stationary_k1", "stationary_k6", "straight_k1", "straight_k6", "diagonal_k1"],
+)
+def test_compute_ade(forecasted_trajectories: NDArrayNumber, expected_ade: NDArrayFloat) -> None:
+    """Test that compute_ade returns the correct output with valid inputs.
+
+    Args:
+        forecasted_trajectories: Forecasted trajectories for test case.
+        expected_ade: Expected average displacement error.
+    """
+    ade = metrics.compute_ade(forecasted_trajectories, _STATIONARY_GT_TRAJ)
+    np.testing.assert_allclose(ade, expected_ade)  # type: ignore
+
+
+@pytest.mark.parametrize(
+    "forecasted_trajectories, expected_fde",
+    [
+        (forecasted_trajectories_stationary_k1, expected_fde_stationary_k1),
+        (forecasted_trajectories_stationary_k6, expected_fde_stationary_k6),
+        (forecasted_trajectories_straight_k1, expected_fde_straight_k1),
+        (forecasted_trajectories_straight_k6, expected_fde_straight_k6),
+        (forecasted_trajectories_diagonal_k1, expected_fde_diagonal_k1),
+    ],
+    ids=["stationary_k1", "stationary_k6", "straight_k1", "straight_k6", "diagonal_k1"],
+)
+def test_compute_fde(forecasted_trajectories: NDArrayNumber, expected_fde: NDArrayFloat) -> None:
+    """Test that compute_fde returns the correct output with valid inputs.
+
+    Args:
+        forecasted_trajectories: Forecasted trajectories for test case.
+        expected_fde: Expected final displacement error.
+    """
+    fde = metrics.compute_fde(forecasted_trajectories, _STATIONARY_GT_TRAJ)
+    assert np.array_equal(fde, expected_fde)
+
+
+@pytest.mark.parametrize(
+    "forecasted_trajectories, miss_threshold_m, expected_is_missed_label",
+    [
+        (forecasted_trajectories_stationary_k1, 2.0, False),
+        (forecasted_trajectories_stationary_k6, 2.0, False),
+        (forecasted_trajectories_straight_k1, expected_fde_straight_k1[0] + 0.01, False),
+        (forecasted_trajectories_straight_k1, expected_fde_straight_k1[0] - 0.01, True),
+        (forecasted_trajectories_diagonal_k1, 2.0, True),
+    ],
+    ids=["stationary_k1", "stationary_k6", "straight_below_threshold", "straight_above_threshold", "diagonal"],
+)
+def test_compute_is_missed_prediction(
+    forecasted_trajectories: NDArrayNumber, miss_threshold_m: float, expected_is_missed_label: bool
+) -> None:
+    """Test that compute_is_missed_prediction returns the correct output with valid inputs.
+
+    Args:
+        forecasted_trajectories: Forecasted trajectories for test case.
+        miss_threshold_m: Minimum distance threshold for final displacement to be considered a miss.
+        expected_is_missed_label: Expected is_missed label for test case.
+    """
+    is_missed_prediction = metrics.compute_is_missed_prediction(
+        forecasted_trajectories, _STATIONARY_GT_TRAJ, miss_threshold_m
+    )
+
+    # Check that is_missed labels are of the correct shape and have the correct value
+    assert is_missed_prediction.shape == forecasted_trajectories.shape[:1]
+    assert np.all(is_missed_prediction == expected_is_missed_label)
diff --git a/tests/datasets/motion_forecasting/test_scenario_serialization.py b/tests/datasets/motion_forecasting/test_scenario_serialization.py
new file mode 100644
index 00000000..6da8cd37
--- /dev/null
+++ b/tests/datasets/motion_forecasting/test_scenario_serialization.py
@@ -0,0 +1,68 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for Argoverse scenario serialization."""
+
+from pathlib import Path
+from typing import List
+
+import numpy as np
+
+from av2.datasets.motion_forecasting import scenario_serialization
+from av2.datasets.motion_forecasting.data_schema import ArgoverseScenario, ObjectState, ObjectType, Track, TrackCategory
+
+# Build test ArgoverseScenario
+_TEST_OBJECT_STATES: List[ObjectState] = [
+    ObjectState(observed=True, timestep=0, position=(0.0, 0.0), heading=0.0, velocity=(0.0, 0.0)),
+    ObjectState(observed=True, timestep=1, position=(1.0, 1.0), heading=0.0, velocity=(1.0, 1.0)),
+    ObjectState(observed=True, timestep=2, position=(2.0, 2.0), heading=0.0, velocity=(2.0, 2.0)),
+]
+_TEST_TRACKS: List[Track] = [
+    Track(
+        track_id="0",
+        object_states=_TEST_OBJECT_STATES,
+        object_type=ObjectType.VEHICLE,
+        category=TrackCategory.FOCAL_TRACK,
+    ),
+    Track(
+        track_id="1",
+        object_states=_TEST_OBJECT_STATES,
+        object_type=ObjectType.VEHICLE,
+        category=TrackCategory.SCORED_TRACK,
+    ),
+]
+_TEST_SCENARIO = ArgoverseScenario(
+    scenario_id="test",
+    timestamps_ns=np.arange(3),
+    tracks=_TEST_TRACKS,
+    focal_track_id="test_track",
+    city_name="pittsburgh",
+    map_id=0,
+    slice_id="dummy_slice",
+)
+
+
+def test_parquet_scenario_serialization_roundtrip(tmpdir: Path) -> None:
+    """Test serialization and deserialization of Argoverse scenarios in parquet format.
+
+    Args:
+        tmpdir: Temp directory used in the test (provided via built-in fixture).
+    """
+    # Serialize Argoverse scenario to parquet and save to disk
+    scenario_path = tmpdir / "test.parquet"
+    scenario_serialization.serialize_argoverse_scenario_parquet(scenario_path, _TEST_SCENARIO)
+    assert scenario_path.exists(), "Serialized Argoverse scenario not saved to disk."
+
+    # Check that loading and deserializing a parquet-formatted Argoverse scenario returns an equivalent object
+    loaded_test_scenario = scenario_serialization.load_argoverse_scenario_parquet(scenario_path)
+    assert loaded_test_scenario == _TEST_SCENARIO, "Deserialized Argoverse scenario did not match original object."
+
+
+def test_load_argoverse_scenario_parquet(test_data_root_dir: Path) -> None:
+    """Try to load a real scenario from the motion forecasting dataset."""
+    test_scenario_id = "0a1e6f0a-1817-4a98-b02e-db8c9327d151"
+    test_scenario_path = (
+        test_data_root_dir / "forecasting_scenarios" / test_scenario_id / f"scenario_{test_scenario_id}.parquet"
+    )
+
+    test_scenario = scenario_serialization.load_argoverse_scenario_parquet(test_scenario_path)
+    assert test_scenario.scenario_id == test_scenario_id
diff --git a/tests/datasets/sensor/test_av2_sensor_dataloader.py b/tests/datasets/sensor/test_av2_sensor_dataloader.py
new file mode 100644
index 00000000..5085f30a
--- /dev/null
+++ b/tests/datasets/sensor/test_av2_sensor_dataloader.py
@@ -0,0 +1,54 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests on Argoverse 2.0 Sensor Dataset dataloader."""
+
+from pathlib import Path
+
+import numpy as np
+
+from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayFloat
+
+
+def test_get_subsampled_ego_trajectory(test_data_root_dir: Path) -> None:
+    """Ensure we can sample the poses at a specific frequency.
+
+    Args:
+        test_data_root_dir: Path to the root dir for test data (provided via fixture).
+    """
+    log_id = "adcf7d18-0510-35b0-a2fa-b4cea13a6d76"
+
+    dataroot = test_data_root_dir / "sensor_dataset_logs"
+    loader = AV2SensorDataLoader(data_dir=dataroot, labels_dir=dataroot)
+
+    # retrieve every pose! (sub-nanosecond precision)
+    traj_ns = loader.get_subsampled_ego_trajectory(log_id=log_id, sample_rate_hz=1e9)
+
+    assert traj_ns.shape == (2637, 2)
+
+    # retrieve poses @ 1 Hz
+    traj_1hz = loader.get_subsampled_ego_trajectory(log_id=log_id, sample_rate_hz=1)
+
+    # 16 second log segment.
+    assert traj_1hz.shape == (16, 2)
+
+
+def test_get_city_SE3_ego(test_data_root_dir: Path) -> None:
+    """Ensure we can obtain the egovehicle's pose in the city coordinate frame at a specific timestamp.
+
+    Args:
+        test_data_root_dir: Path to the root dir for test data (provided via fixture).
+    """
+    log_id = "adcf7d18-0510-35b0-a2fa-b4cea13a6d76"
+
+    timestamp_ns = 315973157899927216
+
+    dataroot = test_data_root_dir / "sensor_dataset_logs"
+    loader = AV2SensorDataLoader(data_dir=dataroot, labels_dir=dataroot)
+
+    city_SE3_egovehicle = loader.get_city_SE3_ego(log_id=log_id, timestamp_ns=timestamp_ns)
+
+    assert isinstance(city_SE3_egovehicle, SE3)
+    expected_translation: NDArrayFloat = np.array([1468.87, 211.51, 13.14])
+    assert np.allclose(city_SE3_egovehicle.translation, expected_translation, atol=1e-2)
diff --git a/tests/datasets/sensor/test_sensor_dataloader.py b/tests/datasets/sensor/test_sensor_dataloader.py
new file mode 100644
index 00000000..98a31e91
--- /dev/null
+++ b/tests/datasets/sensor/test_sensor_dataloader.py
@@ -0,0 +1,94 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests on sensor data synchronization utilities."""
+
+import os
+from pathlib import Path
+
+from av2.datasets.sensor.sensor_dataloader import SensorDataloader
+
+
+def test_sensor_data_loader_milliseconds(tmpdir: "os.PathLike[str]") -> None:
+    """Test that the sensor dataset dataloader can synchronize lidar and image data.
+
+    Given toy data in milliseconds, we write out dummy files at corresponding timestamps.
+
+    (Sensor timestamps are real, and come from log 00a6ffc1-6ce9-3bc3-a060-6006e9893a1a).
+
+      sensor_name  timestamp_ns  ring_front_center  ...
+    0       lidar       2000000         42000000.0
+    1       lidar     102000000         92000000.0
+    2       lidar     202000000        192000000.0
+    3       lidar     303000000        292000000.0
+    4       lidar     402000000        392000000.0
+    5       lidar     502000000        492000000.0
+    6       lidar     603000000                NaN
+    7       lidar     702000000                NaN
+    8       lidar     802000000                NaN
+    9       lidar     903000000                NaN
+
+    Args:
+        tmpdir: Temp directory used in the test (provided via built-in fixture).
+    """
+    tmpdir = Path(tmpdir)
+    log_id = "00a6ffc1-6ce9-3bc3-a060-6006e9893a1a"
+
+    # 7x10 images, and 10 sweeps. Timestamps below given in human-readable milliseconds.
+    sensor_timestamps_ms_dict = {
+        "ring_rear_left": [0, 50, 100, 150, 200, 250, 300, 350, 400, 450],
+        "ring_side_left": [15, 65, 115, 165, 215, 265, 315, 365, 415, 465],
+        "ring_front_left": [30, 80, 130, 180, 230, 280, 330, 380, 430, 480],
+        "ring_front_center": [42, 92, 142, 192, 242, 292, 342, 392, 442, 492],
+        "ring_front_right": [5, 55, 105, 155, 205, 255, 305, 355, 405, 455],
+        "ring_side_right": [20, 70, 120, 170, 220, 270, 320, 370, 420, 470],
+        "ring_rear_right": [35, 85, 135, 185, 235, 285, 335, 385, 435, 485],
+        "lidar": [2, 102, 202, 303, 402, 502, 603, 702, 802, 903],
+    }
+
+    for sensor_name, timestamps_ms in sensor_timestamps_ms_dict.items():
+        for t in timestamps_ms:
+            if "ring" in sensor_name:
+                fpath = tmpdir / log_id / "sensors" / "cameras" / sensor_name / f"{int(t*1e6)}.jpg"
+            elif "lidar" in sensor_name:
+                fpath = tmpdir / log_id / "sensors" / sensor_name / f"{int(t*1e6)}.feather"
+            fpath.parent.mkdir(exist_ok=True, parents=True)
+            # create an empty file
+            f = open(fpath, "w")
+            f.close()
+
+    loader = SensorDataloader(sensor_dataset_dir=tmpdir, with_cache=False)
+
+    #  LiDAR 402 -> matches to ring front center 392.
+    img_fpath = loader.get_closest_img_fpath(
+        log_id=log_id, cam_name="ring_front_center", lidar_timestamp_ns=int(402 * 1e6)
+    )
+    assert isinstance(img_fpath, Path)
+    # result should be 392 milliseconds (and then a conversion to nanoseconds by adding 6 zeros)
+    print(img_fpath)
+    assert img_fpath.name == "392" + "000000" + ".jpg"
+
+    # nothing should be within bounds for this (valid lidar timestamp 903)
+    img_fpath = loader.get_closest_img_fpath(
+        log_id=log_id, cam_name="ring_front_center", lidar_timestamp_ns=int(903 * 1e6)
+    )
+    assert img_fpath is None
+
+    # nothing should be within bounds for this (invalid lidar timestamp 904)
+    img_fpath = loader.get_closest_img_fpath(
+        log_id=log_id, cam_name="ring_front_center", lidar_timestamp_ns=int(904 * 1e6)
+    )
+    assert img_fpath is None
+
+    # ring front center 392 -> matches to LiDAR 402.
+    lidar_fpath = loader.get_closest_lidar_fpath(
+        log_id=log_id, cam_name="ring_front_center", cam_timestamp_ns=int(392 * 1e6)
+    )
+    assert isinstance(lidar_fpath, Path)
+    # result should be 402 milliseconds (and then a conversion to nanoseconds by adding 6 zeros)
+    assert lidar_fpath.name == "402" + "000000.feather"
+
+    # way outside of bounds
+    lidar_fpath = loader.get_closest_lidar_fpath(
+        log_id=log_id, cam_name="ring_front_center", cam_timestamp_ns=int(7000 * 1e6)
+    )
+    assert lidar_fpath is None
diff --git a/tests/geometry/data/b87683ae-14c5-321f-8af3-623e7bafc3a7/annotations.feather b/tests/geometry/data/b87683ae-14c5-321f-8af3-623e7bafc3a7/annotations.feather
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diff --git a/tests/geometry/test_geometry.py b/tests/geometry/test_geometry.py
new file mode 100644
index 00000000..a6330b8e
--- /dev/null
+++ b/tests/geometry/test_geometry.py
@@ -0,0 +1,524 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for geometry utilities."""
+
+from pathlib import Path
+from typing import Any, Callable, Tuple
+
+import numpy as np
+import pytest
+from scipy.spatial.transform import Rotation
+
+import av2.geometry.geometry as geometry_utils
+from av2.datasets.sensor.constants import AnnotationCategories
+from av2.geometry.geometry import mat_to_xyz, xyz_to_mat
+from av2.geometry.se3 import SE3
+from av2.structures.cuboid import Cuboid, CuboidList
+from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt
+
+
+@pytest.mark.parametrize(
+    "yaw_1, yaw_2, expected_error_deg",
+    [
+        (179, -179, 2.0),
+        (-179, 179, 2.0),
+        (179, 178, 1.0),
+        (178, 179, 1.0),
+        (3, -3, 6.0),
+        (-3, 3, 6.0),
+        (-177, -179, 2.0),
+        (-179, -177, 2.0),
+    ],
+    ids=[f"Wrap Angles (Test Case: {idx + 1})" for idx in range(8)],
+)
+def test_wrap_angles(yaw_1: float, yaw_2: float, expected_error_deg: float) -> None:
+    """Test angle mapping (in radians) from domain [-∞, ∞] to [0, π).
+
+    Args:
+        yaw_1: Angle 1 in degrees.
+        yaw_2: Angle 2 in degrees.
+        expected_error_deg: Expected error mapped to the interval [0, π) in degrees.
+    """
+    yaw1 = np.deg2rad([yaw_1]).astype(np.float64)
+    yaw2 = np.deg2rad([yaw_2]).astype(np.float64)
+
+    error_deg = np.rad2deg(geometry_utils.wrap_angles(yaw1 - yaw2))
+    assert np.allclose(error_deg, expected_error_deg, atol=1e-2)
+
+
+def test_cart_to_hom_2d() -> None:
+    """Convert 2d cartesian coordinates to homogenous, and back again."""
+    cart: NDArrayFloat = np.arange(16 * 2).reshape(16, 2).astype(np.float64)
+
+    hom = geometry_utils.cart_to_hom(cart=cart)
+    cart_ = geometry_utils.hom_to_cart(hom=hom)
+
+    assert np.array_equal(cart, cart_)
+
+
+def test_cart_to_hom_3d() -> None:
+    """Convert 3d cartesian coordinates to homogenous, and back again."""
+    cart: NDArrayFloat = np.arange(16 * 3).reshape(16, 3).astype(np.float64)
+
+    hom = geometry_utils.cart_to_hom(cart=cart)
+    cart_ = geometry_utils.hom_to_cart(hom=hom)
+
+    assert np.array_equal(cart, cart_)
+
+
+@pytest.mark.parametrize(
+    "xy, width, height, expected_uv",
+    [
+        (
+            np.array([[0, 0], [0, 1], [1, 1], [1, 0]]).astype(np.float64),
+            1,
+            1,
+            np.array([[0, 0], [0, -1], [-1, -1], [-1, 0]]).astype(np.float64),
+        ),
+        (
+            np.array([[0, 0], [0, 1], [1, 1], [1, 0]]).astype(np.float64),
+            2,
+            0,
+            np.array([[1, -1], [1, -2], [0, -2], [0, -1]]).astype(np.float64),
+        ),
+    ],
+    ids=[f"Cartesian to texture coordinates conversion (Test Case: {idx + 1})" for idx in range(2)],
+)
+def test_xy_to_uv(xy: NDArrayFloat, width: int, height: int, expected_uv: NDArrayFloat) -> None:
+    """Test conversion of coordinates in R^2 (x,y) to texture coordinates (u,v) in R^2.
+
+    Args:
+        xy: (...,2) array of coordinates in R^2 (x,y).
+        width: Texture grid width.
+        height: Texture grid height.
+        expected_uv: Expected (...,2) array of texture / pixel coordinates.
+    """
+    assert np.array_equal(expected_uv, geometry_utils.xy_to_uv(xy, width, height))
+
+
+@pytest.mark.parametrize(
+    "quat_wxyz",
+    [
+        (np.array([0, 0, 0, 1]).astype(np.float64)),
+        (np.array([[0, 0, 0, 1], [0, 0, 0.70710678, 0.70710678]]).astype(np.float64)),
+    ],
+    ids=[f"Quaternion to Matrix conversion (Test Case: {idx + 1})" for idx in range(2)],
+)
+def test_quat_to_mat_3d(quat_wxyz: NDArrayFloat) -> None:
+    """Test conversion of quaternion to its matrix representation, and back again.
+
+    Args:
+        quat_wxyz: (...,4) array of quaternions in scalar first order.
+    """
+    # Quaternion to Quaternion round-trip conversion.
+    # (Note: For comparison, Quaternion needs to be converted from scalar last to scalar first.)
+    quat_to_quat: Callable[[NDArrayFloat], Any] = lambda quat_wxyz: Rotation.as_quat(
+        Rotation.from_matrix(geometry_utils.quat_to_mat(quat_wxyz))
+    )[..., [3, 0, 1, 2]]
+
+    assert np.allclose(quat_wxyz, quat_to_quat(quat_wxyz))
+
+
+@pytest.mark.parametrize(
+    "cart_xyz, expected_sph_theta_phi_r",
+    [
+        (np.array([1, 1, 1]).astype(np.float64), np.array([0.78539816, 0.61547971, 1.73205081])),
+        (
+            np.array([[1, 1, 1], [1, 2, 0]]).astype(np.float64),
+            np.array([[0.78539816, 0.61547971, 1.73205081], [1.10714872, 0.0, 2.23606798]]),
+        ),
+    ],
+    ids=[f"Cartesian to Spherical coodinates (Test Case: {idx + 1})" for idx in range(2)],
+)
+def test_cart_to_sph_3d(cart_xyz: NDArrayFloat, expected_sph_theta_phi_r: NDArrayFloat) -> None:
+    """Test conversion of cartesian coordinates to spherical coordinates.
+
+    Args:
+        cart_xyz: (...,3) Array of points (x,y,z) in Cartesian space.
+        expected_sph_theta_phi_r: (...,3) Array in spherical space. [Order: (azimuth, inclination, radius)].
+    """
+    assert np.allclose(expected_sph_theta_phi_r, geometry_utils.cart_to_sph(cart_xyz))
+
+
+@pytest.mark.parametrize(
+    "points_xyz, lower_bound_inclusive, upper_bound_exclusive, expected_crop_points, expected_mask",
+    [
+        (
+            np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1], [0, 0, 1], [1, 0, 1], [1, 1, 1]]).astype(
+                np.int64
+            ),
+            (0, 0, 0),
+            (1.5, 1.5, 1.5),
+            np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1], [0, 0, 1], [1, 0, 1], [1, 1, 1]]).astype(
+                np.int64
+            ),
+            np.array([True] * 8),
+        ),
+        (
+            np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1], [0, 0, 1], [1, 0, 1], [1, 1, 1]]).astype(
+                np.int64
+            ),
+            (0, 0, 0),
+            (0.5, 0.5, 0.5),
+            np.array([[0, 0, 0]]).astype(np.int64),
+            np.array([True] + [False] * 7),
+        ),
+        (
+            np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1], [0, 0, 1], [1, 0, 1], [1, 1, 1]]).astype(
+                np.int64
+            ),
+            (0, 0, 0),
+            (1.25, 1.25, 1.0),
+            np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]]).astype(np.int64),
+            np.array([True] * 4 + [False] * 4),
+        ),
+        (
+            np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1], [0, 0, 1], [1, 0, 1], [1, 1, 1]]).astype(
+                np.int64
+            ),
+            (-1.0, -1.0, -1.0),
+            (0.0, 1.0, 1.0),
+            np.empty((0, 3)).astype(np.int64),
+            np.array([False] * 8),
+        ),
+    ],
+    ids=[
+        "All points fall within the cropping region.",
+        "Only 1 point falls within the cropping region.",
+        "Few points fall within the cropping region.",
+        "No points fall within the cropping region.",
+    ],
+)
+def test_crop_points(
+    points_xyz: NDArrayInt,
+    lower_bound_inclusive: Tuple[float, float, float],
+    upper_bound_exclusive: Tuple[float, float, float],
+    expected_crop_points: NDArrayInt,
+    expected_mask: NDArrayBool,
+) -> None:
+    """Test cropping cartesian coordinates based on lower and upper boundary.
+
+    Args:
+        points_xyz: (...,3) Cartesian coordinates (x,y,z).
+        lower_bound_inclusive: (3,) Cartesian coordinates lower bound (inclusive).
+        upper_bound_exclusive: (3,) Cartesian coordinates upper bound (exclusive).
+        expected_crop_points: (...,3) Expected tuple of cropped Cartesian coordinates.
+        expected_mask: (...,) Expected boolean mask.
+    """
+    cropped_xyz, mask = geometry_utils.crop_points(points_xyz, lower_bound_inclusive, upper_bound_exclusive)
+
+    np.testing.assert_array_equal(expected_crop_points, cropped_xyz)  # type: ignore
+    np.testing.assert_array_equal(expected_mask, mask)  # type: ignore
+
+
+@pytest.mark.parametrize(
+    "points_xyz, expected_is_interior",
+    [
+        (
+            np.array(
+                [
+                    [0.25, 0.25, 0.25],
+                    [0.5, 0.5, 0.5],
+                    [0.75, 0.75, 0.75],
+                ]
+            ).astype(np.float64),
+            np.array([True] * 3),
+        ),
+        (
+            np.array(
+                [
+                    [0.0, 0.0, 0.0],
+                    [0.25, 0.25, 0.25],
+                    [0.5, 0.5, 0.5],
+                    [0.75, 0.75, 0.75],
+                    [1.0, 1.0, 1.0],
+                ]
+            ).astype(np.float64),
+            np.array([True] * 5),
+        ),
+        (
+            np.array(
+                [
+                    [-0.5, -0.5, -0.5],
+                    [0.0, 0.0, 0.0],
+                    [0.5, 0.5, 0.5],
+                    [1.0, 1.0, 1.0],
+                    [1.5, 1.5, 1.5],
+                ]
+            ).astype(np.float64),
+            np.array([False] + [True] * 3 + [False]),
+        ),
+        (
+            np.array(
+                [
+                    [-1.0, -1.0, -1.0],
+                    [-0.5, -0.5, -0.5],
+                    [1.5, 0.5, 0.5],
+                    [2.0, 2.0, 2.0],
+                ]
+            ).astype(np.float64),
+            np.array([False] * 4),
+        ),
+    ],
+    ids=[
+        "All points lie inside the bounding box.",
+        "All points lie inside the bounding box (with boundary included).",
+        "Few points lie inside the bounding box.",
+        "No points lie inside the bounding box.",
+    ],
+)
+def test_compute_interior_points_mask(points_xyz: NDArrayFloat, expected_is_interior: NDArrayBool) -> None:
+    r"""Test finding the points interior to an axis-aligned cuboid.
+
+    Reference: https://math.stackexchange.com/questions/1472049/check-if-a-point-is-inside-a-rectangular-shaped-area-3d
+
+            5------4
+            |\\    |\\
+            | \\   | \\
+            6--\\--7  \\
+            \\  \\  \\ \\
+        l    \\  1-------0    h
+         e    \\ ||   \\ ||   e
+          n    \\||    \\||   i
+           g    \\2------3    g
+            t      width.     h
+             h.               t.
+
+    Args:
+        points_xyz: (N,3) Array of points.
+        expected_is_interior: (N,) Array of booleans whether the points are interior to the cuboid
+            defined below.
+    """
+    rotation: NDArrayFloat = np.eye(3)
+    translation: NDArrayFloat = np.array([0.5, 0.5, 0.5])
+    egovehicle_SE3_ego = SE3(rotation=rotation, translation=translation)
+    cuboid = Cuboid(
+        dst_SE3_object=egovehicle_SE3_ego,
+        length_m=1.0,
+        width_m=1.0,
+        height_m=1.0,
+        category=AnnotationCategories.REGULAR_VEHICLE,
+        timestamp_ns=0,
+    )
+    is_interior = geometry_utils.compute_interior_points_mask(points_xyz, cuboid.vertices_m)
+    assert np.array_equal(is_interior, expected_is_interior)
+
+
+def test_benchmark_compute_interior_points_mask_optimized(benchmark: Callable[..., Any]) -> None:
+    """Benchmark compute_interior_pts on 100000 random points."""
+    rotation: NDArrayFloat = np.eye(3)
+    translation: NDArrayFloat = np.array([0.5, 0.5, 0.5])
+    egovehicle_SE3_ego = SE3(rotation=rotation, translation=translation)
+    cuboid = Cuboid(
+        dst_SE3_object=egovehicle_SE3_ego,
+        length_m=1.0,
+        width_m=1.0,
+        height_m=1.0,
+        category=AnnotationCategories.REGULAR_VEHICLE,
+        timestamp_ns=0,
+    )
+
+    N = 100000
+    points_xyz: NDArrayFloat = 100.0 * np.random.rand(N, 3)
+    benchmark(geometry_utils.compute_interior_points_mask, points_xyz, cuboid.vertices_m)
+
+
+def test_benchmark_compute_interior_points_mask_slow(benchmark: Callable[..., Any]) -> None:
+    """Benchmark compute_interior_points_mask on 100000 random points."""
+    rotation: NDArrayFloat = np.eye(3)
+    translation: NDArrayFloat = np.array([0.5, 0.5, 0.5])
+    egovehicle_SE3_ego = SE3(rotation=rotation, translation=translation)
+    cuboid = Cuboid(
+        dst_SE3_object=egovehicle_SE3_ego,
+        length_m=1.0,
+        width_m=1.0,
+        height_m=1.0,
+        category=AnnotationCategories.REGULAR_VEHICLE,
+        timestamp_ns=0,
+    )
+
+    N = 100000
+    points_xyz: NDArrayFloat = 100.0 * np.random.rand(N, 3)
+
+    def compute_interior_points_mask_slow(points_xyz: NDArrayFloat, cuboid_vertices: NDArrayFloat) -> NDArrayBool:
+        """Compute the interior points mask with the older slow version.
+
+        Args:
+            points_xyz: (N,3) Array representing a point cloud in Cartesian coordinates (x,y,z).
+            cuboid_vertices: (8,3) Array representing 3D cuboid vertices, ordered as shown below.
+
+        Returns:
+            (N,) An array of boolean flags indicating whether the points are interior to the cuboid.
+        """
+        u = cuboid_vertices[2] - cuboid_vertices[6]
+        v = cuboid_vertices[2] - cuboid_vertices[3]
+        w = cuboid_vertices[2] - cuboid_vertices[1]
+
+        # point x lies within the box when the following
+        # constraints are respected
+        valid_u1 = np.logical_and(
+            u.dot(cuboid_vertices[2]) <= points_xyz.dot(u), points_xyz.dot(u) <= u.dot(cuboid_vertices[6])
+        )
+        valid_v1 = np.logical_and(
+            v.dot(cuboid_vertices[2]) <= points_xyz.dot(v), points_xyz.dot(v) <= v.dot(cuboid_vertices[3])
+        )
+        valid_w1 = np.logical_and(
+            w.dot(cuboid_vertices[2]) <= points_xyz.dot(w), points_xyz.dot(w) <= w.dot(cuboid_vertices[1])
+        )
+
+        valid_u2 = np.logical_and(
+            u.dot(cuboid_vertices[2]) >= points_xyz.dot(u), points_xyz.dot(u) >= u.dot(cuboid_vertices[6])
+        )
+        valid_v2 = np.logical_and(
+            v.dot(cuboid_vertices[2]) >= points_xyz.dot(v), points_xyz.dot(v) >= v.dot(cuboid_vertices[3])
+        )
+        valid_w2 = np.logical_and(
+            w.dot(cuboid_vertices[2]) >= points_xyz.dot(w), points_xyz.dot(w) >= w.dot(cuboid_vertices[1])
+        )
+
+        valid_u = np.logical_or(valid_u1, valid_u2)
+        valid_v = np.logical_or(valid_v1, valid_v2)
+        valid_w = np.logical_or(valid_w1, valid_w2)
+
+        is_interior: NDArrayBool = np.logical_and(np.logical_and(valid_u, valid_v), valid_w)
+        return is_interior
+
+    benchmark(compute_interior_points_mask_slow, points_xyz, cuboid.vertices_m)
+
+
+def test_xyz_to_mat_matrix() -> None:
+    """Unit test for converting Tait-Bryan angles to rotation matrix."""
+    tait_bryan_angles: NDArrayFloat = np.deg2rad([45.0, 45.0, 45.0])
+    rotation_matrix = xyz_to_mat(tait_bryan_angles)
+    rotation_matrix_expected = [  # (45, 45, 45) about the xyz axes (roll, pitch, yaw).
+        [0.5000000, -0.1464466, 0.8535534],
+        [0.5000000, 0.8535534, -0.1464466],
+        [-0.7071068, 0.5000000, 0.5000000],
+    ]
+    np.testing.assert_allclose(rotation_matrix, rotation_matrix_expected)  # type: ignore
+
+
+def test_xyz_to_mat_round_trip() -> None:
+    """Unit test for converting Tait-Bryan angles to rotation matrix."""
+    tait_bryan_angles: NDArrayFloat = np.deg2rad([45.0, 45.0, 45.0])
+    rotation_matrix = xyz_to_mat(tait_bryan_angles)
+    tait_bryan_angles_ = mat_to_xyz(rotation_matrix)
+    np.testing.assert_allclose(tait_bryan_angles, tait_bryan_angles_)  # type: ignore
+
+
+def test_mat_to_xyz_round_trip() -> None:
+    """Unit test for converting Tait-Bryan angles to rotation matrix."""
+    rotation_matrix: NDArrayFloat = np.array(
+        [  # (45, 0, 90) about the xyz axes (roll, pitch, yaw).
+            [0.0000000, -0.7071068, 0.7071068],
+            [1.0000000, 0.0000000, -0.0000000],
+            [0.0000000, 0.7071068, 0.7071068],
+        ]
+    )
+    tait_bryan_angles = mat_to_xyz(rotation_matrix)
+    rotation_matrix_ = xyz_to_mat(tait_bryan_angles)
+    np.testing.assert_allclose(rotation_matrix, rotation_matrix_, atol=1e-10)  # type: ignore
+
+
+def test_mat_to_xyz_constrained() -> None:
+    """Unit test for constraining Tait-Bryan angles and converting to rotation matrix."""
+    rotation_matrix: NDArrayFloat = np.array(
+        [  # (45, 0, 90) about the xyz axes (roll, pitch, yaw).
+            [0.0000000, -0.7071068, 0.7071068],
+            [1.0000000, 0.0000000, -0.0000000],
+            [0.0000000, 0.7071068, 0.7071068],
+        ]
+    )
+    xyz = mat_to_xyz(rotation_matrix)
+    xyz[0] = 0  # Set roll to zero.
+    mat_constrained = xyz_to_mat(xyz)
+
+    xyz_expected = np.deg2rad([0, 0, 90])  # [45, 0, 90] -> constrain roll to zero -> [0, 0, 90].
+    mat_constrained_expected = xyz_to_mat(xyz_expected)
+    np.testing.assert_allclose(mat_constrained, mat_constrained_expected)  # type: ignore
+
+
+def RzRyRx(x: float, y: float, z: float) -> NDArrayFloat:
+    """Convert rotation angles about 3 orthogonal axes to a 3d rotation matrix.
+
+    Computes:
+        R = Rz(z) * Ry(y) * Rx(x)
+
+    For a derivation, see page 2 of http://eecs.qmul.ac.uk/~gslabaugh/publications/euler.pdf
+
+    Reference: GTSAM
+    See: https://github.com/borglab/gtsam/blob/develop/gtsam/geometry/Rot3M.cpp#L85
+
+    Args:
+        x: roll angle, in radians.
+        y: pitch angle, in radians.
+        z: yaw angle, in radians.
+
+    Returns:
+        The 3d rotation matrix.
+    """
+    cx = np.cos(x)
+    cy = np.cos(y)
+    cz = np.cos(z)
+
+    sx = np.sin(x)
+    sy = np.sin(y)
+    sz = np.sin(z)
+
+    ss_ = sx * sy
+    cs_ = cx * sy
+    sc_ = sx * cy
+    cc_ = cx * cy
+    c_s = cx * sz
+    s_s = sx * sz
+    _cs = cy * sz
+    _cc = cy * cz
+    s_c = sx * cz
+    c_c = cx * cz
+    ssc = ss_ * cz
+    csc = cs_ * cz
+    sss = ss_ * sz
+    css = cs_ * sz
+
+    # fmt: off
+    R: NDArrayFloat = np.array([
+        [_cc, -c_s + ssc,  s_s + csc],
+        [_cs,  c_c + sss, -s_c + css],
+        [-sy,        sc_,        cc_]
+    ])
+    # fmt: on
+    return R
+
+
+def test_xyz_to_mat_vs_gtsam() -> None:
+    """Compare our implementation (using Scipy) vs. the GTSAM derivation."""
+    num_iters = 10000
+    for _ in range(num_iters):
+
+        # in [-pi, pi]
+        x = 2 * np.pi * (np.random.rand() - 0.5)
+        z = 2 * np.pi * (np.random.rand() - 0.5)
+
+        # in [-pi/2, pi/2]
+        y = np.pi * (np.random.rand() - 0.5)
+
+        xyz: NDArrayFloat = np.array([x, y, z], dtype=float)
+        R = xyz_to_mat(xyz)
+
+        R_gtsam = RzRyRx(x, y, z)
+        assert np.allclose(R, R_gtsam)
+
+
+def test_constrain_cuboid_pose() -> None:
+    """Unit test to constrain cuboid pose."""
+    path = Path(__file__).parent.resolve() / "data" / "b87683ae-14c5-321f-8af3-623e7bafc3a7" / "annotations.feather"
+    cuboid_list = CuboidList.from_feather(path)
+    for cuboid in cuboid_list.cuboids:
+        pose = cuboid.dst_SE3_object
+        roll, pitch, yaw = mat_to_xyz(pose.rotation)
+        pitch, roll = 0, 0
+        xyz: NDArrayFloat = np.array([roll, pitch, yaw], dtype=float)
+        cuboid.dst_SE3_object.rotation = xyz_to_mat(xyz)
+        R = xyz_to_mat(xyz)
+        R_ = RzRyRx(0, 0, yaw)
+        assert np.allclose(R, R_)
diff --git a/tests/geometry/test_interpolate.py b/tests/geometry/test_interpolate.py
new file mode 100644
index 00000000..aad85044
--- /dev/null
+++ b/tests/geometry/test_interpolate.py
@@ -0,0 +1,623 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for polyline interpolation utilities."""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.spatial.transform import Rotation
+
+import av2.geometry.interpolate as interp_utils
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayFloat
+
+
+def test_compute_lane_width_straight() -> None:
+    """Compute the lane width of the following straight lane segment.
+
+    (waypoints indicated with "o" symbol):
+
+            o   o
+            |   |
+            o   o
+            |   |
+            o   o
+
+    We can swap boundaries for this lane, and the width should be identical.
+    """
+    left_even_pts: NDArrayFloat = np.array([[1, 1], [1, 0], [1, -1]])
+    right_even_pts: NDArrayFloat = np.array([[-1, 1], [-1, 0], [-1, -1]])
+    lane_width = interp_utils.compute_lane_width(left_even_pts, right_even_pts)
+    gt_lane_width = 2.0
+    assert np.isclose(lane_width, gt_lane_width)
+
+    lane_width = interp_utils.compute_lane_width(right_even_pts, left_even_pts)
+    gt_lane_width = 2.0
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_lane_width_telescoping() -> None:
+    r"""Compute the lane width of the following straight lane segment.
+
+    (waypoints indicated with "o" symbol):
+
+    right       left
+     o           o
+     \\         //
+      o        o
+       \\     //
+        o     o
+         \\ //
+           o
+
+    We can swap boundaries for this lane, and the width should be identical.
+    """
+    # fmt: off
+    left_even_pts: NDArrayFloat = np.array(
+        [
+            [3,  2],
+            [2,  1],
+            [1,  0],
+            [0, -1]
+        ]
+    )
+    right_even_pts: NDArrayFloat = np.array(
+        [
+            [-3,  2],
+            [-2,  1],
+            [-1,  0],
+            [ 0, -1]
+        ]
+    )
+    # fmt: on
+    lane_width = interp_utils.compute_lane_width(left_even_pts, right_even_pts)
+    gt_lane_width = (6.0 + 4.0 + 2.0 + 0.0) / 4
+    assert np.isclose(lane_width, gt_lane_width)
+
+    lane_width = interp_utils.compute_lane_width(right_even_pts, left_even_pts)
+    gt_lane_width = (6.0 + 4.0 + 2.0 + 0.0) / 4
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_lane_width_curved_width1() -> None:
+    r"""Compute the lane width of the following curved lane segment.
+
+    Should have width 1 at each pair of boundary waypoints.
+
+          -------boundary
+         /  ----boundary
+        /  /
+        |  |
+        |  \\
+         \\ -----
+          \\-----
+    """
+    left_even_pts: NDArrayFloat = np.array([[0, 2], [-2, 2], [-3, 1], [-3, 0], [-2, -1], [0, -1]])
+    right_even_pts: NDArrayFloat = np.array([[0, 3], [-2, 3], [-4, 1], [-4, 0], [-2, -2], [0, -2]])
+    lane_width = interp_utils.compute_lane_width(left_even_pts, right_even_pts)
+    gt_lane_width = 1.0
+    assert np.isclose(lane_width, gt_lane_width)
+
+    lane_width = interp_utils.compute_lane_width(right_even_pts, left_even_pts)
+    gt_lane_width = 1.0
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_lane_width_curved_not_width1() -> None:
+    r"""Compute the lane width of the following curved lane segment.
+
+          -------
+         /  ----
+        /  /
+        |  |
+        |  \\
+         \\ -----
+          \\-----
+
+    We get waypoint distances of [1,1,1,1,0.707..., 1,1]
+    """
+    left_even_pts: NDArrayFloat = np.array([[0, 2], [-2, 2], [-3, 1], [-3, 0], [-2.5, -0.5], [-2, -1], [0, -1]])
+
+    right_even_pts: NDArrayFloat = np.array([[0, 3], [-2, 3], [-4, 1], [-4, 0], [-3, -1], [-2, -2], [0, -2]])
+
+    lane_width = interp_utils.compute_lane_width(left_even_pts, right_even_pts)
+    gt_lane_width = 0.9581581115980783
+    assert np.isclose(lane_width, gt_lane_width)
+
+    lane_width = interp_utils.compute_lane_width(right_even_pts, left_even_pts)
+    gt_lane_width = 0.9581581115980783
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_mid_pivot_arc_3pt_cul_de_sac() -> None:
+    """Make sure we handle the cul-de-sac case correctly, for 2d polylines.
+
+    When mapping a cul-de-sac, we get a line of points on one boundary,
+    and a single point on the other side. This function produces the middle
+    arc we get by pivoting around the single point.
+
+    Waypoints are depicted below for the cul-de-sac center, and other boundary.
+
+            o
+             \
+              \
+               \
+            O   o
+               /
+              /
+             /
+            o
+    """
+    # Numpy array of shape (3,)
+    single_pt: NDArrayFloat = np.array([0, 0])
+
+    # Numpy array of shape (N,3)
+    arc_pts: NDArrayFloat = np.array([[0, 1], [1, 0], [0, -1]])
+
+    # centerline_pts: Numpy array of shape (N,3)
+    centerline_pts, lane_width = interp_utils.compute_mid_pivot_arc(single_pt, arc_pts)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0, 0.5], [0.5, 0], [0, -0.5]])
+    gt_lane_width = 1.0
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_mid_pivot_arc_3pt_cul_de_sac_3dpolylines() -> None:
+    """Make sure we handle the cul-de-sac case correctly, for 3d polylines.
+
+    When mapping a cul-de-sac, we get a line of points on one boundary,
+    and a single point on the other side. This function produces the middle
+    arc we get by pivoting around the single point.
+
+    Waypoints are depicted below for the cul-de-sac center, and other boundary.
+    The scenario is a banked turn, where the inside is low, and the outside polyline is higher.
+
+            o @ (0,1)
+              \
+            .   \
+                  \
+            O  .   o  @ (1,0)
+                  /
+            .   /
+              /
+            o @ (0,-1)
+    """
+    # Numpy array of shape (3,)
+    single_pt: NDArrayFloat = np.array([0, 0, 0])
+
+    # Numpy array of shape (N,3)
+    # fmt: off
+    arc_pts: NDArrayFloat = np.array(
+        [
+            [0, 1, 2],
+            [1, 0, 2],
+            [0, -1, 2]
+        ]
+    )
+    # fmt: on
+
+    # centerline_pts: Numpy array of shape (N,3)
+    centerline_pts, lane_width = interp_utils.compute_mid_pivot_arc(single_pt, arc_pts)
+
+    # z values should be halfway between outer arc and inner arc
+    gt_centerline_pts: NDArrayFloat = np.array([[0, 0.5, 1], [0.5, 0, 1], [0, -0.5, 1]])
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+
+    # compute hypotenuse using height 2 and width 1 -> 2^2 + 1^2 = 5^2
+    gt_lane_width = np.sqrt(5)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_mid_pivot_arc_5pt_cul_de_sac() -> None:
+    """Make sure we handle the cul-de-sac case correctly, for 2d polylines.
+
+    When mapping a cul-de-sac, we get a line of points on one boundary,
+    and a single point on the other side. This function produces the middle
+    arc we get by pivoting around the single point.
+
+    Waypoints are depicted below for the cul-de-sac center, and other boundary.
+
+            o
+             \
+              o
+               \
+            O   o
+               /
+              o
+             /
+            o
+    """
+    # Numpy array of shape (3,)
+    single_pt: NDArrayFloat = np.array([0, 0])
+
+    # Numpy array of shape (N, 3)
+    arc_pts: NDArrayFloat = np.array([[0, 2], [1, 1], [2, 0], [1, -1], [0, -2]])
+
+    # centerline_pts: Numpy array of shape (N,3)
+    centerline_pts, lane_width = interp_utils.compute_mid_pivot_arc(single_pt, arc_pts)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0, 1], [0.5, 0.5], [1, 0], [0.5, -0.5], [0, -1]])
+    gt_lane_width = (2 + 2 + 2 + np.sqrt(2) + np.sqrt(2)) / 5
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_cul_de_sac_right_onept() -> None:
+    """Compute midpoint line for a cul-de-sac with one right waypoint.
+
+    Make sure that if we provide left and right boundary polylines,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    left_ln_bnds: NDArrayFloat = np.array([[0, 2], [1, 1], [2, 0], [1, -1], [0, -2]])
+    right_ln_bnds: NDArrayFloat = np.array([[0, 0]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=5)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0, 1], [0.5, 0.5], [1, 0], [0.5, -0.5], [0, -1]])
+    gt_lane_width = (2 + 2 + 2 + np.sqrt(2) + np.sqrt(2)) / 5
+
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_cul_de_sac_left_onept() -> None:
+    """Compute midpoint line for a cul-de-sac with one left waypoint.
+
+    Make sure that if we provide left and right boundary polylines in 2d,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    right_ln_bnds: NDArrayFloat = np.array([[0, 2], [1, 1], [2, 0], [1, -1], [0, -2]])
+    left_ln_bnds: NDArrayFloat = np.array([[0, 0]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=5)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0, 1], [0.5, 0.5], [1, 0], [0.5, -0.5], [0, -1]])
+    gt_lane_width = (2 + 2 + 2 + np.sqrt(2) + np.sqrt(2)) / 5
+
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_straightline_maintain_5_waypts() -> None:
+    """Compute midpoint line for a straightline with five waypoints.
+
+    Make sure that if we provide left and right boundary polylines in 2d,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    right_ln_bnds: NDArrayFloat = np.array([[-1, 4], [-1, 2], [-1, 0], [-1, -2], [-1, -4]])
+    left_ln_bnds: NDArrayFloat = np.array([[2, 4], [2, 2], [2, 0], [2, -2], [2, -4]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=5)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0.5, 4], [0.5, 2], [0.5, 0], [0.5, -2], [0.5, -4]])
+    gt_lane_width = 3.0
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_straightline_maintain_4_waypts() -> None:
+    """Test computing midpoint line with 4 waypoints.
+
+    Make sure that if we provide left and right boundary polylines in 2d,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    right_ln_bnds: NDArrayFloat = np.array([[-1, 4], [-1, 2], [-1, 0], [-1, -2], [-1, -4]])
+    left_ln_bnds: NDArrayFloat = np.array([[2, 4], [2, 2], [2, 0], [2, -2], [2, -4]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=4)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0.5, 4], [0.5, 4 / 3], [0.5, -4 / 3], [0.5, -4]])
+    gt_lane_width = 3.0
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_straightline_maintain_3_waypts() -> None:
+    """Test computing midpoint line with 3 waypoints.
+
+    Make sure that if we provide left and right boundary polylines in 2d,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    right_ln_bnds: NDArrayFloat = np.array([[-1, 4], [-1, 2], [-1, 0], [-1, -2], [-1, -4]])
+    left_ln_bnds: NDArrayFloat = np.array([[2, 4], [2, 2], [2, 0], [2, -2], [2, -4]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=3)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0.5, 4], [0.5, 0], [0.5, -4]])
+    gt_lane_width = 3.0
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_straightline_maintain_2_waypts() -> None:
+    """Test computing midpoint line with 2 waympoints.
+
+    Make sure that if we provide left and right boundary polylines in 2d,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    right_ln_bnds: NDArrayFloat = np.array([[-1, 4], [-1, 2], [-1, 0], [-1, -2], [-1, -4]])
+    left_ln_bnds: NDArrayFloat = np.array([[2, 4], [2, 2], [2, 0], [2, -2], [2, -4]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=2)
+
+    gt_centerline_pts: NDArrayFloat = np.array([[0.5, 4], [0.5, -4]])
+    gt_lane_width = 3.0
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_compute_midpoint_line_curved_maintain_4_waypts() -> None:
+    """Test computing midpoint line.
+
+    Make sure that if we provide left and right boundary polylines in 2d,
+    we can get the correct centerline by averaging left and right waypoints.
+
+    Note that because of the curve and the arc interpolation, the land width and centerline in the middle points
+    will be shifted.
+    """
+    right_ln_bnds: NDArrayFloat = np.array([[-1, 3], [1, 3], [4, 0], [4, -2]])
+    left_ln_bnds: NDArrayFloat = np.array([[-1, 1], [1, 1], [2, 0], [2, -2]])
+
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=4)
+
+    # from argoverse.utils.mpl_plotting_utils import draw_polygon_mpl
+
+    # fig = plt.figure(figsize=(22.5, 8))
+    # ax = fig.add_subplot(111)
+
+    # draw_polygon_mpl(ax, right_ln_bnds, "g")
+    # draw_polygon_mpl(ax, left_ln_bnds, "b")
+    # draw_polygon_mpl(ax, centerline_pts, "r")
+
+    gt_centerline_pts: NDArrayFloat = np.array([[-1, 2], [1, 2], [3, 0], [3, -2]])
+
+    assert np.allclose(centerline_pts[0], gt_centerline_pts[0])
+    assert np.allclose(centerline_pts[-1], gt_centerline_pts[-1])
+
+
+def test_compute_midpoint_line_straightline_maintain_3_waypts_3dpolylines() -> None:
+    """Test computing correct centerline.
+
+    Make sure that if we provide left and right boundary polylines in 3d,
+    we can get the correct centerline by averaging left and right waypoints.
+    """
+    # fmt: off
+    # right side is lower, at z = 0
+    right_ln_bnds: NDArrayFloat = np.array(
+        [
+            [-1,  4, 0],
+            [-1,  2, 0],
+            [-1,  0, 0],
+            [-1, -2, 0],
+            [-1, -4, 0]
+        ]
+    )
+    # left side is higher, at z = 2
+    # fewer waypoint on left side, but shouldn't affect anything for a straight line
+    left_ln_bnds: NDArrayFloat = np.array(
+        [
+            [2,  4, 2],
+            [2, -2, 2],
+            [2, -4, 2]
+        ]
+    )
+
+    # fmt: on
+    centerline_pts, lane_width = interp_utils.compute_midpoint_line(left_ln_bnds, right_ln_bnds, num_interp_pts=3)
+    # fmt: off
+    gt_centerline_pts: NDArrayFloat = np.array(
+        [
+            [0.5,  4, 1],
+            [0.5,  0, 1],
+            [0.5, -4, 1]
+        ]
+    )
+    # fmt: on
+    assert np.allclose(centerline_pts, gt_centerline_pts)
+
+    # hypotenuse from width=3 on xy plane and z height diff of 3 -> 3**2 + 2**2 = 13
+    gt_lane_width = np.sqrt(13)
+    assert np.isclose(lane_width, gt_lane_width)
+
+
+def test_interp_arc_straight_line() -> None:
+    """Test arc interpolation on a straight line."""
+    pts: NDArrayFloat = np.array([[-10, 0], [10, 0]])
+    interp_pts = interp_utils.interp_arc(t=3, points=pts)
+    # fmt: off
+    gt_interp_pts: NDArrayFloat = np.array(
+        [
+            [-10, 0],
+            [  0, 0],
+            [ 10, 0]
+        ]
+    )
+    # fmt: on
+    assert np.allclose(interp_pts, gt_interp_pts)
+
+    interp_pts = interp_utils.interp_arc(t=4, points=pts)
+    # fmt: off
+    gt_interp_pts_: NDArrayFloat = np.array(
+        [
+            [-10,     0],
+            [-10 / 3, 0],
+            [ 10 / 3, 0],
+            [ 10,     0]
+        ]
+    )
+    # fmt: on
+    assert np.allclose(interp_pts, gt_interp_pts_)
+
+
+def test_interp_arc_straight_line_3d() -> None:
+    """Ensure that linear interpolation works in 3d."""
+    # fmt: off
+    pts: NDArrayFloat = np.array(
+        [
+            [-10, 0, -1],
+            [ 10, 0,  1],
+        ]
+    )
+    # fmt: on
+    interp_pts = interp_utils.interp_arc(t=3, points=pts)
+
+    # expect to get 3 waypoints along the straight line
+    # fmt: off
+    expected_interp_pts: NDArrayFloat = np.array(
+        [
+            [-10.,   0.,  -1.],
+            [  0.,   0.,   0.],
+            [ 10.,   0.,   1.]
+        ]
+    )
+    # fmt: on
+    assert np.allclose(interp_pts, expected_interp_pts)
+
+
+def test_interp_arc_straight_line_3d_5pts() -> None:
+    """Ensure that linear interpolation works in 3d, with 5 desired waypoints."""
+    # fmt: off
+    pts: NDArrayFloat = np.array(
+        [
+            [-10, 0, -1],
+            [ 10, 0,  1],
+        ]
+    )
+    # fmt: on
+    interp_pts = interp_utils.interp_arc(t=5, points=pts)
+
+    # expect to get 5 waypoints along the straight line
+    # fmt: off
+    expected_interp_pts: NDArrayFloat = np.array(
+        [
+            [-10, 0, -1],
+            [ -5, 0, -0.5],
+            [  0, 0,  0],
+            [  5, 0,  0.5],
+            [ 10, 0,  1]
+        ]
+    )
+    # fmt: on
+    assert np.allclose(interp_pts, expected_interp_pts)
+
+
+def test_interp_arc_curved_line_3d_5pts() -> None:
+    r"""Ensure that linear interpolation works for curves in 3d, with 5 desired waypoints.
+
+    Shape of interpolated polyline:
+        .        +       .
+         \\           //
+            .        .
+              \\   //
+                 .
+    """
+    # fmt: off
+    pts: NDArrayFloat = np.array(
+        [
+            [-10,   0, -1],
+            [  0, -10,  0.5],
+            [ 10,   0,  1],
+        ]
+    )
+    # fmt: on
+    interp_pts = interp_utils.interp_arc(t=5, points=pts)
+
+    # expect to get 5 waypoints along the straight line
+    # fmt: off
+    expected_interp_pts: NDArrayFloat = np.array(
+        [
+            [-10,   0, -1],
+            [ -5,  -5, -0.25],
+            [  0, -10,  0.5],
+            [  5,  -5,  0.75],
+            [ 10,   0,  1]
+        ]
+    )
+    # fmt: on
+    assert np.allclose(interp_pts, expected_interp_pts, atol=0.03)
+
+
+def test_interpolate_pose() -> None:
+    """Interpolate poses between the time interval [0,1].
+
+    Note: visualization may be turned on to understand the setup. See figure here:
+        https://user-images.githubusercontent.com/16724970/155865211-cee5f72c-a886-4d47-bc75-65feb3b41fe6.png
+
+    Two coordinate frames @t0 and @t1 are visualized in 2d below:
+
+    @t1
+       |
+    ___|
+       .
+       .     |
+    .........|___ @t0
+       .
+       .
+    """
+    visualize = False
+
+    city_SE3_egot0 = SE3(rotation=np.eye(3), translation=np.array([5, 0, 0]))
+    city_SE3_egot1 = SE3(
+        rotation=Rotation.from_euler("z", 90, degrees=True).as_matrix(), translation=np.array([0, 5, 0])
+    )
+
+    t0 = 0
+    t1 = 10
+    for query_timestamp in np.arange(11):
+        pose = interp_utils.interpolate_pose(
+            key_timestamps=(t0, t1), key_poses=(city_SE3_egot0, city_SE3_egot1), query_timestamp=query_timestamp
+        )
+        if visualize:
+            _plot_pose(pose)
+    if visualize:
+        plt.axis("equal")
+        plt.show()
+
+
+def _plot_pose(cTe: SE3) -> None:
+    """Visualize a pose by plotting x,y axes of a coordinate frame in red and green.
+
+    Args:
+        cTe: represents an egovehicle pose in the city frame.
+    """
+    x0_e: NDArrayFloat = np.array([[0, 0, 0]], dtype=float)
+    x0_c = cTe.transform_point_cloud(x0_e).squeeze()
+
+    x1_e: NDArrayFloat = np.array([[1, 0, 0]], dtype=float)
+    x1_c = cTe.transform_point_cloud(x1_e).squeeze()
+
+    x2_e: NDArrayFloat = np.array([[0, 1, 0]], dtype=float)
+    x2_c = cTe.transform_point_cloud(x2_e).squeeze()
+
+    plt.plot([x0_c[0], x1_c[0]], [x0_c[1], x1_c[1]], color="r")
+    plt.plot([x0_c[0], x2_c[0]], [x0_c[1], x2_c[1]], color="g")
+
+
+def test_linear_interpolation() -> None:
+    """Ensure we can linear interpolation positions in an interval [t0,t1].
+
+    Locations marked by "O" below:
+    @t1
+       |
+     O |
+       \
+    ---|-O--
+       | @ t0
+    """
+    X0: NDArrayFloat = np.array([1, 0, 0], dtype=float)
+    X1: NDArrayFloat = np.array([-1, 2, 10], dtype=float)
+
+    # at start of interval (@5 sec)
+    Xt_5 = interp_utils.linear_interpolation(key_timestamps=(5, 15), key_translations=(X0, X1), query_timestamp=5)
+    expected_Xt_5: NDArrayFloat = np.array([1, 0, 0], dtype=float)
+    assert np.array_equal(Xt_5, expected_Xt_5)
+
+    # midway through interval (@10 sec)
+    Xt_10 = interp_utils.linear_interpolation(key_timestamps=(5, 15), key_translations=(X0, X1), query_timestamp=10)
+    expected_Xt_10: NDArrayFloat = np.array([0, 1, 5], dtype=float)
+    assert np.array_equal(Xt_10, expected_Xt_10)
+
+    # at end of interval (@15 sec)
+    Xt_15 = interp_utils.linear_interpolation(key_timestamps=(5, 15), key_translations=(X0, X1), query_timestamp=15)
+    expected_Xt_15: NDArrayFloat = np.array([-1, 2, 10], dtype=float)
+    assert np.array_equal(Xt_15, expected_Xt_15)
diff --git a/tests/geometry/test_pinhole_camera.py b/tests/geometry/test_pinhole_camera.py
new file mode 100644
index 00000000..b1a16667
--- /dev/null
+++ b/tests/geometry/test_pinhole_camera.py
@@ -0,0 +1,599 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for PinholeCamera model."""
+
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import pytest
+
+from av2.datasets.sensor.constants import RingCameras
+from av2.geometry.camera.pinhole_camera import Intrinsics, PinholeCamera
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayFloat, NDArrayInt
+
+_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent / "test_data"
+
+
+def _create_pinhole_camera(
+    fx_px: float,
+    fy_px: float,
+    cx_px: float,
+    cy_px: float,
+    height_px: int,
+    width_px: int,
+    cam_name: str,
+) -> PinholeCamera:
+    """Create a pinhole camera."""
+    rotation: NDArrayFloat = np.eye(3)
+    translation: NDArrayFloat = np.zeros(3)
+    ego_SE3_cam = SE3(rotation=rotation, translation=translation)
+
+    intrinsics = Intrinsics(fx_px=fx_px, fy_px=fy_px, cx_px=cx_px, cy_px=cy_px, width_px=width_px, height_px=height_px)
+    pinhole_camera = PinholeCamera(ego_SE3_cam, intrinsics, cam_name)
+    return pinhole_camera
+
+
+def _fit_plane_to_point_cloud(points_xyz: NDArrayFloat) -> Tuple[float, float, float, float]:
+    """Use SVD with at least 3 points to fit a plane.
+
+    Args:
+        points_xyz: (N,3) array of points.
+
+    Returns:
+        (4,) Plane coefficients. Defining ax + by + cz = d for the plane.
+    """
+    center_xyz: NDArrayFloat = np.mean(points_xyz, axis=0)
+    out: Tuple[NDArrayFloat, NDArrayFloat, NDArrayFloat] = np.linalg.svd(points_xyz - center_xyz)  # type: ignore
+    vh = out[2]
+
+    # Get the unitary normal vector
+    a, b, c = float(vh[2, 0]), float(vh[2, 1]), float(vh[2, 2])
+    d: float = -np.dot([a, b, c], center_xyz)  # type: ignore
+    return (a, b, c, d)
+
+
+def test_intrinsics_constructor() -> None:
+    """Ensure 3x3 intrinsics matrix is populated correctly."""
+    fx_px, fy_px = 1000, 1001
+
+    width_px = 2048
+    height_px = 1550
+
+    cx_px, cy_px = 1024, 775
+
+    intrinsics = Intrinsics(fx_px=fx_px, fy_px=fy_px, cx_px=cx_px, cy_px=cy_px, width_px=width_px, height_px=height_px)
+    K_expected: NDArrayFloat = np.array(([1000, 0, 1024], [0, 1001, 775], [0, 0, 1]), dtype=np.float64)
+    assert np.array_equal(intrinsics.K, K_expected)
+
+
+def test_right_clipping_plane() -> None:
+    """Test form_right_clipping_plane(). Use 4 points to fit the right clipping plane.
+
+    In the camera coordinate frame, y is down the imager, x is across the imager,
+    and z is along the optical axis. The focal length is the distance to the center
+    of the image plane. We know that a similar triangle is formed as follows:
+
+    (x,y,z)---(x,y,z)
+       |         /
+       |        / ->outside of frustum
+       |       / ->outside of frustum
+       | (w/2)/
+       o-----o IMAGE PLANE
+       |    /
+     fx|   /
+       |  /
+       | /
+       O PINHOLE
+
+    Normal must point into the frustum. The plane moves +fx in z-axis for
+    every +w/2 in x-axis, so normal will have negative inverse slope components.
+    """
+    fx_px = 10.0
+    width_px = 30
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx_px, fy_px=0, cx_px=0, cy_px=0, height_px=30, width_px=width_px, cam_name="ring_front_center"
+    )
+    right_plane = pinhole_camera.right_clipping_plane
+
+    Y_OFFSET = 10  # arbitrary extent down the imager
+
+    right: NDArrayFloat = np.array(
+        [
+            [0, 0, 0],
+            [width_px / 2.0, 0, fx_px],
+            [0, Y_OFFSET, 0],
+            [width_px / 2.0, Y_OFFSET, fx_px],
+        ]
+    )
+
+    a, b, c, d = _fit_plane_to_point_cloud(right)
+    right_plane_expected: NDArrayFloat = np.array([a, b, c, d])
+
+    # enforce that plane normal points into the frustum
+    # x-component of normal should point in negative direction.
+    if right_plane_expected[0] > 0:
+        right_plane_expected *= -1
+
+    assert np.allclose(right_plane, right_plane_expected)
+
+
+def test_left_clipping_plane() -> None:
+    r"""Test left_clipping_plane. Use 4 points to fit the left clipping plane.
+
+                      (x,y,z)-----(x,y,z)
+                         \\          |
+    outside of frustum <- \\         |
+     outside of frustum <- \\        |
+                            \\ (-w/2)|
+                              o------o IMAGE PLANE
+                              \\     |
+                               \\    |
+                                \\   |fx
+                                 \\  |
+                                  \\ |
+                                     O PINHOLE
+    """
+    fx_px = 10.0
+    width_px = 30
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx_px, fy_px=0, cx_px=0, cy_px=0, height_px=30, width_px=width_px, cam_name="ring_front_center"
+    )
+    left_plane = pinhole_camera.left_clipping_plane
+
+    Y_OFFSET = 10
+    points_xyz: NDArrayFloat = np.array(
+        [
+            [0, 0, 0],
+            [-width_px / 2.0, 0, fx_px],
+            [0, Y_OFFSET, 0],
+            [-width_px / 2.0, Y_OFFSET, fx_px],
+        ]
+    )
+
+    a, b, c, d = _fit_plane_to_point_cloud(points_xyz)
+    left_plane_expected = -np.array([a, b, c, d])
+
+    # enforce that plane normal points into the frustum
+    if left_plane_expected[0] < 0:
+        left_plane_expected *= -1
+
+    assert np.allclose(left_plane, left_plane_expected)
+
+
+def test_top_clipping_plane() -> None:
+    r"""Test top_clipping_plane. Use 3 points to fit the TOP clipping plane.
+
+      (x,y,z)               (x,y,z)
+          \\=================//
+           \\               //
+    (-w/h,-h/2,fx)       (w/h,-h/2,fx)
+             o-------------o
+             |\\         //| IMAGE PLANE
+             | \\       // | IMAGE PLANE
+             o--\\-----//--o
+                 \\   //
+                  \\ //
+                    O PINHOLE
+    """
+    fx_px = 10.0
+    height_px = 45
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx_px, fy_px=0, cx_px=0, cy_px=0, height_px=height_px, width_px=1000, cam_name="ring_front_center"
+    )
+
+    top_plane = pinhole_camera.top_clipping_plane
+
+    width_px = 1000.0
+    points_xyz: NDArrayFloat = np.array(
+        [
+            [0, 0, 0],
+            [-width_px / 2, -height_px / 2, fx_px],
+            [width_px / 2, -height_px / 2, fx_px],
+        ]
+    )
+    a, b, c, d = _fit_plane_to_point_cloud(points_xyz)
+    top_plane_expected: NDArrayFloat = np.array([a, b, c, d])
+
+    # enforce that plane normal points into the frustum
+    if top_plane_expected[1] < 0:
+        # y-coord of normal should point in pos y-axis dir(down) on top-clipping plane
+        top_plane_expected *= -1
+
+    assert top_plane_expected[1] > 0 and top_plane_expected[2] > 0
+    assert np.allclose(top_plane, top_plane_expected)
+
+
+def test_bottom_clipping_plane() -> None:
+    r"""Test bottom_clipping_plane. Use 3 points to fit the BOTTOM clipping plane.
+
+           (x,y,z)              (x,y,z)
+              \\                   //
+               \\ o-------------o //
+                \\| IMAGE PLANE |//
+                  |             |/
+    (-w/h,h/2,fx) o-------------o (w/h,h/2,fx)
+                   \\         //
+                    \\       //
+                     \\     //
+                      \\   //
+                       \\ //
+                         O PINHOLE
+    """
+    fx_px = 12.0
+    height_px = 35
+    width_px = 10000
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx_px, fy_px=1, cx_px=0, cy_px=0, height_px=height_px, width_px=width_px, cam_name="ring_front_center"
+    )
+    bottom_plane = pinhole_camera.bottom_clipping_plane
+
+    low_pts: NDArrayFloat = np.array(
+        [
+            [0, 0, 0],
+            [-width_px / 2, height_px / 2, fx_px],
+            [width_px / 2, height_px / 2, fx_px],
+        ]
+    )
+    a, b, c, d = _fit_plane_to_point_cloud(low_pts)
+    bottom_plane_expected: NDArrayFloat = np.array([a, b, c, d])
+
+    # enforce that plane normal points into the frustum
+    # y-coord of normal should point in neg y-axis dir(up) on low-clipping plane
+    # z-coord should point in positive z-axis direction (away from camera)
+    if bottom_plane_expected[1] > 0:
+        bottom_plane_expected *= -1
+    assert bottom_plane_expected[1] < 0 and bottom_plane_expected[2] > 0
+
+    assert np.allclose(bottom_plane, bottom_plane_expected)
+
+
+def test_form_near_clipping_plane() -> None:
+    """Test near_clipping_plane(). Use 4 points to fit the near clipping plane."""
+    width_px = 10
+    height_px = 15
+    near_clip_dist = 30.0
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=1, fy_px=0, cx_px=0, cy_px=0, height_px=30, width_px=width_px, cam_name="ring_front_center"
+    )
+    near_plane = pinhole_camera.near_clipping_plane(near_clip_dist)
+
+    points_xyz: NDArrayFloat = np.array(
+        [
+            [width_px / 2, 0, near_clip_dist],
+            [-width_px / 2, 0, near_clip_dist],
+            [width_px / 2, -height_px / 2.0, near_clip_dist],
+            [width_px / 2, height_px / 2.0, near_clip_dist],
+        ]
+    )
+
+    a, b, c, d = _fit_plane_to_point_cloud(points_xyz)
+    near_plane_expected: NDArrayFloat = np.array([a, b, c, d])
+
+    assert np.allclose(near_plane, near_plane_expected)
+
+
+def test_frustum_planes_ring_cam() -> None:
+    """Test frustum_planes for a ring camera."""
+    near_clip_dist = 6.89  # arbitrary value
+
+    # Set "focal_length_x_px_"
+    fx_px = 1402.4993697398709
+
+    # Set "focal_length_y_px_"
+    fy_px = 1405.1207294310225
+
+    # Set "focal_center_x_px_"
+    cx_px = 957.8471720086527
+
+    # Set "focal_center_y_px_"
+    cy_px = 600.442948946496
+
+    camera_name = "ring_front_right"
+    height_px = 1550
+    width_px = 2048
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx_px, fy_px=fy_px, cx_px=cx_px, cy_px=cy_px, height_px=height_px, width_px=width_px, cam_name=camera_name
+    )
+    left_plane, right_plane, near_plane, bottom_plane, top_plane = pinhole_camera.frustum_planes(near_clip_dist)
+
+    left_plane_expected: NDArrayFloat = np.array([fx_px, 0.0, width_px / 2.0, 0.0])
+    right_plane_expected: NDArrayFloat = np.array([-fx_px, 0.0, width_px / 2.0, 0.0])
+    near_plane_expected: NDArrayFloat = np.array([0.0, 0.0, 1.0, -near_clip_dist])
+    bottom_plane_expected: NDArrayFloat = np.array([0.0, -fx_px, height_px / 2.0, 0.0])
+    top_plane_expected: NDArrayFloat = np.array([0.0, fx_px, height_px / 2.0, 0.0])
+
+    assert np.allclose(left_plane, left_plane_expected / np.linalg.norm(left_plane_expected))  # type: ignore
+    assert np.allclose(right_plane, right_plane_expected / np.linalg.norm(right_plane_expected))  # type: ignore
+    assert np.allclose(bottom_plane, bottom_plane_expected / np.linalg.norm(bottom_plane_expected))  # type: ignore
+    assert np.allclose(top_plane, top_plane_expected / np.linalg.norm(top_plane_expected))  # type: ignore
+    assert np.allclose(near_plane, near_plane_expected)
+
+
+def test_generate_frustum_planes_stereo() -> None:
+    """Test generate_frustum_planes() for a stereo camera."""
+    near_clip_dist = 3.56  # arbitrary value
+
+    # Set "focal_length_x_px_"
+    fx_px = 3666.534329132812
+
+    # Set "focal_length_y_px_"
+    fy_px = 3673.5030423482513
+
+    # Set "focal_center_x_px_"
+    cx_px = 1235.0158218941356
+
+    # Set "focal_center_y_px_"
+    cy_px = 1008.4536901420888
+
+    camera_name = "stereo_front_left"
+    height_px = 1550
+    width_px = 2048
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx_px, fy_px=fy_px, cx_px=cx_px, cy_px=cy_px, height_px=height_px, width_px=width_px, cam_name=camera_name
+    )
+    left_plane, right_plane, near_plane, bottom_plane, top_plane = pinhole_camera.frustum_planes(near_clip_dist)
+
+    left_plane_expected: NDArrayFloat = np.array([fx_px, 0.0, width_px / 2.0, 0.0])
+    right_plane_expected: NDArrayFloat = np.array([-fx_px, 0.0, width_px / 2.0, 0.0])
+    near_plane_expected: NDArrayFloat = np.array([0.0, 0.0, 1.0, -near_clip_dist])
+    bottom_plane_expected: NDArrayFloat = np.array([0.0, -fx_px, height_px / 2.0, 0.0])
+    top_plane_expected: NDArrayFloat = np.array([0.0, fx_px, height_px / 2.0, 0.0])
+
+    assert np.allclose(left_plane, left_plane_expected / np.linalg.norm(left_plane_expected))  # type: ignore
+    assert np.allclose(right_plane, right_plane_expected / np.linalg.norm(right_plane_expected))  # type: ignore
+    assert np.allclose(bottom_plane, bottom_plane_expected / np.linalg.norm(bottom_plane_expected))  # type: ignore
+    assert np.allclose(top_plane, top_plane_expected / np.linalg.norm(top_plane_expected))  # type: ignore
+    assert np.allclose(near_plane, near_plane_expected)
+
+
+def test_compute_pixel_ray_directions_vectorized_invalid_focal_lengths() -> None:
+    """If focal lengths in the x and y directions do not match, we throw an exception.
+
+    Tests vectorized variant (multiple ray directions.)
+    """
+    uv: NDArrayInt = np.array([[12, 2], [12, 2], [12, 2], [12, 2]])
+    fx = 10
+    fy = 11
+
+    img_w = 20
+    img_h = 10
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx,
+        fy_px=fy,
+        cx_px=img_w / 2,
+        cy_px=img_h / 2,
+        height_px=img_h,
+        width_px=img_w,
+        cam_name="ring_front_center",  # dummy name
+    )
+
+    with pytest.raises(ValueError):
+        pinhole_camera.compute_pixel_ray_directions(uv)
+
+
+def test_compute_pixel_ray_direction_invalid_focal_lengths() -> None:
+    """If focal lengths in the x and y directions do not match, we throw an exception.
+
+    Tests non-vectorized variant (single ray direction).
+    """
+    u = 12
+    v = 2
+    fx = 10
+    fy = 11
+
+    img_w = 20
+    img_h = 10
+    with pytest.raises(ValueError):
+        _compute_pixel_ray_direction(u, v, fx, fy, img_w, img_h)
+
+
+def test_compute_pixel_ray_directions_vectorized() -> None:
+    """Ensure that the ray direction (in camera coordinate frame) for each pixel is computed correctly.
+
+    Small scale test, for just four selected positions in a 10 x 20 px image in (height, width).
+    """
+    fx = 10
+    fy = 10
+
+    # dummy 2d coordinates in the image plane.
+    uv: NDArrayInt = np.array([[12, 2], [12, 2], [12, 2], [12, 2]])
+
+    # principal point is at (10,5)
+    img_w = 20
+    img_h = 10
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx,
+        fy_px=fy,
+        cx_px=img_w / 2,
+        cy_px=img_h / 2,
+        height_px=img_h,
+        width_px=img_w,
+        cam_name="ring_front_center",  # dummy name
+    )
+    ray_dirs = pinhole_camera.compute_pixel_ray_directions(uv)
+
+    gt_ray_dir: NDArrayFloat = np.array([2, -3, 10.0])
+    gt_ray_dir /= np.linalg.norm(gt_ray_dir)  # type: ignore
+
+    for i in range(4):
+        assert np.allclose(gt_ray_dir, ray_dirs[i])
+
+
+def test_compute_pixel_ray_directions_vectorized_entireimage() -> None:
+    """Ensure that the ray direction for each pixel (in camera coordinate frame) is computed correctly.
+
+    Compare all computed rays against non-vectorized variant, for correctness.
+    Larger scale test, for every pixel in a 50 x 100 px image in (height, width).
+    """
+    fx = 10
+    fy = 10
+
+    img_w = 100
+    img_h = 50
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx,
+        fy_px=fy,
+        cx_px=img_w / 2,
+        cy_px=img_h / 2,
+        height_px=img_h,
+        width_px=img_w,
+        cam_name="ring_front_center",  # dummy name
+    )
+
+    uv_list = []
+    for u in range(img_w):
+        for v in range(img_h):
+            uv_list += [(u, v)]
+
+    uv: NDArrayInt = np.array(uv_list)
+    assert uv.shape == (img_w * img_h, 2)
+
+    ray_dirs = pinhole_camera.compute_pixel_ray_directions(uv)
+
+    # compare w/ vectorized, should be identical
+    for i, ray_dir_vec in enumerate(ray_dirs):
+        u, v = uv[i]
+        ray_dir_nonvec = _compute_pixel_ray_direction(u, v, fx, fy, img_w, img_h)
+        assert np.allclose(ray_dir_vec, ray_dir_nonvec)
+
+
+def test_compute_pixel_rays() -> None:
+    """Ensure that the ray direction (in camera coordinate frame) for a single pixel is computed correctly.
+
+    Small scale test, for just one selected position in a 10 x 20 px image in (height, width).
+    For row = 2, column = 12.
+    """
+    u = 12
+    v = 2
+    img_w = 20
+    img_h = 10
+    fx = 10
+    fy = 10
+
+    ray_dir = _compute_pixel_ray_direction(u, v, fx, fy, img_w, img_h)
+
+    gt_ray_dir: NDArrayFloat = np.array([2.0, -3.0, 10.0])
+    gt_ray_dir /= np.linalg.norm(gt_ray_dir)  # type: ignore
+
+    assert np.allclose(gt_ray_dir, ray_dir)
+
+
+def _compute_pixel_ray_direction(u: float, v: float, fx: float, fy: float, img_w: int, img_h: int) -> NDArrayFloat:
+    r"""Generate rays in the camera coordinate frame.
+
+    Note: only used as a test utility.
+
+       Find point P on image plane.
+
+                      (x,y,z)-----(x,y,z)
+                         \\          |
+    outside of frustum <- \\         |
+     outside of frustum <- \\        |
+                            \\ (-w/2)|
+                              o------o IMAGE PLANE
+                              \\     |
+                               \\    |
+                                \\   |fx
+                                 \\  |
+                                  \\ |
+                                     O PINHOLE
+
+    Args:
+        u: pixel's x-coordinate
+        v: pixel's y-coordinate
+        fx: focal length in x-direction, measured in pixels.
+        fy: focal length in y-direction,  measured in pixels.
+        img_w: image width (in pixels)
+        img_h: image height (in pixels)
+
+    Returns:
+        Direction of 3d ray, provided in the camera frame.
+
+    Raises:
+        ValueError: If horizontal and vertical focal lengths are not close (within 1e-3).
+    """
+    if not np.isclose(fx, fy, atol=1e-3):
+        raise ValueError(f"Focal lengths in the x and y directions must match: {fx} != {fy}")
+
+    # approximation for principal point
+    px = img_w / 2
+    py = img_h / 2
+
+    # the camera coordinate frame (where Z is out, x is right, y is down).
+
+    # compute offset from the center
+    x_center_offs = u - px
+    y_center_offs = v - py
+
+    ray_dir: NDArrayFloat = np.array([x_center_offs, y_center_offs, fx])
+    ray_dir /= np.linalg.norm(ray_dir)  # type: ignore
+    return ray_dir
+
+
+def test_get_frustum_parameters() -> None:
+    r"""Ensure we can compute field of view, and camera's yaw in the egovehicle frame.
+
+      w/2 = 1000
+    o----------o IMAGE PLANE
+    \\         |       //
+      \\       |     //
+        \\     |fx = 1000
+          \\   |   //
+            \\ | //
+               O PINHOLE
+    """
+    fx, fy = 1000, 1000
+    img_w = 2000
+    img_h = 1000
+
+    pinhole_camera = _create_pinhole_camera(
+        fx_px=fx,
+        fy_px=fy,
+        cx_px=img_w / 2,
+        cy_px=img_h / 2,
+        height_px=img_h,
+        width_px=img_w,
+        cam_name="ring_front_center",  # dummy name
+    )
+
+    fov_theta_deg = np.rad2deg(pinhole_camera.fov_theta_rad)
+    assert np.isclose(fov_theta_deg, 90.0)
+
+    # for identity SE(3), the yaw angle is zero radians.
+    cam_yaw_ego = pinhole_camera.egovehicle_yaw_cam_rad
+    assert np.isclose(cam_yaw_ego, 0)
+
+
+def test_get_egovehicle_yaw_cam() -> None:
+    """Ensure we can compute the camera's yaw in the egovehicle frame."""
+    sample_log_dir = _TEST_DATA_ROOT / "sensor_dataset_logs" / "test_log"
+
+    # clockwise around the top of the car, in degrees.
+    expected_ego_yaw_cam_deg_dict = {
+        "ring_rear_left": 153.2,
+        "ring_side_left": 99.4,
+        "ring_front_left": 44.7,
+        "ring_front_center": 0.4,
+        "ring_front_right": -44.9,
+        "ring_side_right": -98.9,
+        "ring_rear_right": -152.9,
+    }
+
+    for cam_enum in list(RingCameras):
+        cam_name = cam_enum.value
+        pinhole_camera = PinholeCamera.from_feather(log_dir=sample_log_dir, cam_name=cam_name)
+
+        ego_yaw_cam_deg = np.rad2deg(pinhole_camera.egovehicle_yaw_cam_rad)
+        assert np.isclose(ego_yaw_cam_deg, expected_ego_yaw_cam_deg_dict[cam_name], atol=0.1)
+
+        np.rad2deg(pinhole_camera.fov_theta_rad)
diff --git a/tests/map/test_drivable_area.py b/tests/map/test_drivable_area.py
new file mode 100644
index 00000000..3d6b4a8c
--- /dev/null
+++ b/tests/map/test_drivable_area.py
@@ -0,0 +1,32 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for the DrivableArea class."""
+
+
+import unittest
+
+from av2.map.drivable_area import DrivableArea
+
+
+class TestDrivableArea(unittest.TestCase):
+    """Class for unit testing the drivable area raster map."""
+
+    def test_from_dict(self) -> None:
+        """Ensure object is generated correctly from a dictionary.
+
+        Note: 3 arbitrary waypoints taken from the dummy log map file.
+        """
+        json_data = {
+            "area_boundary": [
+                {"x": 905.09, "y": -148.95, "z": -19.19},
+                {"x": 919.48, "y": -150.0, "z": -18.86},
+                {"x": 905.14, "y": -150.0, "z": -19.18},
+            ],
+            "id": 4499430,
+        }
+
+        drivable_area = DrivableArea.from_dict(json_data)
+
+        assert isinstance(drivable_area, DrivableArea)
+        assert drivable_area.id == 4499430
+        assert len(drivable_area.area_boundary) == 4  # first vertex is repeated as the last vertex
diff --git a/tests/map/test_lane_segment.py b/tests/map/test_lane_segment.py
new file mode 100644
index 00000000..9ee9997c
--- /dev/null
+++ b/tests/map/test_lane_segment.py
@@ -0,0 +1,55 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Tests for the classes associated w/ LaneSegment's and their properties (e.g. markings)."""
+
+import unittest
+
+from av2.map.lane_segment import LaneMarkType, LaneSegment, LaneType
+from av2.map.map_primitives import Point, Polyline
+
+
+class TestLaneSegment(unittest.TestCase):
+    """LaneSegment unit testing class."""
+
+    def test_from_dict(self) -> None:
+        """Ensure object is generated correctly from a dictionary."""
+        json_data = {
+            "id": 93269421,
+            "is_intersection": False,
+            "lane_type": "VEHICLE",
+            "left_lane_boundary": [
+                {"x": 873.97, "y": -101.75, "z": -19.7},
+                {"x": 880.31, "y": -101.44, "z": -19.7},
+                {"x": 890.29, "y": -100.56, "z": -19.66},
+            ],
+            "left_lane_mark_type": "SOLID_YELLOW",
+            "left_neighbor_id": None,
+            "right_lane_boundary": [{"x": 874.01, "y": -105.15, "z": -19.58}, {"x": 890.58, "y": -104.26, "z": -19.58}],
+            "right_lane_mark_type": "SOLID_WHITE",
+            "right_neighbor_id": 93269520,
+            "predecessors": [],
+            "successors": [93269500],
+        }
+        lane_segment = LaneSegment.from_dict(json_data)
+
+        assert isinstance(lane_segment, LaneSegment)
+
+        assert lane_segment.id == 93269421
+        assert not lane_segment.is_intersection
+        assert lane_segment.lane_type == LaneType("VEHICLE")
+        # fmt: off
+        assert lane_segment.right_lane_boundary == Polyline(waypoints=[
+            Point(874.01, -105.15, -19.58),
+            Point(890.58, -104.26, -19.58)
+        ])
+        assert lane_segment.left_lane_boundary == Polyline(waypoints=[
+            Point(873.97, -101.75, -19.7),
+            Point(880.31, -101.44, -19.7),
+            Point(890.29, -100.56, -19.66)
+        ])
+        # fmt: on
+        assert lane_segment.right_mark_type == LaneMarkType("SOLID_WHITE")
+        assert lane_segment.left_mark_type == LaneMarkType("SOLID_YELLOW")
+        assert lane_segment.successors == [93269500]
+        assert lane_segment.right_neighbor_id == 93269520
+        assert lane_segment.left_neighbor_id is None
diff --git a/tests/map/test_map_api.py b/tests/map/test_map_api.py
new file mode 100644
index 00000000..1e1e16d1
--- /dev/null
+++ b/tests/map/test_map_api.py
@@ -0,0 +1,348 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""
+Tests for the Argoverse 2.0 map API.
+
+Uses a simplified map with 2 pedestrian crossings, and 3 lane segments.
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from av2.map.drivable_area import DrivableArea
+from av2.map.lane_segment import LaneSegment
+from av2.map.map_api import ArgoverseStaticMap
+from av2.map.map_primitives import Point, Polyline
+from av2.map.pedestrian_crossing import PedestrianCrossing
+from av2.utils.typing import NDArrayBool, NDArrayFloat
+
+
+@pytest.fixture()
+def dummy_static_map(test_data_root_dir: Path) -> ArgoverseStaticMap:
+    """Set up test by instantiating static map object from dummy test data.
+
+    Args:
+        test_data_root_dir: Path to the root dir for test data (provided via fixture).
+
+    Returns:
+        Static map instantiated from dummy test data.
+    """
+    log_map_dirpath = (
+        test_data_root_dir / "static_maps" / "dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076"
+    )
+
+    return ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)
+
+
+@pytest.fixture(scope="module")
+def full_static_map(test_data_root_dir: Path) -> ArgoverseStaticMap:
+    """Set up test by instantiating static map object from full test data.
+
+    Args:
+        test_data_root_dir: Path to the root dir for test data (provided via fixture).
+
+    Returns:
+        Static map instantiated from full test data.
+    """
+    log_map_dirpath = (
+        test_data_root_dir / "static_maps" / "full_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076"
+    )
+    return ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)
+
+
+class TestPolyline:
+    """Class for unit testing `PolyLine`."""
+
+    def test_from_list(self) -> None:
+        """Ensure object is generated correctly from a list of dictionaries."""
+        points_dict_list = [{"x": 874.01, "y": -105.15, "z": -19.58}, {"x": 890.58, "y": -104.26, "z": -19.58}]
+        polyline = Polyline.from_json_data(points_dict_list)
+
+        assert isinstance(polyline, Polyline)
+
+        assert len(polyline.waypoints) == 2
+        assert polyline.waypoints[0] == Point(874.01, -105.15, -19.58)
+        assert polyline.waypoints[1] == Point(890.58, -104.26, -19.58)
+
+    def test_from_array(self) -> None:
+        """Ensure object is generated correctly from a Numpy array of shape (N,3)."""
+        # fmt: off
+        array: NDArrayFloat = np.array([
+            [1., 2., 3.],
+            [4., 5., 6.],
+            [7., 8., 9.],
+            [9., 10., 11.]
+        ])
+        # fmt: on
+        polyline = Polyline.from_array(array)
+        assert isinstance(polyline, Polyline)
+        assert len(polyline) == 4
+        assert polyline.waypoints[0].x == 1
+        assert polyline.waypoints[0].y == 2
+        assert polyline.waypoints[0].z == 3
+
+
+class TestPedestrianCrossing:
+    """Class for unit testing `PedestrianCrossing`."""
+
+    def test_from_dict(self) -> None:
+        """Ensure object is generated correctly from a dictionary."""
+        json_data = {
+            "id": 6310421,
+            "edge1": [{"x": 899.17, "y": -91.52, "z": -19.58}, {"x": 915.68, "y": -93.93, "z": -19.53}],
+            "edge2": [{"x": 899.44, "y": -95.37, "z": -19.48}, {"x": 918.25, "y": -98.05, "z": -19.4}],
+        }
+        pedestrian_crossing = PedestrianCrossing.from_dict(json_data)
+
+        isinstance(pedestrian_crossing, PedestrianCrossing)
+
+
+class TestArgoverseStaticMap:
+    """Unit test for the Argoverse 2.0 per-log map."""
+
+    def test_get_lane_segment_successor_ids(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure lane segment successors are fetched properly."""
+        lane_segment_id = 93269421
+        successor_ids = dummy_static_map.get_lane_segment_successor_ids(lane_segment_id)
+        expected_successor_ids = [93269500]
+        assert successor_ids == expected_successor_ids
+
+        lane_segment_id = 93269500
+        successor_ids = dummy_static_map.get_lane_segment_successor_ids(lane_segment_id)
+        expected_successor_ids = [93269554]
+        assert successor_ids == expected_successor_ids
+
+        lane_segment_id = 93269520
+        successor_ids = dummy_static_map.get_lane_segment_successor_ids(lane_segment_id)
+        expected_successor_ids = [93269526]
+        assert successor_ids == expected_successor_ids
+
+    def test_lane_is_in_intersection(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure the attribute describing if a lane segment is located with an intersection is fetched properly."""
+        lane_segment_id = 93269421
+        in_intersection = dummy_static_map.lane_is_in_intersection(lane_segment_id)
+        assert isinstance(in_intersection, bool)
+        assert not in_intersection
+
+        lane_segment_id = 93269500
+        in_intersection = dummy_static_map.lane_is_in_intersection(lane_segment_id)
+        assert isinstance(in_intersection, bool)
+        assert in_intersection
+
+        lane_segment_id = 93269520
+        in_intersection = dummy_static_map.lane_is_in_intersection(lane_segment_id)
+        assert isinstance(in_intersection, bool)
+        assert not in_intersection
+
+    def test_get_lane_segment_left_neighbor_id(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Test getting a lane segment id from the left neighbor."""
+        # Ensure id of lane segment (if any) that is the left neighbor to the query lane segment can be fetched properly
+        lane_segment_id = 93269421
+        l_neighbor_id = dummy_static_map.get_lane_segment_left_neighbor_id(lane_segment_id)
+        assert l_neighbor_id is None
+
+        lane_segment_id = 93269500
+        l_neighbor_id = dummy_static_map.get_lane_segment_left_neighbor_id(lane_segment_id)
+        assert l_neighbor_id is None
+
+        lane_segment_id = 93269520
+        l_neighbor_id = dummy_static_map.get_lane_segment_left_neighbor_id(lane_segment_id)
+        assert l_neighbor_id == 93269421
+
+    def test_get_lane_segment_right_neighbor_id(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Test getting a lane segment id from the right neighbor."""
+        # Ensure id of lane segment (if any) that is the right neighbor to the query lane segment can be fetched
+        lane_segment_id = 93269421
+        r_neighbor_id = dummy_static_map.get_lane_segment_right_neighbor_id(lane_segment_id)
+        assert r_neighbor_id == 93269520
+
+        lane_segment_id = 93269500
+        r_neighbor_id = dummy_static_map.get_lane_segment_right_neighbor_id(lane_segment_id)
+        assert r_neighbor_id == 93269526
+
+        lane_segment_id = 93269520
+        r_neighbor_id = dummy_static_map.get_lane_segment_right_neighbor_id(lane_segment_id)
+        assert r_neighbor_id == 93269458
+
+    def test_get_scenario_lane_segment_ids(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure ids of all lane segments in the local map can be fetched properly."""
+        lane_segment_ids = dummy_static_map.get_scenario_lane_segment_ids()
+
+        expected_lane_segment_ids = [93269421, 93269500, 93269520]
+        assert lane_segment_ids == expected_lane_segment_ids
+
+    def test_get_lane_segment_polygon(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure lane segment polygons are fetched properly."""
+        lane_segment_id = 93269421
+
+        ls_polygon = dummy_static_map.get_lane_segment_polygon(lane_segment_id)
+        assert isinstance(ls_polygon, np.ndarray)
+
+        expected_ls_polygon: NDArrayFloat = np.array(
+            [
+                [874.01, -105.15, -19.58],
+                [890.58, -104.26, -19.58],
+                [890.29, -100.56, -19.66],
+                [880.31, -101.44, -19.7],
+                [873.97, -101.75, -19.7],
+                [874.01, -105.15, -19.58],
+            ]
+        )
+        np.testing.assert_allclose(ls_polygon, expected_ls_polygon)  # type: ignore
+
+    def test_get_lane_segment_centerline(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure lane segment centerlines can be inferred and fetched properly."""
+        lane_segment_id = 93269421
+
+        centerline = dummy_static_map.get_lane_segment_centerline(lane_segment_id)
+        assert isinstance(centerline, np.ndarray)
+
+        expected_centerline: NDArrayFloat = np.array(
+            [
+                [873.99, -103.45, -19.64],
+                [875.81871374, -103.35615034, -19.64],
+                [877.64742747, -103.26230069, -19.64],
+                [879.47614121, -103.16845103, -19.64],
+                [881.30361375, -103.0565384, -19.63815074],
+                [883.129891, -102.92723072, -19.63452059],
+                [884.95616825, -102.79792304, -19.63089044],
+                [886.7824455, -102.66861536, -19.62726029],
+                [888.60872275, -102.53930768, -19.62363015],
+                [890.435, -102.41, -19.62],
+            ]
+        )
+        np.testing.assert_allclose(centerline, expected_centerline)  # type: ignore
+
+    def test_get_scenario_lane_segments(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure that all LaneSegment objects in the local map can be returned as a list."""
+        vector_lane_segments = dummy_static_map.get_scenario_lane_segments()
+        assert isinstance(vector_lane_segments, list)
+        assert all([isinstance(vls, LaneSegment) for vls in vector_lane_segments])
+        assert len(vector_lane_segments) == 3
+
+    def test_get_scenario_ped_crossings(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure that all PedCrossing objects in the local map can be returned as a list."""
+        ped_crossings = dummy_static_map.get_scenario_ped_crossings()
+        assert isinstance(ped_crossings, list)
+        assert all([isinstance(pc, PedestrianCrossing) for pc in ped_crossings])
+
+        # fmt: off
+        expected_ped_crossings = [
+            PedestrianCrossing(
+                id=6310407,
+                edge1=Polyline.from_array(np.array(
+                    [
+                        [ 892.17,  -99.44,  -19.59],
+                        [ 893.47, -115.4 ,  -19.45]
+                    ]
+                )), edge2=Polyline.from_array(np.array(
+                    [
+                        [ 896.06,  -98.95,  -19.52],
+                        [ 897.43, -116.58,  -19.42]
+                    ]
+                ))
+            ), PedestrianCrossing(
+                id=6310421,
+                edge1=Polyline.from_array(np.array(
+                    [
+                        [899.17, -91.52, -19.58],
+                        [915.68, -93.93, -19.53]
+                    ]
+                )),
+                edge2=Polyline.from_array(np.array(
+                    [
+                        [899.44, -95.37, -19.48],
+                        [918.25, -98.05, -19.4]
+                    ]
+                )),
+            )
+        ]
+        # fmt: on
+        assert len(ped_crossings) == len(expected_ped_crossings)
+        assert all([pc == expected_pc for pc, expected_pc in zip(ped_crossings, expected_ped_crossings)])
+
+    def test_get_scenario_vector_drivable_areas(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure that drivable areas are loaded and formatted correctly."""
+        vector_das = dummy_static_map.get_scenario_vector_drivable_areas()
+        assert isinstance(vector_das, list)
+        assert len(vector_das) == 1
+        assert isinstance(vector_das[0], DrivableArea)
+
+        # examine just one sample
+        vector_da = vector_das[0]
+        assert vector_da.xyz.shape == (172, 3)
+
+        # compare first and last vertex, for equality
+        np.testing.assert_allclose(vector_da.xyz[0], vector_da.xyz[171])  # type: ignore
+
+        # fmt: off
+        # compare first 4 vertices
+        expected_first4_vertices: NDArrayFloat = np.array(
+            [[905.09, -148.95, -19.19],
+             [904.85, -141.95, -19.25],
+             [904.64, -137.25, -19.28],
+             [904.37, -132.55, -19.32]])
+        # fmt: on
+        np.testing.assert_allclose(vector_da.xyz[:4], expected_first4_vertices)  # type: ignore
+
+    def test_get_ground_height_at_xy(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure that ground height at (x,y) locations can be retrieved properly."""
+        point_cloud: NDArrayFloat = np.array(
+            [
+                [770.6398, -105.8351, -19.4105],  # ego-vehicle pose at one timestamp
+                [943.5386, -49.6295, -19.3291],  # ego-vehicle pose at one timestamp
+                [918.0960, 82.5588, -20.5742],  # ego-vehicle pose at one timestamp
+                [9999999, 999999, 0],  # obviously out of bounds value for city coordinate system
+                [-999999, -999999, 0],  # obviously out of bounds value for city coordinate system
+            ]
+        )
+
+        assert dummy_static_map.raster_ground_height_layer is not None
+
+        ground_height_z = dummy_static_map.raster_ground_height_layer.get_ground_height_at_xy(point_cloud)
+
+        assert ground_height_z.shape[0] == point_cloud.shape[0]
+        assert ground_height_z.dtype == np.dtype(np.float64)
+
+        # last 2 indices should be filled with dummy values (NaN) because obviously out of bounds.
+        assert np.all(np.isnan(ground_height_z[-2:]))
+
+        # based on grid resolution, ground should be within 7 centimeters of 30cm under back axle.
+        expected_ground = point_cloud[:3, 2] - 0.30
+        assert np.allclose(np.absolute(expected_ground - ground_height_z[:3]), 0, atol=0.07)
+
+    def test_get_ground_points_boolean(self, dummy_static_map: ArgoverseStaticMap) -> None:
+        """Ensure that points close to the ground surface are correctly classified as `ground` category."""
+        point_cloud: NDArrayFloat = np.array(
+            [
+                [770.6398, -105.8351, -19.4105],  # ego-vehicle pose at one timestamp
+                [943.5386, -49.6295, -19.3291],  # ego-vehicle pose at one timestamp
+                [918.0960, 82.5588, -20.5742],  # ego-vehicle pose at one timestamp
+                [9999999, 999999, 0],  # obviously out of bounds value for city coordinate system
+                [-999999, -999999, 0],  # obviously out of bounds value for city coordinate system
+            ]
+        )
+
+        # first 3 points correspond to city_SE3_ego, i.e. height of rear axle in city frame
+        # ~30 cm below the axle should be the ground surface.
+        point_cloud -= 0.30
+
+        assert dummy_static_map.raster_ground_height_layer is not None
+
+        is_ground_pt = dummy_static_map.raster_ground_height_layer.get_ground_points_boolean(point_cloud)
+        expected_is_ground_pt: NDArrayBool = np.array([True, True, True, False, False])
+        assert is_ground_pt.dtype == np.dtype(bool)
+        assert np.array_equal(is_ground_pt, expected_is_ground_pt)
+
+
+def test_load_motion_forecasting_map(test_data_root_dir: Path) -> None:
+    """Try to load a real map from the motion forecasting dataset."""
+    mf_scenario_id = "0a1e6f0a-1817-4a98-b02e-db8c9327d151"
+    mf_scenario_map_path = (
+        test_data_root_dir / "forecasting_scenarios" / mf_scenario_id / f"log_map_archive_{mf_scenario_id}.json"
+    )
+
+    mf_map = ArgoverseStaticMap.from_json(mf_scenario_map_path)
+    assert mf_map.log_id == mf_scenario_id
diff --git a/tests/rendering/ops/test_draw.py b/tests/rendering/ops/test_draw.py
new file mode 100644
index 00000000..0feb5a05
--- /dev/null
+++ b/tests/rendering/ops/test_draw.py
@@ -0,0 +1,146 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for drawing ops."""
+
+from typing import Any, Callable
+
+import cv2
+import numpy as np
+
+from av2.rendering.ops.draw import alpha_blend_kernel, draw_points_kernel, gaussian_kernel
+from av2.utils.typing import NDArrayByte, NDArrayInt
+
+
+def _draw_points_cv2(
+    img: NDArrayByte, points_xy: NDArrayInt, colors: NDArrayByte, radius: int, with_anti_alias: bool = True
+) -> NDArrayByte:
+    """Draw points in an image using OpenCV functionality.
+
+    Args:
+        img: (H,W,3) BGR image.
+        points_xy: (N,2) Array of points.
+        colors: (N,3) Array of BGR colors.
+        radius: Radius of the circles.
+        with_anti_alias: Boolean to enable anti-aliasing.
+
+    Returns:
+        The image with points overlaid.
+    """
+    line_type = cv2.LINE_AA if with_anti_alias else None
+    for i, (x, y) in enumerate(points_xy):
+        rgb = colors[i]
+        rgb = tuple([int(intensity) for intensity in rgb])
+        cv2.circle(img, (x, y), radius, (0, 0, 0), -1, line_type)
+    return img
+
+
+def test_alpha_blend_kernel() -> None:
+    """Unit test alpha blending."""
+    fg: NDArrayByte = np.array([128, 128, 128], dtype=np.uint8)
+    bg: NDArrayByte = np.array([64, 64, 64], dtype=np.uint8)
+
+    alpha = 0
+    blended_pixel = alpha_blend_kernel(fg, bg, alpha)
+    assert blended_pixel == (64, 64, 64)
+
+    alpha = 128
+    blended_pixel = alpha_blend_kernel(fg, bg, alpha)
+    assert blended_pixel == (96, 96, 96)
+
+    alpha = 255
+    blended_pixel = alpha_blend_kernel(fg, bg, alpha)
+    assert blended_pixel == (128, 128, 128)
+
+
+def test_gaussian_kernel() -> None:
+    """Unit test for a Gaussian kernel."""
+    x = 0
+    mu = 0
+    sigma = 1
+    out = gaussian_kernel(x=x, mu=mu, sigma=sigma)
+
+    assert out == 1.0
+    assert gaussian_kernel(x=x - 1, mu=mu, sigma=sigma) < out
+    assert gaussian_kernel(x=x + 1, mu=mu, sigma=sigma) < out
+
+
+def test_draw_points_kernel_3x3_antialiased() -> None:
+    """Unit test drawing points on an image."""
+    img: NDArrayByte = np.zeros((3, 3, 3), dtype=np.uint8)
+    points_xy: NDArrayInt = np.array([[1, 1]], dtype=int)
+    colors: NDArrayByte = np.array([[128, 128, 128]], dtype=np.uint8)
+    sigma = 1.0
+    diameter = 3
+
+    expected_img: NDArrayByte = np.array(
+        [
+            [[47, 47, 47], [77, 77, 77], [47, 47, 47]],
+            [[77, 77, 77], [128, 128, 128], [77, 77, 77]],
+            [[47, 47, 47], [77, 77, 77], [47, 47, 47]],
+        ],
+        dtype=np.uint8,
+    )
+    img = draw_points_kernel(
+        img=img, points_uv=points_xy, colors=colors, diameter=diameter, sigma=sigma, with_anti_alias=True
+    )
+    assert np.array_equal(img, expected_img)
+
+
+def test_draw_points_kernel_9x9_aliased() -> None:
+    """Unit test drawing points on an image."""
+    img: NDArrayByte = np.zeros((9, 9, 3), dtype=np.uint8)
+    points_xy: NDArrayInt = np.array([[4, 4]], dtype=int)
+    colors: NDArrayByte = np.array([[128, 128, 128]], dtype=np.uint8)
+    sigma = 1.0
+    diameter = 3
+
+    expected_img = np.zeros((9, 9, 3), dtype=np.uint8)
+
+    expected_img[3:6, 3:6] = np.array(
+        [
+            [[128, 128, 128], [128, 128, 128], [128, 128, 128]],
+            [[128, 128, 128], [128, 128, 128], [128, 128, 128]],
+            [[128, 128, 128], [128, 128, 128], [128, 128, 128]],
+        ],
+        dtype=np.uint8,
+    )
+    img = draw_points_kernel(
+        img=img, points_uv=points_xy, colors=colors, diameter=diameter, sigma=sigma, with_anti_alias=False
+    )
+    assert np.array_equal(img, expected_img)
+
+
+def test_benchmark_draw_points_kernel_aliased(benchmark: Callable[..., Any]) -> None:
+    """Benchmark the draw points kernel _without_ anti-aliasing."""
+    img: NDArrayByte = np.zeros((2048, 2048, 3), dtype=np.uint8)
+    points_xy: NDArrayInt = np.random.randint(low=0, high=2048, size=(60000, 2))
+    colors: NDArrayByte = np.random.randint(low=0, high=255, size=(60000, 3)).astype(np.uint8)
+    diameter = 10
+    benchmark(draw_points_kernel, img, points_xy, colors, diameter)
+
+
+def test_benchmark_draw_points_kernel_anti_aliased(benchmark: Callable[..., Any]) -> None:
+    """Benchmark the draw points kernel _with_ anti-aliasing."""
+    img: NDArrayByte = np.zeros((2048, 2048, 3), dtype=np.uint8)
+    points_xy: NDArrayInt = np.random.randint(low=0, high=2048, size=(60000, 2))
+    colors: NDArrayByte = np.random.randint(low=0, high=255, size=(60000, 3)).astype(np.uint8)
+    diameter = 10
+    benchmark(draw_points_kernel, img, points_xy, colors, diameter, with_anti_alias=True)
+
+
+def test_benchmark_draw_points_cv2_aliased(benchmark: Callable[..., Any]) -> None:
+    """Benchmark the draw points method from OpenCV _without_ anti-aliasing."""
+    img: NDArrayByte = np.zeros((2048, 2048, 3), dtype=np.uint8)
+    points_xy: NDArrayInt = np.random.randint(low=0, high=2048, size=(60000, 2))
+    colors: NDArrayByte = np.random.randint(low=0, high=255, size=(60000, 3)).astype(np.uint8)
+    radius = 10
+    benchmark(_draw_points_cv2, img, points_xy, colors, radius, with_anti_alias=False)
+
+
+def test_benchmark_draw_points_cv2_anti_aliased(benchmark: Callable[..., Any]) -> None:
+    """Benchmark the draw points method from OpenCV _with_ anti-aliasing."""
+    img: NDArrayByte = np.zeros((2048, 2048, 3), dtype=np.uint8)
+    points_xy: NDArrayInt = np.random.randint(low=0, high=2048, size=(60000, 2))
+    colors: NDArrayByte = np.random.randint(low=0, high=255, size=(60000, 3)).astype(np.uint8)
+    radius = 10
+    benchmark(_draw_points_cv2, img, points_xy, colors, radius, with_anti_alias=True)
diff --git a/tests/rendering/test_color.py b/tests/rendering/test_color.py
new file mode 100644
index 00000000..c30d07b5
--- /dev/null
+++ b/tests/rendering/test_color.py
@@ -0,0 +1,16 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit test on colormap utilities."""
+
+import numpy as np
+
+import av2.rendering.color as color_utils
+from av2.rendering.color import GREEN_HEX, RED_HEX
+
+
+def test_create_colormap() -> None:
+    """Ensure we can create a red-to-green RGB colormap with values in [0,1]."""
+    colors_arr_rgb = color_utils.create_colormap(color_list=[RED_HEX, GREEN_HEX], n_colors=10)
+    assert np.logical_and(0 <= colors_arr_rgb, colors_arr_rgb <= 1).all()
+
+    assert colors_arr_rgb.shape == (10, 3)
diff --git a/tests/rendering/test_map.py b/tests/rendering/test_map.py
new file mode 100644
index 00000000..7d8735ae
--- /dev/null
+++ b/tests/rendering/test_map.py
@@ -0,0 +1,50 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for HD maps."""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import av2.rendering.map as map_rendering_utils
+from av2.rendering.color import RED_BGR
+from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayInt
+
+
+def test_draw_visible_polyline_segments_cv2_some_visible() -> None:
+    """Test rendering when one vertex is marked as occluded, and so two line segments are dropped out."""
+    visualize = False
+    # 6 vertices in the polyline.
+    line_segments_arr: NDArrayInt = np.array([[50, 0], [50, 20], [50, 40], [50, 60], [50, 80], [60, 120]])
+
+    valid_pts_bool: NDArrayBool = np.array([True, True, True, False, True, True])
+    img_bgr: NDArrayByte = np.zeros((100, 100, 3), dtype=np.uint8)
+    map_rendering_utils.draw_visible_polyline_segments_cv2(
+        line_segments_arr=line_segments_arr,
+        valid_pts_bool=valid_pts_bool,
+        image=img_bgr,
+        color=RED_BGR,
+        thickness_px=2,
+    )
+    if visualize:
+        plt.imshow(img_bgr[:, :, ::-1])
+        plt.show()
+
+
+def test_draw_visible_polyline_segments_cv2_all_visible() -> None:
+    """Test rendering when all vertices are visible (and thus all line segments are visible)."""
+    visualize = False
+    # 6 vertices in the polyline.
+    line_segments_arr: NDArrayInt = np.array([[50, 0], [50, 20], [50, 40], [50, 60], [50, 80], [60, 120]])
+
+    valid_pts_bool: NDArrayBool = np.array([True, True, True, True, True, True])
+    img_bgr: NDArrayByte = np.zeros((100, 100, 3), dtype=np.uint8)
+    map_rendering_utils.draw_visible_polyline_segments_cv2(
+        line_segments_arr=line_segments_arr,
+        valid_pts_bool=valid_pts_bool,
+        image=img_bgr,
+        color=RED_BGR,
+        thickness_px=2,
+    )
+    if visualize:
+        plt.imshow(img_bgr[:, :, ::-1])
+        plt.show()
diff --git a/tests/rendering/test_video.py b/tests/rendering/test_video.py
new file mode 100644
index 00000000..18947f70
--- /dev/null
+++ b/tests/rendering/test_video.py
@@ -0,0 +1,91 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests on rendering tools for video visualizations."""
+
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+
+import av2.rendering.video as video_utils
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+
+def generate_dummy_rgb_video(N: int, H: int, W: int) -> NDArrayByte:
+    """Generate dummy video data (increasing brightness from top-left to bottom-right, and as video progresses).
+
+    Args:
+        N: number of video frames to generate.
+        H: frame height, in pixels.
+        W: frame width, in pixels.
+
+    Returns:
+        tensor of shape (N,H,W,3)
+
+    Raises:
+        ValueError: if more than 55 frames are requested (to keep values in [0,200 + 55]).
+    """
+    if N > 55:
+        raise ValueError("Will overflow")
+
+    video: NDArrayByte = np.zeros((N, H, W, 3), dtype=np.uint8)
+    for frame_idx in np.arange(N):
+        frame_f: NDArrayFloat = np.arange(H * W).reshape(H, W).astype(np.float32)
+        frame_f /= frame_f.max()
+        frame_f *= 200.0
+        frame_f += frame_idx
+        frame: NDArrayByte = frame_f.astype(np.uint8)
+        for c in range(3):
+            video[frame_idx, :, :, c] = frame
+    return video
+
+
+def test_write_video_even_dims() -> None:
+    """Ensure we can encode a video tensor (with even H/W dimensions) as a mp4 file with AV, and save it to disk.
+
+    Dummy data is 30 frames of (60,60) RGB video.
+    """
+    video: NDArrayByte = generate_dummy_rgb_video(N=30, H=60, W=60)
+    save_fpath = Path(NamedTemporaryFile(suffix=".mp4").name)
+    assert not save_fpath.exists()
+
+    video_utils.write_video(
+        video=video,
+        dst=save_fpath,
+    )
+    assert save_fpath.exists()
+
+
+def test_write_video_odd_dims() -> None:
+    """Ensure we can encode a video tensor (with odd H/W dimensions) as a mp4 file with AV, and save it to disk.
+
+    Dummy data is 30 frames of (65,65) RGB video.
+    """
+    video: NDArrayByte = generate_dummy_rgb_video(N=30, H=65, W=65)
+    save_fpath = Path(NamedTemporaryFile(suffix=".mp4").name)
+    assert not save_fpath.exists()
+
+    video_utils.write_video(
+        video=video,
+        dst=save_fpath,
+    )
+    assert save_fpath.exists()
+
+
+def test_crop_video_to_even_dims() -> None:
+    """Ensure we can crop a video tensor along the height and width dimensions to assure even dimensions.
+
+    Dummy data is 55 frames of (501,501) RGB video.
+    """
+    video: NDArrayByte = generate_dummy_rgb_video(N=55, H=501, W=501)
+
+    cropped_video = video_utils.crop_video_to_even_dims(video)
+
+    assert cropped_video.shape == (55, 500, 500, 3)
+    assert cropped_video.dtype == np.dtype(np.uint8)
+
+    save_fpath = Path(NamedTemporaryFile(suffix=".mp4").name)
+    assert not save_fpath.exists()
+
+    video_utils.write_video(video=cropped_video, dst=save_fpath, fps=10, preset="medium")
+    assert save_fpath.exists()
diff --git a/tests/structures/test_cuboid.py b/tests/structures/test_cuboid.py
new file mode 100644
index 00000000..1eee301e
--- /dev/null
+++ b/tests/structures/test_cuboid.py
@@ -0,0 +1,252 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Units tests for cuboids."""
+
+from enum import Enum
+from typing import Any, Callable, List, Tuple
+
+import numpy as np
+import pytest
+
+from av2.datasets.sensor.constants import AnnotationCategories
+from av2.geometry.se3 import SE3
+from av2.structures.cuboid import Cuboid, CuboidList
+from av2.utils.typing import NDArrayBool, NDArrayFloat
+
+
+def test_vertices() -> None:
+    """Ensure that 8 cuboid vertices are located where we expect.
+
+    Cuboid center is placed at the origin.
+    """
+    dst_SE3_object = SE3(rotation=np.eye(3), translation=np.zeros(3))
+    cuboid = Cuboid(
+        dst_SE3_object=dst_SE3_object,
+        length_m=3,
+        width_m=2,
+        height_m=1,
+        category=AnnotationCategories.REGULAR_VEHICLE,
+        timestamp_ns=0,  # dummy value
+    )
+    vertices = cuboid.vertices_m
+
+    expected_vertices: NDArrayFloat = np.array(
+        [
+            [1.5, 1, 0.5],
+            [1.5, -1, 0.5],
+            [1.5, -1, -0.5],
+            [1.5, 1, -0.5],
+            [-1.5, 1, 0.5],
+            [-1.5, -1, 0.5],
+            [-1.5, -1, -0.5],
+            [-1.5, 1, -0.5],
+        ]
+    )
+    assert np.array_equal(vertices, expected_vertices)
+
+
+def test_compute_interior_points() -> None:
+    """Ensure that a point cloud is filtered correctly to a cuboid's interior.
+
+    Cuboid center is placed at (2,0,0) in the egovehicle frame here.
+    """
+    # fmt: off
+    points_xyz: NDArrayFloat = np.array(
+        [
+            [-1, 0, 0],
+            [0, 0, 0],  # start
+            [1, 0, 0],
+            [2, 0, 0],
+            [3, 0, 0],
+            [4, 0, 0],  # end
+            [5, 0, 0]
+        ], dtype=float  
+    )
+    # fmt: on
+    expected_is_interior: NDArrayBool = np.array([False, True, True, True, True, True, False])
+
+    dst_SE3_object = SE3(rotation=np.eye(3), translation=np.array([2, 0, 0]))
+
+    cuboid = Cuboid(
+        dst_SE3_object=dst_SE3_object,
+        length_m=4,
+        width_m=2,
+        height_m=1,
+        category=AnnotationCategories.REGULAR_VEHICLE,
+        timestamp_ns=0,  # dummy value
+    )
+    interior_pts, is_interior = cuboid.compute_interior_points(points_xyz)
+    expected_interior_pts: NDArrayFloat = np.array(
+        [
+            [0, 0, 0],  # start
+            [1, 0, 0],
+            [2, 0, 0],
+            [3, 0, 0],
+            [4, 0, 0],  # end
+        ],
+        dtype=float,
+    )
+    assert np.array_equal(interior_pts, expected_interior_pts)
+    assert np.array_equal(is_interior, expected_is_interior)
+
+
+def _get_dummy_cuboid_params() -> Tuple[SE3, float, float, float, Enum, int]:
+    """Create cuboid parameters to construct a `Cuboid` object."""
+    rotation: NDArrayFloat = np.eye(3)
+    translation: NDArrayFloat = np.array([1.0, 0.0, 0.0])
+    ego_SE3_obj = SE3(rotation=rotation, translation=translation)
+    length_m = 3.0
+    width_m = 2.0
+    height_m = 1.0
+    category = AnnotationCategories.BUS
+    timestamp_ns = 0
+
+    return ego_SE3_obj, length_m, width_m, height_m, category, timestamp_ns
+
+
+def _get_dummy_cuboid_list_params(num_cuboids: int) -> List[Cuboid]:
+    """Create a cuboid list of length `num_cuboids`."""
+    cuboids: List[Cuboid] = []
+    for i in range(num_cuboids):
+        ego_SE3_object, length_m, width_m, height_m, category, timestamp_ns = _get_dummy_cuboid_params()
+        cuboid = Cuboid(
+            dst_SE3_object=ego_SE3_object,
+            length_m=length_m + i,
+            width_m=width_m + i,
+            height_m=height_m + i,
+            category=category,
+            timestamp_ns=timestamp_ns,
+        )
+        cuboids.append(cuboid)
+    return cuboids
+
+
+def test_cuboid_constructor() -> None:
+    """Test initializing a single cuboid."""
+    ego_SE3_object, length_m, width_m, height_m, category, timestamp_ns = _get_dummy_cuboid_params()
+    cuboid = Cuboid(
+        dst_SE3_object=ego_SE3_object,
+        length_m=length_m,
+        width_m=width_m,
+        height_m=height_m,
+        category=category,
+        timestamp_ns=timestamp_ns,
+    )
+
+    rotation_expected = ego_SE3_object.rotation
+    translation_expected = ego_SE3_object.translation
+    length_m_expected = length_m
+    width_m_expected = width_m
+    height_m_expected = height_m
+
+    assert isinstance(cuboid, Cuboid)
+    assert np.array_equal(cuboid.dst_SE3_object.rotation, rotation_expected)
+    assert np.array_equal(cuboid.dst_SE3_object.translation, translation_expected)
+    assert cuboid.length_m == length_m_expected
+    assert cuboid.width_m == width_m_expected
+    assert cuboid.height_m == height_m_expected
+
+
+def test_cuboid_list_constructor_single() -> None:
+    """Test initializing a single cuboid."""
+    num_cuboids = 1
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    cuboid_list = CuboidList(cuboids)
+    assert isinstance(cuboid_list, CuboidList)
+
+
+def test_cuboid_list_constructor_multiple() -> None:
+    """Test initializing a list of cuboids."""
+    num_cuboids = 50
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    cuboid_list = CuboidList(cuboids)
+    assert isinstance(cuboid_list, CuboidList)
+
+
+def test_getitem() -> None:
+    """Ensure __getitem__ works as expected for access operations."""
+    num_cuboids = 50
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    cuboid_list = CuboidList(cuboids)
+    assert isinstance(cuboid_list, CuboidList)
+
+    assert isinstance(cuboid_list[5], Cuboid)
+
+    with pytest.raises(IndexError) as e_info:
+        cuboid_list[-1]
+
+    with pytest.raises(IndexError) as e_info:
+        cuboid_list[50]
+
+    with pytest.raises(IndexError) as e_info:
+        cuboid_list[51]
+
+
+def test_benchmark_cuboid_list_1k(benchmark: Callable[..., Any]) -> None:
+    """Test initializing a list of 1000 cuboids."""
+    num_cuboids = 1000
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    benchmark(CuboidList, cuboids)
+
+
+def test_benchmark_cuboid_list_10k(benchmark: Callable[..., Any]) -> None:
+    """Test initializing a list of 10,000 cuboids."""
+    num_cuboids = 10000
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    benchmark(CuboidList, cuboids)
+
+
+def test_benchmark_cuboid_list_100k(benchmark: Callable[..., Any]) -> None:
+    """Test initializing a list of 100,000 cuboids."""
+    num_cuboids = 100000
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    benchmark(CuboidList, cuboids)
+
+
+def test_benchmark_transform_for_loop(benchmark: Callable[..., Any]) -> None:
+    """Benchmark cuboid transform with a for loop."""
+
+    def benchmark_transform(cuboids: List[Cuboid], target_SE3_ego: SE3) -> List[Cuboid]:
+        transformed_cuboids: List[Cuboid] = []
+        for cuboid in cuboids:
+            target_SE3_object = target_SE3_ego.compose(cuboid.dst_SE3_object)
+            transformed_cuboid = Cuboid(
+                dst_SE3_object=target_SE3_object,
+                length_m=cuboid.length_m,
+                width_m=cuboid.width_m,
+                height_m=cuboid.height_m,
+                category=cuboid.category,
+                timestamp_ns=cuboid.timestamp_ns,
+            )
+            transformed_cuboids.append(transformed_cuboid)
+        return transformed_cuboids
+
+    num_cuboids = 1000
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    benchmark(benchmark_transform, cuboids, cuboids[0].dst_SE3_object)
+
+
+def test_benchmark_transform_list_comprehension(benchmark: Callable[..., Any]) -> None:
+    """Benchmark cuboid transform with list comprehension."""
+
+    def benchmark_transform_list_comprehension(cuboids: List[Cuboid], target_SE3_ego: SE3) -> List[Cuboid]:
+        transformed_cuboids: List[Cuboid] = [
+            Cuboid(
+                dst_SE3_object=target_SE3_ego.compose(cuboid.dst_SE3_object),
+                length_m=cuboid.length_m,
+                width_m=cuboid.width_m,
+                height_m=cuboid.height_m,
+                category=cuboid.category,
+                timestamp_ns=cuboid.timestamp_ns,
+            )
+            for cuboid in cuboids
+        ]
+        return transformed_cuboids
+
+    num_cuboids = 1000
+    cuboids = _get_dummy_cuboid_list_params(num_cuboids)
+    benchmark(benchmark_transform_list_comprehension, cuboids, cuboids[0].dst_SE3_object)
+
+
+if __name__ == "__main__":
+    test_getitem()
diff --git a/tests/structures/test_ndgrid.py b/tests/structures/test_ndgrid.py
new file mode 100644
index 00000000..97068142
--- /dev/null
+++ b/tests/structures/test_ndgrid.py
@@ -0,0 +1,89 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for N-dimensional grids."""
+
+import numpy as np
+
+from av2.rendering.color import GRAY_BGR
+from av2.structures.ndgrid import BEVGrid, NDGrid
+from av2.utils.typing import NDArrayFloat, NDArrayInt
+
+
+def test_ndgrid_3d() -> None:
+    """Unit tests for the NDGrid class."""
+    min_range_m = (-5.0, -5.0, -5.0)
+    max_range_m = (+5.0, +5.0, +5.0)
+    resolution_m_per_cell = (+0.1, +0.1, +0.1)
+
+    ndgrid = NDGrid(min_range_m, max_range_m, resolution_m_per_cell)
+
+    dims_expected = (100.0, 100.0, 100.0)
+    assert ndgrid.dims == dims_expected
+
+    range_m_expected = (10.0, 10.0, 10.0)
+    assert ndgrid.range_m == range_m_expected
+
+    points: NDArrayFloat = np.array([[1.11, 2.22, 3.33], [4.44, 5.55, 6.66]])
+    scaled_points = ndgrid.scale_points(points)
+    scaled_points_expected = [[11.1, 22.2, 33.3], [44.4, 55.5, 66.6]]
+    assert np.allclose(scaled_points, scaled_points_expected)
+
+    quantized_points = ndgrid.quantize_points(points)
+    quantized_points_expected = [[1, 2, 3], [4, 6, 7]]
+    assert np.allclose(quantized_points, quantized_points_expected)
+
+    grid_coordinates = ndgrid.transform_to_grid_coordinates(points)
+    grid_coordinates_expected = [[61, 72, 83], [94, 106, 117]]
+    assert np.allclose(grid_coordinates, grid_coordinates_expected)
+
+
+def test_bev_grid() -> None:
+    """Unit tests for the BEVGrid class."""
+    min_range_m = (-5.0, -5.0)
+    max_range_m = (+5.0, +5.0)
+    resolution_m_per_cell = (+0.1, +0.1)
+
+    bev_grid = BEVGrid(min_range_m, max_range_m, resolution_m_per_cell)
+
+    points: NDArrayFloat = np.array([[1.11, 2.22, 3.33], [4.44, 5.55, 6.66]])
+
+    color = GRAY_BGR
+    bev_img = bev_grid.points_to_bev_img(points, color=color)
+
+    grid_coordinates_expected: NDArrayInt = np.array([61, 72], dtype=int)
+    values_expected = GRAY_BGR
+    assert np.array_equal(bev_img[grid_coordinates_expected[1], grid_coordinates_expected[0]], values_expected)
+
+
+def test_BEVGrid_non_integer_multiple() -> None:
+    """Unit test with non-integer-multiple resolution."""
+    min_range_m = (-5.0, -5.0)
+    max_range_m = (+5.0, +5.0)
+    resolution_m_per_cell = (+0.3, +0.3)
+
+    bev_grid = BEVGrid(min_range_m, max_range_m, resolution_m_per_cell)
+
+    dims_expected = (33, 33)
+    assert bev_grid.dims == dims_expected
+
+    range_m_expected = (10.0, 10.0)
+    assert bev_grid.range_m == range_m_expected
+
+    points: NDArrayFloat = np.array([[1.11, 2.22], [4.44, 5.55]])
+    scaled_points = bev_grid.scale_points(points)
+    scaled_points_expected = [[3.7, 7.4], [14.8, 18.5]]
+    assert np.allclose(scaled_points, scaled_points_expected)
+
+    quantized_points = bev_grid.quantize_points(points)
+    quantized_points_expected = [[1, 2], [4, 6]]
+    assert np.allclose(quantized_points, quantized_points_expected)
+
+    grid_coordinates = bev_grid.transform_to_grid_coordinates(points)
+    grid_coordinates_expected = [[20, 24], [31, 35]]
+    assert np.allclose(grid_coordinates, grid_coordinates_expected)
+
+    color = GRAY_BGR
+
+    points2: NDArrayFloat = np.array([[4.99, 4.99]])
+    bev_img = bev_grid.points_to_bev_img(points2, color=color)
+    assert bev_img.sum() == 0  # round(4.99 / 0.3) is out of bounds, so it won't be set.
diff --git a/tests/structures/test_sweep.py b/tests/structures/test_sweep.py
new file mode 100644
index 00000000..ebb1c3ce
--- /dev/null
+++ b/tests/structures/test_sweep.py
@@ -0,0 +1,33 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for sweeps."""
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from av2.structures.sweep import Sweep
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt
+
+
+@pytest.fixture
+def dummy_sweep(test_data_root_dir: Path) -> Sweep:
+    """Get a fake sweep containing two points."""
+    path = test_data_root_dir / "sensor_dataset_logs" / "dummy" / "sensors" / "lidar" / "315968663259918000.feather"
+    return Sweep.from_feather(path)
+
+
+def test_sweep_from_feather(dummy_sweep: Sweep) -> None:
+    """Test loading a sweep from a feather file."""
+    xyz_expected: NDArrayFloat = np.array([[-22.1875, 20.484375, 0.55029296875], [-20.609375, 19.1875, 1.30078125]])
+    intensity_expected: NDArrayByte = np.array([38, 5], dtype=np.uint8)
+    laser_number_expected: NDArrayByte = np.array([19, 3], dtype=np.uint8)
+    offset_ns_expected: NDArrayInt = np.array([253440, 283392], dtype=np.int32)
+    timestamp_ns_expected: int = 315968663259918000
+
+    assert np.array_equal(dummy_sweep.xyz, xyz_expected)
+    assert np.array_equal(dummy_sweep.intensity, intensity_expected)
+    assert np.array_equal(dummy_sweep.laser_number, laser_number_expected)
+    assert np.array_equal(dummy_sweep.offset_ns, offset_ns_expected)
+    assert dummy_sweep.timestamp_ns == timestamp_ns_expected
diff --git a/tests/test_data/a_Sim2_b.json b/tests/test_data/a_Sim2_b.json
new file mode 100644
index 00000000..7f866103
--- /dev/null
+++ b/tests/test_data/a_Sim2_b.json
@@ -0,0 +1 @@
+{"R": [1, 0, 0, 1], "t": [3930, 3240], "s": 1.6666666666666667}
diff --git a/tests/test_data/a_Sim2_b___invalid.json b/tests/test_data/a_Sim2_b___invalid.json
new file mode 100644
index 00000000..006cc193
--- /dev/null
+++ b/tests/test_data/a_Sim2_b___invalid.json
@@ -0,0 +1 @@
+{"R": [1, 0, 0, 1], "t": [3930, 3240], "s": 0.0}
diff --git a/tests/test_data/forecasting_scenarios/0a1e6f0a-1817-4a98-b02e-db8c9327d151/log_map_archive_0a1e6f0a-1817-4a98-b02e-db8c9327d151.json b/tests/test_data/forecasting_scenarios/0a1e6f0a-1817-4a98-b02e-db8c9327d151/log_map_archive_0a1e6f0a-1817-4a98-b02e-db8c9327d151.json
new file mode 100644
index 00000000..5d53827a
--- /dev/null
+++ b/tests/test_data/forecasting_scenarios/0a1e6f0a-1817-4a98-b02e-db8c9327d151/log_map_archive_0a1e6f0a-1817-4a98-b02e-db8c9327d151.json
@@ -0,0 +1 @@
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diff --git a/tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/sensors/lidar/315973157959879000.feather b/tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/sensors/lidar/315973157959879000.feather
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diff --git a/tests/test_data/static_maps/dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076/log_map_archive_dummy_log_map_v2_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076.json b/tests/test_data/static_maps/dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076/log_map_archive_dummy_log_map_v2_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076.json
new file mode 100644
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--- /dev/null
+++ b/tests/test_data/static_maps/dummy_log_map_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076/log_map_archive_dummy_log_map_v2_gs1B8ZCv7DMi8cMt5aN5rSYjQidJXvGP__2020-07-21-Z1F0076.json
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+                {"x": 895.26, "y": -99.96, "z": -19.60},
+                {"x": 896.97, "y": -99.84, "z": -19.53},
+                {"x": 897.82, "y": -99.74, "z": -19.50},
+                {"x": 898.49, "y": -99.57, "z": -19.49},
+                {"x": 899.11, "y": -99.26, "z": -19.47},
+                {"x": 899.59, "y": -98.90, "z": -19.45},
+                {"x": 900.02, "y": -98.45, "z": -19.45},
+                {"x": 900.34, "y": -98.01, "z": -19.44},
+                {"x": 900.58, "y": -97.54, "z": -19.45},
+                {"x": 900.72, "y": -97.13, "z": -19.45},
+                {"x": 900.80, "y": -96.67, "z": -19.46},
+                {"x": 900.80, "y": -96.11, "z": -19.47},
+                {"x": 900.73, "y": -95.65, "z": -19.49},
+                {"x": 899.49, "y": -89.87, "z": -19.64},
+                {"x": 896.75, "y": -77.12, "z": -19.92},
+                {"x": 892.92, "y": -60.00, "z": -20.12},
+                {"x": 907.23, "y": -60.00, "z": -19.87},
+                {"x": 908.08, "y": -63.96, "z": -19.80},
+                {"x": 910.96, "y": -76.80, "z": -19.74},
+                {"x": 913.32, "y": -87.74, "z": -19.64},
+                {"x": 914.57, "y": -93.04, "z": -19.59},
+                {"x": 915.10, "y": -94.59, "z": -19.57},
+                {"x": 915.64, "y": -95.75, "z": -19.54},
+                {"x": 916.16, "y": -96.66, "z": -19.51},
+                {"x": 916.70, "y": -97.31, "z": -19.48},
+                {"x": 917.45, "y": -97.97, "z": -19.45},
+                {"x": 918.36, "y": -98.53, "z": -19.42},
+                {"x": 919.22, "y": -98.88, "z": -19.40},
+                {"x": 919.88, "y": -99.06, "z": -19.38},
+                {"x": 920.43, "y": -99.15, "z": -19.38},
+                {"x": 921.64, "y": -99.19, "z": -19.35},
+                {"x": 935.29, "y": -98.46, "z": -19.16},
+                {"x": 939.25, "y": -98.38, "z": -19.17},
+                {"x": 939.75, "y": -98.40, "z": -19.17},
+                {"x": 940.25, "y": -98.35, "z": -19.17},
+                {"x": 941.40, "y": -98.04, "z": -19.17},
+                {"x": 942.21, "y": -97.73, "z": -19.17},
+                {"x": 942.88, "y": -97.37, "z": -19.20},
+                {"x": 943.62, "y": -96.86, "z": -19.25},
+                {"x": 944.35, "y": -96.23, "z": -19.28},
+                {"x": 945.00, "y": -95.52, "z": -19.29},
+                {"x": 945.72, "y": -94.61, "z": -19.29},
+                {"x": 946.19, "y": -93.80, "z": -19.29},
+                {"x": 946.45, "y": -93.15, "z": -19.30},
+                {"x": 946.64, "y": -92.49, "z": -19.31},
+                {"x": 946.74, "y": -91.98, "z": -19.32},
+                {"x": 946.80, "y": -91.38, "z": -19.34},
+                {"x": 946.78, "y": -89.96, "z": -19.39},
+                {"x": 946.50, "y": -88.20, "z": -19.44},
+                {"x": 945.93, "y": -85.65, "z": -19.43},
+                {"x": 945.11, "y": -81.60, "z": -19.45},
+                {"x": 944.11, "y": -77.36, "z": -19.45},
+                {"x": 942.23, "y": -68.96, "z": -19.48},
+                {"x": 941.67, "y": -66.11, "z": -19.52},
+                {"x": 940.39, "y": -60.21, "z": -19.64},
+                {"x": 940.34, "y": -60.00, "z": -19.61},
+                {"x": 954.60, "y": -60.00, "z": -19.44},
+                {"x": 956.56, "y": -68.39, "z": -19.38},
+                {"x": 960.37, "y": -85.83, "z": -19.24},
+                {"x": 960.97, "y": -89.87, "z": -19.20},
+                {"x": 961.03, "y": -89.87, "z": -19.19},
+                {"x": 961.58, "y": -93.98, "z": -19.18},
+                {"x": 963.88, "y": -117.83, "z": -18.87},
+                {"x": 964.77, "y": -129.38, "z": -18.69},
+                {"x": 965.17, "y": -137.01, "z": -18.61},
+                {"x": 965.32, "y": -140.43, "z": -18.58},
+                {"x": 965.66, "y": -150.00, "z": -18.47},
+                {"x": 951.91, "y": -150.00, "z": -18.67},
+                {"x": 951.71, "y": -144.99, "z": -18.71},
+                {"x": 951.66, "y": -143.11, "z": -18.73},
+                {"x": 951.36, "y": -136.81, "z": -18.80},
+                {"x": 951.32, "y": -135.06, "z": -18.83},
+                {"x": 950.38, "y": -124.41, "z": -18.94},
+                {"x": 950.02, "y": -121.16, "z": -18.97},
+                {"x": 949.79, "y": -118.61, "z": -19.03},
+                {"x": 949.06, "y": -114.00, "z": -19.05},
+                {"x": 948.86, "y": -112.90, "z": -19.06},
+                {"x": 948.70, "y": -112.30, "z": -19.06},
+                {"x": 948.36, "y": -111.48, "z": -19.06},
+                {"x": 948.01, "y": -110.91, "z": -19.05},
+                {"x": 947.39, "y": -110.29, "z": -19.05},
+                {"x": 946.94, "y": -109.99, "z": -19.05},
+                {"x": 946.61, "y": -109.88, "z": -19.05},
+                {"x": 946.32, "y": -109.84, "z": -19.05},
+                {"x": 945.88, "y": -109.87, "z": -19.05},
+                {"x": 945.56, "y": -109.95, "z": -19.06},
+                {"x": 945.25, "y": -110.05, "z": -19.06},
+                {"x": 944.98, "y": -110.20, "z": -19.07},
+                {"x": 944.38, "y": -110.66, "z": -19.06},
+                {"x": 943.97, "y": -111.15, "z": -19.06},
+                {"x": 943.73, "y": -111.65, "z": -19.06},
+                {"x": 943.61, "y": -112.21, "z": -19.06},
+                {"x": 943.55, "y": -113.32, "z": -19.05},
+                {"x": 943.59, "y": -113.83, "z": -19.04},
+                {"x": 943.84, "y": -115.38, "z": -19.02},
+                {"x": 944.31, "y": -119.62, "z": -19.05},
+                {"x": 944.78, "y": -124.42, "z": -18.98},
+                {"x": 945.13, "y": -129.12, "z": -18.93},
+                {"x": 945.28, "y": -132.22, "z": -18.91},
+                {"x": 945.62, "y": -141.67, "z": -18.80},
+                {"x": 945.74, "y": -146.47, "z": -18.75},
+                {"x": 945.87, "y": -150.00, "z": -18.71},
+                {"x": 938.03, "y": -150.00, "z": -18.81},
+                {"x": 938.02, "y": -149.46, "z": -18.81},
+                {"x": 937.81, "y": -144.71, "z": -18.86},
+                {"x": 937.73, "y": -141.56, "z": -18.89},
+                {"x": 937.58, "y": -138.56, "z": -18.91},
+                {"x": 937.40, "y": -132.21, "z": -18.96},
+                {"x": 937.11, "y": -127.46, "z": -19.00},
+                {"x": 936.83, "y": -123.41, "z": -19.05},
+                {"x": 936.54, "y": -120.71, "z": -19.09},
+                {"x": 936.13, "y": -117.66, "z": -19.10},
+                {"x": 935.63, "y": -115.69, "z": -19.10},
+                {"x": 935.34, "y": -114.93, "z": -19.11},
+                {"x": 934.89, "y": -114.12, "z": -19.11},
+                {"x": 934.38, "y": -113.44, "z": -19.10},
+                {"x": 933.73, "y": -112.78, "z": -19.10},
+                {"x": 932.98, "y": -112.16, "z": -19.10},
+                {"x": 932.18, "y": -111.64, "z": -19.10},
+                {"x": 931.17, "y": -111.12, "z": -19.11},
+                {"x": 930.26, "y": -110.77, "z": -19.12},
+                {"x": 929.30, "y": -110.55, "z": -19.13},
+                {"x": 927.89, "y": -110.48, "z": -19.15},
+                {"x": 924.09, "y": -110.62, "z": -19.17},
+                {"x": 921.14, "y": -110.79, "z": -19.18},
+                {"x": 920.29, "y": -110.88, "z": -19.19},
+                {"x": 919.62, "y": -111.04, "z": -19.20},
+                {"x": 918.91, "y": -111.34, "z": -19.21},
+                {"x": 918.38, "y": -111.71, "z": -19.21},
+                {"x": 917.96, "y": -112.16, "z": -19.21},
+                {"x": 917.58, "y": -112.78, "z": -19.21},
+                {"x": 917.36, "y": -113.45, "z": -19.21},
+                {"x": 917.28, "y": -114.21, "z": -19.21},
+                {"x": 917.34, "y": -115.87, "z": -19.21},
+                {"x": 917.63, "y": -119.12, "z": -19.20},
+                {"x": 917.71, "y": -120.72, "z": -19.22},
+                {"x": 917.88, "y": -122.77, "z": -19.19},
+                {"x": 918.29, "y": -130.42, "z": -19.10},
+                {"x": 918.55, "y": -133.17, "z": -19.06},
+                {"x": 918.60, "y": -134.47, "z": -19.05},
+                {"x": 918.84, "y": -137.87, "z": -19.01},
+                {"x": 918.89, "y": -139.27, "z": -19.00},
+                {"x": 919.03, "y": -140.72, "z": -18.98},
+                {"x": 919.32, "y": -145.77, "z": -18.91},
+                {"x": 919.48, "y": -150.00, "z": -18.86},
+                {"x": 905.14, "y": -150.00, "z": -19.18}
+            ],
+            "id": 4499430
+        }
+    }
+}
diff --git a/tests/utils/test_dense_grid_interpolation.py b/tests/utils/test_dense_grid_interpolation.py
new file mode 100644
index 00000000..3b36023a
--- /dev/null
+++ b/tests/utils/test_dense_grid_interpolation.py
@@ -0,0 +1,96 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests on utilities that perform dense 2d grid interpolation from sparse values."""
+from typing import Final, Tuple
+
+import numpy as np
+
+import av2.utils.dense_grid_interpolation as dense_grid_interpolation
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt
+
+RED_RGB: Final[Tuple[int, int, int]] = (255, 0, 0)
+GREEN_RGB: Final[Tuple[int, int, int]] = (0, 255, 0)
+BLUE_RGB: Final[Tuple[int, int, int]] = (0, 0, 255)
+
+
+def test_interp_dense_grid_from_sparse_insufficient_points_simplex() -> None:
+    """Try to interpolate a dense grid using an insufficient number of samples.
+
+    Attempts interpolation with 2 data points (need at least 4).
+    """
+    bev_img: NDArrayByte = np.zeros((10, 10, 3), dtype=np.uint8)
+    points: NDArrayInt = np.array([[1, 1], [5, 5]])
+    rgb_values: NDArrayByte = np.array([RED_RGB, GREEN_RGB], dtype=np.uint8)
+    grid_h = 10
+    grid_w = 10
+
+    bev_img_interp = dense_grid_interpolation.interp_dense_grid_from_sparse(
+        grid_img=bev_img, points=points, values=rgb_values, grid_h=grid_h, grid_w=grid_w, interp_method="linear"
+    )
+    assert np.allclose(bev_img_interp, np.zeros((10, 10, 3), dtype=np.uint8))
+
+
+def test_interp_dense_grid_from_sparse_byte() -> None:
+    """Interpolate *uint8* vals using linear interpolation, with color data points provided in 4 corners of a square.
+
+     ___       __
+    |Red     Red |
+
+    |Green  Blue |
+    |___      ___|
+
+    """
+    bev_img: NDArrayByte = np.zeros((4, 4, 3), dtype=np.uint8)
+
+    # provided as (x,y) tuples
+    points: NDArrayInt = np.array([[0, 0], [0, 3], [3, 3], [3, 0]])
+    rgb_values: NDArrayByte = np.array([RED_RGB, GREEN_RGB, BLUE_RGB, RED_RGB], dtype=np.uint8)
+    grid_h = 4
+    grid_w = 4
+
+    bev_img_interp = dense_grid_interpolation.interp_dense_grid_from_sparse(
+        grid_img=bev_img, points=points, values=rgb_values, grid_h=grid_h, grid_w=grid_w, interp_method="linear"
+    )
+
+    assert bev_img_interp.dtype == np.dtype(np.uint8)
+    assert isinstance(bev_img_interp, np.ndarray)
+    assert bev_img_interp.shape == (4, 4, 3)
+
+    # now, index in at (y,x)
+    assert np.allclose(bev_img_interp[0, 0], RED_RGB)
+    assert np.allclose(bev_img_interp[3, 0], GREEN_RGB)
+    assert np.allclose(bev_img_interp[3, 3], BLUE_RGB)
+    assert np.allclose(bev_img_interp[0, 3], RED_RGB)
+
+
+def test_interp_dense_grid_from_sparse_float() -> None:
+    """Interpolate *float* vals using linear interpolation, with color data points provided in 4 corners of a square.
+
+     ___       __
+    |Red     Red |
+
+    |Green  Blue |
+    |___      ___|
+
+    """
+    bev_img: NDArrayFloat = np.zeros((4, 4, 3), dtype=float)
+
+    # provided as (x,y) tuples
+    points: NDArrayInt = np.array([[0, 0], [0, 3], [3, 3], [3, 0]], dtype=int)
+    rgb_values: NDArrayFloat = np.array([RED_RGB, GREEN_RGB, BLUE_RGB, RED_RGB], dtype=float)
+    grid_h = 4
+    grid_w = 4
+
+    bev_img_interp = dense_grid_interpolation.interp_dense_grid_from_sparse(
+        grid_img=bev_img, points=points, values=rgb_values, grid_h=grid_h, grid_w=grid_w, interp_method="linear"
+    )
+
+    assert bev_img_interp.dtype == np.dtype(np.float64)
+    assert isinstance(bev_img_interp, np.ndarray)
+    assert bev_img_interp.shape == (4, 4, 3)
+
+    # now, index in at (y,x)
+    assert np.allclose(bev_img_interp[0, 0], RED_RGB)
+    assert np.allclose(bev_img_interp[3, 0], GREEN_RGB)
+    assert np.allclose(bev_img_interp[3, 3], BLUE_RGB)
+    assert np.allclose(bev_img_interp[0, 3], RED_RGB)
diff --git a/tests/utils/test_depth_map_utils.py b/tests/utils/test_depth_map_utils.py
new file mode 100644
index 00000000..d0a0390a
--- /dev/null
+++ b/tests/utils/test_depth_map_utils.py
@@ -0,0 +1,31 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for depth map utilities."""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import av2.utils.depth_map_utils as depth_map_utils
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+
+def test_vis_depth_map() -> None:
+    """Ensure a dummy RGB image and a dummy depth map can be plotted side by side."""
+    visualize = False
+
+    H = 1000
+    W = 2000
+    img_rgb: NDArrayByte = np.zeros((H, W, 3), dtype=np.uint8)
+    img_rgb[:, :, 0] = 255  # channels will be (255,0,0) for red.
+
+    depth_map: NDArrayFloat = np.arange(H * W).reshape(H, W).astype(np.float32) / (H * W) * 255
+
+    depth_map_utils.vis_depth_map(img_rgb=img_rgb, depth_map=depth_map, interp_depth_map=True)
+    if visualize:
+        plt.show()
+        plt.close("all")
+
+    depth_map_utils.vis_depth_map(img_rgb=img_rgb, depth_map=depth_map, interp_depth_map=False)
+    if visualize:
+        plt.show()
+        plt.close("all")
diff --git a/tests/utils/test_dilate_utils_unit.py b/tests/utils/test_dilate_utils_unit.py
new file mode 100644
index 00000000..e8644fee
--- /dev/null
+++ b/tests/utils/test_dilate_utils_unit.py
@@ -0,0 +1,83 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Unit tests for dilation_utils.py."""
+
+import numpy as np
+
+import av2.utils.dilation_utils as dilation_utils
+from av2.utils.typing import NDArrayByte
+
+
+def test_dilate_by_l2_no_change() -> None:
+    """Test dilate_by_l2() and expect no change.
+
+    Do not modify the following 8-bit image (0 px dilation)
+    0 0 0 0 0
+    0 x x x 0
+    0 x x x 0
+    0 x x x 0
+    0 0 0 0 0
+    """
+    img: NDArrayByte = np.zeros((5, 5), dtype=np.uint8)
+    img[1:4, 1:4] = 1
+    dilated_img = dilation_utils.dilate_by_l2(img.copy(), dilation_thresh=0.0)
+    assert np.allclose(img, dilated_img)
+
+
+def test_dilate_by_l2_2x2square_1px() -> None:
+    """Test dilate_by_l2() with a 1 pixel dilation.
+
+    Dilate the following 8-bit image w/ 1 px border
+
+    0 0 0 0 0 0    0 0 0 0 0 0
+    0 0 0 0 0 0    0 0 x x 0 0
+    0 0 x x 0 0 -> 0 x x x x 0
+    0 0 x x 0 0    0 x x x x 0
+    0 0 0 0 0 0    0 0 x x 0 0
+    0 0 0 0 0 0    0 0 0 0 0 0
+    """
+    img: NDArrayByte = np.zeros((6, 6), dtype=np.uint8)
+    img[2:4, 2:4] = 1
+    dilated_img = dilation_utils.dilate_by_l2(img, dilation_thresh=1.0)
+    print(dilated_img)
+    # ground truth
+    dilated_img_gt: NDArrayByte = np.array(
+        [
+            [0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 1, 1, 0],
+            [0, 1, 1, 1, 1, 0],
+            [0, 0, 1, 1, 0, 0],
+            [0, 0, 0, 0, 0, 0],
+        ],
+        dtype=np.uint8,
+    )
+    assert np.allclose(dilated_img, dilated_img_gt)
+
+
+def test_dilate_by_l2_horizline_1point42px() -> None:
+    """Test dilate_by_l2() with a non-integer dilation that should get cut off.
+
+    Dilate the following 8-bit image w/ 1.42 px border,
+    allowing us to access diagonal neighbors:
+
+    0 0 0 0 0    0 0 0 0 0
+    0 0 0 0 0    0 x x x x
+    0 0 x x 0 -> 0 x x x x
+    0 0 0 0 0    0 x x x x
+    0 0 0 0 0    0 0 0 0 0
+    """
+    img: NDArrayByte = np.zeros((5, 5), dtype=np.uint8)
+    img[2, 2:4] = 1
+    dilated_img = dilation_utils.dilate_by_l2(img, dilation_thresh=1.42)
+    # ground truth
+    dilated_img_gt: NDArrayByte = np.array(
+        [
+            [0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0],
+        ],
+        dtype=np.uint8,
+    )
+    assert np.allclose(dilated_img, dilated_img_gt)
diff --git a/tests/utils/test_infinity_norm_utils.py b/tests/utils/test_infinity_norm_utils.py
new file mode 100644
index 00000000..369575f0
--- /dev/null
+++ b/tests/utils/test_infinity_norm_utils.py
@@ -0,0 +1,57 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for infinity norm utilities."""
+
+import numpy as np
+
+import av2.geometry.infinity_norm_utils as infinity_norm_utils
+from av2.utils.typing import NDArrayFloat
+
+
+def test_has_pts_in_infinity_norm_radius1() -> None:
+    """No points within radius."""
+    # fmt: off
+    pts: NDArrayFloat = np.array(
+        [
+            [5.1, 0],
+            [0, -5.1],
+            [5.1, 5.1]
+        ])
+    # fmt: on
+    within = infinity_norm_utils.has_pts_in_infinity_norm_radius(pts, window_center=np.zeros(2), window_sz=5)
+    assert not within
+
+
+def test_has_pts_in_infinity_norm_radius2() -> None:
+    """1 point within radius."""
+    # fmt: off
+    pts: NDArrayFloat = np.array(
+        [
+            [4.9, 0],
+            [0, -5.1],
+            [5.1, 5.1]
+        ])
+    # fmt: on
+    within = infinity_norm_utils.has_pts_in_infinity_norm_radius(pts, window_center=np.zeros(2), window_sz=5)
+    assert within
+
+
+def test_has_pts_in_infinity_norm_radius3() -> None:
+    """All pts within radius."""
+    # fmt: off
+    pts: NDArrayFloat = np.array(
+        [
+            [4.9, 0],
+            [0, -4.9],
+            [4.9, 4.9]
+        ])
+    # fmt: on
+    within = infinity_norm_utils.has_pts_in_infinity_norm_radius(pts, window_center=np.zeros(2), window_sz=5)
+    assert within
+
+
+def test_has_pts_in_infinity_norm_radius4() -> None:
+    """All pts within radius."""
+    pts: NDArrayFloat = np.array([[4.9, 4.9]])
+    within = infinity_norm_utils.has_pts_in_infinity_norm_radius(pts, window_center=np.zeros(2), window_sz=5)
+    assert within
diff --git a/tests/utils/test_io.py b/tests/utils/test_io.py
new file mode 100644
index 00000000..be66b96f
--- /dev/null
+++ b/tests/utils/test_io.py
@@ -0,0 +1,40 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for IO related utilities."""
+
+from pathlib import Path
+
+import numpy as np
+
+import av2.utils.io as io_utils
+from av2.utils.io import read_ego_SE3_sensor, read_feather
+
+
+def test_read_feather(test_data_root_dir: Path) -> None:
+    """Read an Apache Feather file."""
+    feather_path = test_data_root_dir / "sensor_dataset_logs" / "test_log" / "calibration" / "intrinsics.feather"
+    feather_file = read_feather(feather_path)
+
+    assert feather_file is not None
+    assert len(feather_file) > 0
+
+
+def test_read_ego_SE3_sensor(test_data_root_dir: Path) -> None:
+    """Read sensor extrinsics for a particular log."""
+    ego_SE3_sensor_path = test_data_root_dir / "sensor_dataset_logs" / "test_log"
+    sensor_name_to_sensor_pose = read_ego_SE3_sensor(ego_SE3_sensor_path)
+
+    assert sensor_name_to_sensor_pose is not None
+    assert len(sensor_name_to_sensor_pose) > 0
+
+
+def test_read_lidar_sweep() -> None:
+    """Read 3d point coordinates from a LiDAR sweep file from an example log."""
+    log_id = "adcf7d18-0510-35b0-a2fa-b4cea13a6d76"
+    EXAMPLE_LOG_DATA_ROOT = Path(__file__).resolve().parent.parent / "test_data" / "sensor_dataset_logs" / log_id
+
+    fpath = EXAMPLE_LOG_DATA_ROOT / "sensors" / "lidar" / "315973157959879000.feather"
+    arr = io_utils.read_lidar_sweep(fpath, attrib_spec="xyz")
+
+    assert arr.shape == (100660, 3)
+    assert arr.dtype == np.dtype(np.float64)
diff --git a/tests/utils/test_mesh_grid.py b/tests/utils/test_mesh_grid.py
new file mode 100644
index 00000000..731aa881
--- /dev/null
+++ b/tests/utils/test_mesh_grid.py
@@ -0,0 +1,102 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for mesh grid related utilities."""
+
+from typing import List
+
+import numpy as np
+
+import av2.geometry.mesh_grid as mesh_grid_utils
+from av2.utils.typing import NDArrayFloat
+
+
+def test_get_mesh_grid_as_point_cloud_3x3square() -> None:
+    """Ensure a sampled regular grid returns 9 grid points from 1 meter resolution on 2x2 meter area."""
+    min_x = -3  # integer, minimum x-coordinate of 2D grid
+    max_x = -1  # integer, maximum x-coordinate of 2D grid
+    min_y = 2  # integer, minimum y-coordinate of 2D grid
+    max_y = 4  # integer, maximum y-coordinate of 2D grid
+
+    # return pts, a Numpy array of shape (N,2)
+    pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(min_x, max_x, min_y, max_y, downsample_factor=1.0)
+
+    assert pts.shape == (9, 2)
+    gt_pts: NDArrayFloat = np.array(
+        [
+            [-3.0, 2.0],
+            [-2.0, 2.0],
+            [-1.0, 2.0],
+            [-3.0, 3.0],
+            [-2.0, 3.0],
+            [-1.0, 3.0],
+            [-3.0, 4.0],
+            [-2.0, 4.0],
+            [-1.0, 4.0],
+        ]
+    )
+
+    assert np.allclose(gt_pts, pts)
+
+
+def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:
+    """Ensure a sampled regular grid returns 6 grid points from 1 meter resolution on 1x2 meter area."""
+    min_x = -3  # integer, minimum x-coordinate of 2D grid
+    max_x = -1  # integer, maximum x-coordinate of 2D grid
+    min_y = 2  # integer, minimum y-coordinate of 2D grid
+    max_y = 3  # integer, maximum y-coordinate of 2D grid
+
+    # return pts, a Numpy array of shape (N,2)
+    pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(min_x, max_x, min_y, max_y, downsample_factor=1.0)
+
+    assert pts.shape == (6, 2)
+    # fmt: off
+    gt_pts: NDArrayFloat = np.array(
+        [
+            [-3.0, 2.0],
+            [-2.0, 2.0],
+            [-1.0, 2.0],
+            [-3.0, 3.0],
+            [-2.0, 3.0],
+            [-1.0, 3.0]
+        ])
+    # fmt: on
+    assert np.allclose(gt_pts, pts)
+
+
+def test_get_mesh_grid_as_point_cloud_single_pt() -> None:
+    """Ensure a sampled regular grid returns only 1 point for a range of 0 meters in x and 0 meters in y."""
+    min_x = -3  # integer, minimum x-coordinate of 2D grid
+    max_x = -3  # integer, maximum x-coordinate of 2D grid
+    min_y = 2  # integer, minimum y-coordinate of 2D grid
+    max_y = 2  # integer, maximum y-coordinate of 2D grid
+
+    # return pts, a Numpy array of shape (N,2)
+    pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(min_x, max_x, min_y, max_y, downsample_factor=1.0)
+
+    assert pts.shape == (1, 2)
+    gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])
+
+    assert np.allclose(gt_pts, pts)
+
+
+def test_get_mesh_grid_as_point_cloud_downsample() -> None:
+    """Given 3x3 area, ensure a sampled regular grid returns coordinates at 4 corners only."""
+    min_x = -3  # integer, minimum x-coordinate of 2D grid
+    max_x = 0  # integer, maximum x-coordinate of 2D grid
+    min_y = 2  # integer, minimum y-coordinate of 2D grid
+    max_y = 5  # integer, maximum y-coordinate of 2D grid
+
+    # return pts, a Numpy array of shape (N,2)
+    pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(min_x, max_x, min_y, max_y, downsample_factor=3.0)
+
+    assert pts.shape == (4, 2)
+
+    # fmt: off
+    gt_pts: List[List[float]] = [
+        [-3.0, 2.0],
+        [0.0, 2.0],
+        [-3.0, 5.0],
+        [0.0, 5.0]
+    ]
+    # fmt: on
+    assert np.allclose(gt_pts, pts)
diff --git a/tests/utils/test_metric_time.py b/tests/utils/test_metric_time.py
new file mode 100644
index 00000000..77e5b946
--- /dev/null
+++ b/tests/utils/test_metric_time.py
@@ -0,0 +1,37 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for metric time."""
+
+import numpy as np
+
+from av2.utils.metric_time import TimeUnit, to_metric_time
+
+
+def test_nanoseconds_to_seconds() -> None:
+    """Test conversion from nanoseconds to seconds."""
+    ts_ns = 950000000
+    ts_s = to_metric_time(ts=ts_ns, src=TimeUnit.Nanosecond, dst=TimeUnit.Second)
+    assert np.isclose(ts_s, 0.95)
+    assert np.isclose(ts_s, ts_ns / 1e9)
+
+
+def test_seconds_to_nanoseconds() -> None:
+    """Test conversion from seconds to nanoseconds."""
+    ts_s = 0.95
+    ts_ns = to_metric_time(ts=ts_s, src=TimeUnit.Second, dst=TimeUnit.Nanosecond)
+    assert np.isclose(ts_ns, 950000000)
+    assert np.isclose(ts_ns, ts_s * 1e9)
+
+
+def test_milliseconds_to_seconds() -> None:
+    """Test conversion from milliseconds to seconds."""
+    ts_ms = 300
+    ts_s = to_metric_time(ts=ts_ms, src=TimeUnit.Millisecond, dst=TimeUnit.Second)
+    assert np.isclose(ts_s, 0.3)
+
+
+def test_seconds_to_milliseconds() -> None:
+    """Test conversion from seconds to milliseconds."""
+    ts_s = 0.3
+    ts_ms = to_metric_time(ts=ts_s, src=TimeUnit.Second, dst=TimeUnit.Millisecond)
+    assert np.isclose(ts_ms, 300)
diff --git a/tests/utils/test_polyline_utils.py b/tests/utils/test_polyline_utils.py
new file mode 100644
index 00000000..1558861a
--- /dev/null
+++ b/tests/utils/test_polyline_utils.py
@@ -0,0 +1,100 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for polyline related utilities."""
+
+import numpy as np
+
+import av2.geometry.polyline_utils as polyline_utils
+from av2.utils.typing import NDArrayFloat
+
+
+def test_convert_lane_boundaries_to_polygon_3d() -> None:
+    """Ensure 3d lane polygon is created correctly from 3d left and right lane boundaries."""
+    # fmt: off
+    right_ln_bnd: NDArrayFloat = np.array(
+        [
+            [7, 3, 5],
+            [11, -1, 4]
+        ])
+    left_ln_bnd: NDArrayFloat = np.array(
+        [
+            [10, 3, 7],
+            [14, -1, 8]
+        ])
+    # fmt: on
+    polygon = polyline_utils.convert_lane_boundaries_to_polygon(right_ln_bnd, left_ln_bnd)
+
+    # fmt: off
+    gt_polygon: NDArrayFloat = np.array(
+        [
+            [7, 3, 5],
+            [11, -1, 4],
+            [14, -1, 8],
+            [10, 3, 7],
+            [7, 3, 5]
+        ])
+    # fmt: on
+    assert np.allclose(polygon, gt_polygon)
+
+
+def test_straight_centerline_to_polygon() -> None:
+    """Try converting a simple straight polyline into a polygon.
+
+    Represents the conversion from a centerline to a lane segment polygon.
+
+    Note that the returned polygon will ba a Numpy array of
+    shape (2N+1,2), with duplicate first and last vertices.
+    Dots below signify the centerline coordinates.
+
+            |   .   |
+            |   .   |
+            |   .   |
+            |   .   |
+    """
+    # create centerline: Numpy array of shape (N,2)
+    # fmt: off
+    centerline: NDArrayFloat = np.array(
+        [
+            [0, 2.0],
+            [0.0, 0.0],
+            [0.0, -2.0]
+        ])
+    # fmt: on
+
+    polygon = polyline_utils.centerline_to_polygon(centerline, width_scaling_factor=2)
+    # polygon wraps around with right boundary, then reversed
+    # left boundary, then back to start vertex
+    gt_polygon: NDArrayFloat = np.array(
+        [
+            [-3.8, 2.0],
+            [-3.8, 0.0],
+            [-3.8, -2.0],
+            [3.8, -2.0],
+            [3.8, 0.0],
+            [3.8, 2.0],
+            [-3.8, 2.0],
+        ]
+    )
+
+    # assert np.array_equal(polygon, gt_polygon)
+    assert polygon.shape == (7, 2)
+
+
+def test_centerline_to_polygon() -> None:
+    """Ensure correctness of conversion of centerline->two parallel lines for degenerate case.
+
+    Ensure we can extract two parallel lines (connected to form a polygon) from a central polyline,
+    when some central waypoints have zero slope in dy direction.
+    """
+    # fmt: off
+    centerline: NDArrayFloat = np.array(
+        [
+            [22.87, 6.56],
+            [29.93, 6.82],
+            [30.0, 6.82]
+        ],
+        dtype=float
+    )
+    # fmt: on
+    polygon = polyline_utils.centerline_to_polygon(centerline, width_scaling_factor=0.1)
+    assert polygon.shape == (7, 2)
diff --git a/tests/utils/test_raster.py b/tests/utils/test_raster.py
new file mode 100644
index 00000000..f379d15b
--- /dev/null
+++ b/tests/utils/test_raster.py
@@ -0,0 +1,192 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for rasterization related utilities."""
+
+from typing import Any, Callable
+
+import cv2
+import numpy as np
+
+import av2.utils.raster as raster_utils
+from av2.utils.typing import NDArrayByte, NDArrayFloat
+
+
+def test_get_mask_from_polygon() -> None:
+    """Ensure that a triangle and skinny-column-like rectangle can be correctly rasterized onto a square grid."""
+    # fmt: off
+    triangle: NDArrayFloat = np.array(
+        [
+            [1, 1],
+            [1, 3],
+            [3, 1]
+        ]
+    )
+    rectangle: NDArrayFloat = np.array(
+        [
+            [5, 1],
+            [5, 4],
+            [5.5, 4],
+            [5.5, 1]
+        ]
+    )
+    # fmt: on
+    mask = raster_utils.get_mask_from_polygons(polygons=[triangle, rectangle], img_h=7, img_w=7)
+
+    # fmt: off
+    expected_mask: NDArrayByte = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 1, 0],
+            [0, 1, 1, 0, 0, 1, 0],
+            [0, 1, 0, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0],
+        ]
+    ).astype(np.uint8)
+    # fmt: on
+    assert np.array_equal(mask, expected_mask)
+    assert mask.dtype == expected_mask.dtype
+
+
+def test_get_mask_from_polygon_repeated_coords() -> None:
+    """Verify polygon rasterization works correctly when the first coordinate is repeated (as last coordinate).
+
+    Note: same scenario as above, a square grid with 2 polygons: a triangle and skinny-column-like rectangle.
+    """
+    # fmt: off
+    triangle: NDArrayFloat = np.array(
+        [
+            [1, 1],
+            [1, 3],
+            [3, 1],
+            [1, 1]
+        ]
+    )
+    rectangle: NDArrayFloat = np.array(
+        [
+            [5, 1],
+            [5, 4],
+            [5.5, 4],
+            [5.5, 1],
+            [5, 1]
+        ]
+    )
+    # fmt: on
+    mask = raster_utils.get_mask_from_polygons(polygons=[triangle, rectangle], img_h=7, img_w=7)
+
+    # fmt: off
+    expected_mask: NDArrayByte = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 1, 0],
+            [0, 1, 1, 0, 0, 1, 0],
+            [0, 1, 0, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0],
+        ]
+    ).astype(np.uint8)
+    # fmt: on
+    assert np.array_equal(mask, expected_mask)
+    assert mask.dtype == expected_mask.dtype
+
+
+def test_get_mask_from_polygon_coords_out_of_bounds() -> None:
+    """Test rasterization with polygon coordinates outside of the boundaries."""
+    # fmt: off
+    rectangle: NDArrayFloat = np.array(
+        [
+            [-2, 1],
+            [8, 1],
+            [8, 2],
+            [-5, 2]
+        ]
+    )
+    # fmt: on
+    mask = raster_utils.get_mask_from_polygons(polygons=[rectangle], img_h=5, img_w=5)
+
+    # fmt: off
+    expected_mask: NDArrayByte = np.array(
+        [
+            [0, 0, 0, 0, 0],
+            [1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0]
+        ]
+    ).astype(np.uint8)
+    # fmt: on
+    assert np.array_equal(mask, expected_mask)
+    assert mask.dtype == expected_mask.dtype
+
+
+def test_benchmark_blend_images_cv2(benchmark: Callable[..., Any]) -> None:
+    """Benchmark opencv implementation of alpha blending."""
+
+    def blend_images(img0: NDArrayByte, img1: NDArrayByte, alpha: float = 0.7) -> NDArrayByte:
+        """Alpha-blend two images together using OpenCV `addWeighted`.
+
+        Args:
+            img0: uint8 array of shape (H,W,3).
+            img1: uint8 array of shape (H,W,3).
+            alpha: alpha blending coefficient.
+
+        Returns:
+            uint8 array of shape (H,W,3)
+        """
+        blended: NDArrayByte = cv2.addWeighted(img0, alpha, img1, (1 - alpha), gamma=0)
+        return blended
+
+    size = (2048, 1550, 3)
+    img0: NDArrayByte = np.random.randint(0, 255, size=size).astype(np.uint8)
+    img1: NDArrayByte = np.random.randint(0, 255, size=size).astype(np.uint8)
+    benchmark(blend_images, img0, img1)
+
+
+def test_benchmark_blend_images_npy(benchmark: Callable[..., Any]) -> None:
+    """Benchmark numpy implementation of alpha blending."""
+
+    def blend_images(img0: NDArrayByte, img1: NDArrayByte, alpha: float = 0.7) -> NDArrayByte:
+        """Alpha-blend two images together using `numpy`.
+
+        Args:
+            img0: uint8 array of shape (H,W,3)
+            img1: uint8 array of shape (H,W,3)
+            alpha: Alpha blending coefficient.
+
+        Returns:
+            uint8 array of shape (H,W,3)
+        """
+        blended: NDArrayFloat = np.multiply(img0.astype(np.float32), alpha, dtype=float) + np.multiply(
+            img1.astype(np.float32),
+            (1 - alpha),
+            dtype=float,
+        )
+        blended_uint8: NDArrayByte = blended.astype(np.uint8)
+        return blended_uint8
+
+    size = (2048, 1550, 3)
+    img0: NDArrayByte = np.random.randint(0, 255, size=size).astype(np.uint8)
+    img1: NDArrayByte = np.random.randint(0, 255, size=size).astype(np.uint8)
+    benchmark(blend_images, img0, img1)
+
+
+def test_blend_images() -> None:
+    """Test alpha blending two images together."""
+    alpha = 0.7
+    beta = 1 - alpha
+    gamma = 0.0
+
+    H, W = 3, 3
+    img_a: NDArrayByte = np.zeros((H, W, 3), dtype=np.uint8)
+    img_a[:, :, 0] = np.array([2, 4, 8])  # column 0 has uniform intensity
+    img_a[:, :, 1] = np.array([4, 8, 16])
+    img_a[:, :, 2] = np.array([8, 16, 32])
+
+    img_b: NDArrayByte = np.zeros((H, W, 3), dtype=np.uint8)
+    img_b[:, :, :3] = np.array([2, 4, 8])  # column 0 has uniform intensity
+
+    blended_img_expected: NDArrayByte = np.round(img_a * alpha + img_b * beta + gamma).astype(np.uint8)  # type: ignore
+    blended_img = raster_utils.blend_images(img_a, img_b, alpha=alpha)
+    assert np.array_equal(blended_img, blended_img_expected)
diff --git a/tests/utils/test_se3.py b/tests/utils/test_se3.py
new file mode 100644
index 00000000..f442209e
--- /dev/null
+++ b/tests/utils/test_se3.py
@@ -0,0 +1,210 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""SE(3) Lie group unit tests."""
+
+from typing import Any, Callable, Union
+
+import numpy as np
+
+import av2.geometry.geometry as geometry_utils
+from av2.geometry.se3 import SE3
+from av2.utils.typing import NDArrayFloat
+
+
+def get_yaw_angle_rotmat(theta: Union[float, int]) -> NDArrayFloat:
+    """Build simple test cases that rotate points by yaw angle. Points are rotated about the z-axis in the xy-plane."""
+    c = np.cos(theta)
+    s = np.sin(theta)
+    R: NDArrayFloat = np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])
+    return R
+
+
+def test_SE3_constructor() -> None:
+    """Test making an arbitrary SE2 transformation for a pedestrian cuboid."""
+    # x, y, z of cuboid center
+    t: NDArrayFloat = np.array([-34.7128603513203, 5.29461762417753, 0.10328996181488])
+
+    # quaternion has order (w,x,y,z)
+    q: NDArrayFloat = np.array([0.700322174885275, 0.0, 0.0, -0.713826905743933])
+    R = geometry_utils.quat_to_mat(q)
+
+    dst_se3_src = SE3(rotation=R.copy(), translation=t.copy())
+
+    T_mat_gt = np.eye(4)
+    T_mat_gt[:3, :3] = R
+    T_mat_gt[:3, 3] = t
+
+    assert np.allclose(dst_se3_src.rotation, R)
+    assert np.allclose(dst_se3_src.translation, t)
+    assert np.allclose(dst_se3_src.transform_matrix, T_mat_gt)
+
+
+def test_SE3_transform_point_cloud_identity() -> None:
+    """Test taking a point cloud and performing an SE3 transformation.
+
+    Since the transformation is the identity, the points should not be affected.
+    """
+    pts: NDArrayFloat = np.array([[1.0, 1.0, 1.1], [1.0, 1.0, 2.1], [1.0, 1.0, 3.1]])
+    dst_se3_src = SE3(rotation=np.eye(3), translation=np.zeros(3))
+    transformed_pts = dst_se3_src.transform_point_cloud(pts.copy())
+
+    assert np.allclose(transformed_pts, pts)
+
+
+def test_SE3_transform_point_cloud_by_quaternion() -> None:
+    """Test rotating points by a given quaternion, and then adding translation vector to each point."""
+    pts: NDArrayFloat = np.array([[1.0, 1.0, 1.1], [1.0, 1.0, 2.1], [1.0, 1.0, 3.1], [1.0, 1.0, 4.1]])
+
+    # x, y, z of cuboid center
+    t: NDArrayFloat = np.array([-34.7128603513203, 5.29461762417753, 0.10328996181488])
+
+    # quaternion has order (w,x,y,z)
+    q: NDArrayFloat = np.array([0.700322174885275, 0.0, 0.0, -0.713826905743933])
+    R = geometry_utils.quat_to_mat(q)
+
+    dst_se3_src = SE3(rotation=R.copy(), translation=t.copy())
+    transformed_pts = dst_se3_src.transform_point_cloud(pts.copy())
+
+    gt_transformed_pts = pts.dot(R.T)
+    gt_transformed_pts += t
+    print(gt_transformed_pts)
+
+    assert np.allclose(transformed_pts, gt_transformed_pts)
+    gt_transformed_pts_explicit: NDArrayFloat = np.array(
+        [
+            [-33.73214043, 4.2757023, 1.20328996],
+            [-33.73214043, 4.2757023, 2.20328996],
+            [-33.73214043, 4.2757023, 3.20328996],
+            [-33.73214043, 4.2757023, 4.20328996],
+        ]
+    )
+    assert np.allclose(transformed_pts, gt_transformed_pts_explicit)
+
+
+def test_SE3_transform_point_cloud_by_yaw_angle() -> None:
+    """Test rotating points by yaw angle of pi/4 in the xy plane, then adding a translation vector to them all."""
+    pts: NDArrayFloat = np.array([[1.0, 0.0, 4.0], [1.0, 0.0, 3.0]])
+    theta = np.pi / 4
+    R = get_yaw_angle_rotmat(theta)
+    t: NDArrayFloat = np.array([1.0, 2.0, 3.0])
+
+    dst_SE3_src = SE3(rotation=R.copy(), translation=t.copy())
+
+    transformed_pts = dst_SE3_src.transform_point_cloud(pts.copy())
+    gt_transformed_pts: NDArrayFloat = np.array(
+        [[np.sqrt(2) / 2, np.sqrt(2) / 2, 4.0], [np.sqrt(2) / 2, np.sqrt(2) / 2, 3.0]]
+    )
+    gt_transformed_pts += t
+    assert np.allclose(transformed_pts, gt_transformed_pts)
+
+
+def test_SE3_inverse_transform_point_cloud_identity() -> None:
+    """Test taking a transformed point cloud and undoing the transformation.
+
+    Since the transformation was the identity, the points should not be affected.
+    """
+    transformed_pts: NDArrayFloat = np.array([[1.0, 1.0, 1.1], [1.0, 1.0, 2.1], [1.0, 1.0, 3.1], [1.0, 1.0, 4.1]])
+    dst_se3_src = SE3(rotation=np.eye(3), translation=np.zeros(3))
+    pts = dst_se3_src.inverse().transform_point_cloud(transformed_pts.copy())
+    assert np.allclose(pts, transformed_pts)
+
+
+def test_SE3_inverse_transform_point_cloud() -> None:
+    """Test taking a transformed point cloud and undoing the transformation to recover the original points."""
+    transformed_pts: NDArrayFloat = np.array(
+        [
+            [-33.73214043, 4.2757023, 1.20328996],
+            [-33.73214043, 4.2757023, 2.20328996],
+            [-33.73214043, 4.2757023, 3.20328996],
+            [-33.73214043, 4.2757023, 4.20328996],
+        ]
+    )
+    # x, y, z of cuboid center
+    t: NDArrayFloat = np.array([-34.7128603513203, 5.29461762417753, 0.10328996181488])
+
+    # quaternion has order (w,x,y,z)
+    q: NDArrayFloat = np.array([0.700322174885275, 0.0, 0.0, -0.713826905743933])
+    R = geometry_utils.quat_to_mat(q)
+
+    dst_se3_src = SE3(rotation=R, translation=t)
+    pts = dst_se3_src.inverse().transform_point_cloud(transformed_pts)
+    gt_pts: NDArrayFloat = np.array([[1.0, 1.0, 1.1], [1.0, 1.0, 2.1], [1.0, 1.0, 3.1], [1.0, 1.0, 4.1]])
+
+    assert np.allclose(pts, gt_pts)
+
+
+def test_SE3_chaining_transforms() -> None:
+    """Test chaining two transformations to restore 3 points back to their original position."""
+    theta0 = np.pi / 4
+    R0 = get_yaw_angle_rotmat(theta0)
+
+    theta1 = np.pi
+    R1 = get_yaw_angle_rotmat(theta1)
+
+    t0 = np.zeros(3)
+    t1 = np.zeros(3)
+
+    fr2_se3_fr1 = SE3(rotation=R0, translation=t0)
+    fr1_se3_fr0 = SE3(rotation=R1, translation=t1)
+
+    fr2_se3_fr0 = fr2_se3_fr1.compose(fr1_se3_fr0)
+
+    pts: NDArrayFloat = np.array([[1.0, 0.0, 4.0], [1.0, 0.0, 3.0]])
+    transformed_pts = fr2_se3_fr0.transform_point_cloud(pts.copy())
+    gt_transformed_pts: NDArrayFloat = np.array(
+        [
+            [-np.sqrt(2) / 2, -np.sqrt(2) / 2, 4.0],
+            [-np.sqrt(2) / 2, -np.sqrt(2) / 2, 3.0],
+        ]
+    )
+
+    assert np.allclose(transformed_pts, gt_transformed_pts)
+
+    combined_R = get_yaw_angle_rotmat(theta0 + theta1)
+    assert np.allclose(fr2_se3_fr0.rotation, combined_R)
+    assert np.allclose(fr2_se3_fr0.translation, np.zeros(3))
+
+
+def test_benchmark_SE3_transform_point_cloud_optimized(benchmark: Callable[..., Any]) -> None:
+    """Ensure that our transform_point_cloud() implementation is faster than a naive implementation."""
+    num_pts = 100000
+    points_src = np.random.randn(num_pts, 3)
+
+    # x, y, z of cuboid center
+    t: NDArrayFloat = np.array([-34.7128603513203, 5.29461762417753, 0.10328996181488])
+
+    # quaternion has order (w,x,y,z)
+    q: NDArrayFloat = np.array([0.700322174885275, 0.0, 0.0, -0.713826905743933])
+    R = geometry_utils.quat_to_mat(q)
+
+    dst_SE3_src = SE3(rotation=R.copy(), translation=t.copy())
+
+    benchmark(dst_SE3_src.transform_point_cloud, points_src)
+
+
+def test_benchmark_SE3_transform_point_cloud_unoptimized(benchmark: Callable[..., Any]) -> None:
+    """Benchmark unoptimized SE(3) transformation."""
+
+    def benchmark_SE3_transform_point_cloud_unoptimized(
+        point_cloud: NDArrayFloat, transform_matrix: NDArrayFloat
+    ) -> NDArrayFloat:
+        """Slow, unoptimized method -- allocated unnecessary array for homogeneous coordinates."""
+        # convert to homogeneous
+        num_pts = len(point_cloud)
+        homogeneous_pts: NDArrayFloat = np.hstack([point_cloud, np.ones((num_pts, 1))])
+        transformed_point_cloud: NDArrayFloat = homogeneous_pts.dot(transform_matrix.T)[:, :3]
+        return transformed_point_cloud
+
+    num_pts = 100000
+    points_src = np.random.randn(num_pts, 3)
+
+    # x, y, z of cuboid center
+    t: NDArrayFloat = np.array([-34.7128603513203, 5.29461762417753, 0.10328996181488])
+
+    # quaternion has order (w,x,y,z)
+    q: NDArrayFloat = np.array([0.700322174885275, 0.0, 0.0, -0.713826905743933])
+    R = geometry_utils.quat_to_mat(q)
+
+    dst_SE3_src = SE3(rotation=R.copy(), translation=t.copy())
+
+    benchmark(benchmark_SE3_transform_point_cloud_unoptimized, points_src, dst_SE3_src.transform_matrix)
diff --git a/tests/utils/test_sim2.py b/tests/utils/test_sim2.py
new file mode 100644
index 00000000..47730e23
--- /dev/null
+++ b/tests/utils/test_sim2.py
@@ -0,0 +1,314 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Unit tests for Sim(2) related utilities."""
+
+import math
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+import pytest
+
+import av2.utils.io as io_utils
+from av2.geometry.sim2 import Sim2
+from av2.utils.typing import NDArrayFloat
+
+_TEST_DATA_ROOT = Path(__file__).resolve().parent.parent / "test_data"
+
+
+def test_constructor() -> None:
+    """Sim(2) to perform p_b = bSa * p_a."""
+    bRa = np.eye(2)
+    bta: NDArrayFloat = np.array([1.0, 2.0])
+    bsa = 3.0
+    bSa = Sim2(R=bRa, t=bta, s=bsa)
+    assert isinstance(bSa, Sim2)
+    assert np.allclose(bSa.R, bRa)
+    assert np.allclose(bSa.t, bta)
+    assert np.allclose(bSa.s, bsa)
+
+
+def test_is_eq() -> None:
+    """Ensure object equality works properly (are equal)."""
+    bSa = Sim2(R=np.eye(2), t=np.array([1, 2]), s=3.0)
+    bSa_ = Sim2(R=np.eye(2), t=np.array([1.0, 2.0]), s=3)
+    assert bSa == bSa_
+
+
+def test_not_eq_translation() -> None:
+    """Ensure object equality works properly (not equal translation)."""
+    bSa = Sim2(R=np.eye(2), t=np.array([2, 1]), s=3.0)
+    bSa_ = Sim2(R=np.eye(2), t=np.array([1.0, 2.0]), s=3)
+    assert bSa != bSa_
+
+
+def test_not_eq_rotation() -> None:
+    """Ensure object equality works properly (not equal rotation)."""
+    bSa = Sim2(R=np.eye(2), t=np.array([2, 1]), s=3.0)
+    bSa_ = Sim2(R=-1 * np.eye(2), t=np.array([2.0, 1.0]), s=3)
+    assert bSa != bSa_
+
+
+def test_not_eq_scale() -> None:
+    """Ensure object equality works properly (not equal scale)."""
+    bSa = Sim2(R=np.eye(2), t=np.array([2, 1]), s=3.0)
+    bSa_ = Sim2(R=np.eye(2), t=np.array([2.0, 1.0]), s=1.0)
+    assert bSa != bSa_
+
+
+def test_rotation() -> None:
+    """Ensure rotation component is returned properly."""
+    R: NDArrayFloat = np.array([[0, -1], [1, 0]])
+    t: NDArrayFloat = np.array([1, 2])
+    bSa = Sim2(R=R, t=t, s=3.0)
+
+    expected_R: NDArrayFloat = np.array([[0, -1], [1, 0]])
+    assert np.allclose(expected_R, bSa.rotation)
+
+
+def test_translation() -> None:
+    """Ensure translation component is returned properly."""
+    R: NDArrayFloat = np.array([[0, -1], [1, 0]])
+    t: NDArrayFloat = np.array([1, 2])
+    bSa = Sim2(R=R, t=t, s=3.0)
+
+    expected_t: NDArrayFloat = np.array([1, 2])
+    assert np.allclose(expected_t, bSa.translation)
+
+
+def test_scale() -> None:
+    """Ensure the scale factor is returned properly."""
+    bRa = np.eye(2)
+    bta: NDArrayFloat = np.array([1, 2])
+    bsa = 3.0
+    bSa = Sim2(R=bRa, t=bta, s=bsa)
+    assert bSa.scale == 3.0
+
+
+def test_compose_1() -> None:
+    """Ensure we can compose two Sim(2) transforms together, with identity rotations."""
+    scale = 2.0
+    imgSw = Sim2(R=np.eye(2), t=np.array([1.0, 3.0]), s=scale)
+
+    scale = 0.5
+    wSimg = Sim2(R=np.eye(2), t=np.array([-2.0, -6.0]), s=scale)
+
+    # identity
+    wSw = Sim2(R=np.eye(2), t=np.zeros((2,)), s=1.0)
+    assert wSw == imgSw.compose(wSimg)
+
+
+def test_compose_2() -> None:
+    """Verify composition of Sim2 objects works for non-identity input."""
+    aSb = Sim2(R=rotmat2d(np.deg2rad(90)), t=np.array([1, 2]), s=4)
+
+    bSc = Sim2(R=rotmat2d(np.deg2rad(-45)), t=np.array([3, 4]), s=0.5)
+
+    aSc = aSb.compose(bSc)
+    # Via composition: 90 + -45 = 45 degrees
+    assert math.isclose(aSc.theta_deg, 45.0)
+    # Via composition: 4 * 0.5 = 2.0
+    assert aSc.scale == 2.0
+
+
+def test_compose_3() -> None:
+    """Verify correctness of composed inverted and non-inverted transformations."""
+    # Note: these are dummy translation values; should be ignored when considering correctness
+    aSb = Sim2(R=rotmat2d(np.deg2rad(20)), t=np.array([1, 2]), s=2)
+
+    bSc = Sim2(R=rotmat2d(np.deg2rad(30)), t=np.array([1, 2]), s=3)
+
+    aSc = Sim2(R=rotmat2d(np.deg2rad(50)), t=np.array([1, 2]), s=6)
+
+    # ground truth is an identity transformation
+    aSa_gt = Sim2(R=np.eye(2), t=np.zeros(2), s=1.0)
+    aSa_est = aSb.compose(bSc).compose(aSc.inverse())
+
+    assert np.isclose(aSa_gt.theta_deg, aSa_est.theta_deg, atol=1e-5)
+
+
+def test_inverse() -> None:
+    """Ensure that the .inverse() method returns the correct result."""
+    scale = 2.0
+    imgSw = Sim2(R=np.eye(2), t=np.array([1.0, 3.0]), s=scale)
+
+    scale = 0.5
+    wSimg = Sim2(R=np.eye(2), t=np.array([-2.0, -6.0]), s=scale)
+
+    assert imgSw == wSimg.inverse()
+    assert wSimg == imgSw.inverse()
+
+
+def test_matrix() -> None:
+    """Ensure 3x3 matrix is formed correctly."""
+    bRa: NDArrayFloat = np.array([[0, -1], [1, 0]])
+    bta: NDArrayFloat = np.array([1, 2])
+    bsa = 3.0
+    bSa = Sim2(R=bRa, t=bta, s=bsa)
+
+    bSa_expected: NDArrayFloat = np.array([[0, -1, 1], [1, 0, 2], [0, 0, 1 / 3]])
+    assert np.allclose(bSa_expected, bSa.matrix)
+
+
+def test_from_matrix() -> None:
+    """Ensure that classmethod can construct an object instance from a 3x3 numpy matrix."""
+    bRa: NDArrayFloat = np.array([[0, -1], [1, 0]])
+    bta: NDArrayFloat = np.array([1, 2])
+    bsa = 3.0
+    bSa = Sim2(R=bRa, t=bta, s=bsa)
+
+    bSa_ = Sim2.from_matrix(bSa.matrix)
+
+    # ensure we can reconstruct new instance from matrix
+    assert bSa == bSa_
+
+    # ensure generated class object has correct attributes
+    assert np.allclose(bSa_.rotation, bRa)
+    assert np.allclose(bSa_.translation, bta)
+    assert np.isclose(bSa_.scale, bsa)
+
+    # ensure generated class object has correct 3x3 matrix attribute
+    bSa_expected: NDArrayFloat = np.array([[0, -1, 1], [1, 0, 2], [0, 0, 1 / 3]])
+    assert np.allclose(bSa_expected, bSa_.matrix)
+
+
+def test_matrix_homogenous_transform() -> None:
+    """Ensure 3x3 matrix transforms homogenous points as expected."""
+    expected_img_pts: NDArrayFloat = np.array([[6, 4], [4, 6], [0, 0], [1, 7]])
+
+    world_pts: NDArrayFloat = np.array([[2, -1], [1, 0], [-1, -3], [-0.5, 0.5]])
+    scale = 2.0
+    imgSw = Sim2(R=np.eye(2), t=np.array([1.0, 3.0]), s=scale)
+
+    # convert to homogeneous
+    world_pts_h: NDArrayFloat = np.hstack([world_pts, np.ones((4, 1))])
+
+    # multiply each (3,1) homogeneous point vector w/ transform matrix
+    img_pts_h = (imgSw.matrix @ world_pts_h.T).T  # type: ignore
+    # divide (x,y,s) by s
+    img_pts = img_pts_h[:, :2] / img_pts_h[:, 2].reshape(-1, 1)
+    assert np.allclose(expected_img_pts, img_pts)
+
+
+def test_transform_from_forwards() -> None:
+    """Test Similarity(2) forward transform."""
+    expected_img_pts: NDArrayFloat = np.array([[6, 4], [4, 6], [0, 0], [1, 7]])
+
+    world_pts: NDArrayFloat = np.array([[2, -1], [1, 0], [-1, -3], [-0.5, 0.5]])
+    scale = 2.0
+    imgSw = Sim2(R=np.eye(2), t=np.array([1.0, 3.0]), s=scale)
+
+    img_pts = imgSw.transform_from(world_pts)
+    assert np.allclose(expected_img_pts, img_pts)
+
+
+def test_transform_from_backwards() -> None:
+    """Test Similarity(2) backward transform."""
+    img_pts: NDArrayFloat = np.array([[6, 4], [4, 6], [0, 0], [1, 7]])
+
+    expected_world_pts: NDArrayFloat = np.array([[2, -1], [1, 0], [-1, -3], [-0.5, 0.5]])
+    scale = 0.5
+    wSimg = Sim2(R=np.eye(2), t=np.array([-2.0, -6.0]), s=scale)
+
+    world_pts = wSimg.transform_from(img_pts)
+    assert np.allclose(expected_world_pts, world_pts)
+
+
+def rotmat2d(theta: float) -> NDArrayFloat:
+    """Convert angle `theta` (in radians) to a 2x2 rotation matrix."""
+    s = np.sin(theta)
+    c = np.cos(theta)
+    R: NDArrayFloat = np.array([[c, -s], [s, c]])
+    return R
+
+
+def test_cannot_set_zero_scale() -> None:
+    """Ensure that an exception is thrown if Sim(2) scale is set to zero."""
+    R: NDArrayFloat = np.eye(2, dtype=float)
+    t: NDArrayFloat = np.arange(2, dtype=float)
+    s = 0.0
+
+    with pytest.raises(ZeroDivisionError) as e_info:
+        Sim2(R, t, s)
+
+
+def test_sim2_theta_deg_1() -> None:
+    """Ensure we can recover the rotation angle theta, when theta=0 degrees."""
+    R: NDArrayFloat = np.eye(2, dtype=float)
+    t: NDArrayFloat = np.arange(2, dtype=float)
+    s = 10.5
+    aSb = Sim2(R, t, s)
+    assert aSb.theta_deg == 0
+
+
+def test_sim2_theta_deg_2() -> None:
+    """Ensure we can recover the rotation angle theta, when theta=135 degrees."""
+    R: NDArrayFloat = rotmat2d(np.deg2rad(135))
+    t: NDArrayFloat = np.arange(2, dtype=float)
+    s = 10.5
+    aSb = Sim2(R, t, s)
+    assert aSb.theta_deg == 135
+
+
+def test_sim2_repr() -> None:
+    """Ensure we can print the class, and obtain the correct string representation from __repr__."""
+    R: NDArrayFloat = np.eye(2, dtype=float)
+    t: NDArrayFloat = np.arange(2, dtype=float)
+    s = 10.5
+    aSb = Sim2(R, t, s)
+    print(aSb)
+    assert repr(aSb) == "Angle (deg.): 0.0, Trans.: [0. 1.], Scale: 10.5"
+
+
+def test_transform_from_wrong_dims() -> None:
+    """Ensure that 1d input is not allowed (row vectors are required, as Nx2)."""
+    bRa = np.eye(2)
+    bta: NDArrayFloat = np.array([1, 2])
+    bsa = 3.0
+    bSa = Sim2(R=bRa, t=bta, s=bsa)
+
+    with pytest.raises(ValueError):
+        bSa.transform_from(np.array([1.0, 3.0]))
+
+
+def test_from_json() -> None:
+    """Ensure that classmethod can construct an object instance from a json file."""
+    json_fpath = _TEST_DATA_ROOT / "a_Sim2_b.json"
+    aSb = Sim2.from_json(json_fpath)
+
+    expected_rotation: NDArrayFloat = np.array([[1.0, 0.0], [0.0, 1.0]])
+    expected_translation: NDArrayFloat = np.array([3930.0, 3240.0])
+    expected_scale = 1.6666666666666667
+    assert np.allclose(aSb.rotation, expected_rotation)
+    assert np.allclose(aSb.translation, expected_translation)
+    assert np.isclose(aSb.scale, expected_scale)
+
+
+def test_from_json_invalid_scale() -> None:
+    """Ensure that classmethod raises an error with invalid JSON input."""
+    json_fpath = _TEST_DATA_ROOT / "a_Sim2_b___invalid.json"
+
+    with pytest.raises(ZeroDivisionError) as e_info:
+        Sim2.from_json(json_fpath)
+
+
+def test_save_as_json() -> None:
+    """Ensure that JSON serialization of a class instance works correctly."""
+    bSc = Sim2(R=np.array([[0, 1], [1, 0]]), t=np.array([-5, 5]), s=0.1)
+    save_fpath = Path(NamedTemporaryFile().name)
+    bSc.save_as_json(save_fpath=save_fpath)
+
+    bSc_dict = io_utils.read_json_file(save_fpath)
+    assert bSc_dict["R"] == [0, 1, 1, 0]
+    assert bSc_dict["t"] == [-5, 5]
+    assert bSc_dict["s"] == 0.1
+
+
+def test_round_trip() -> None:
+    """Test round trip of serialization, then de-serialization."""
+    bSc = Sim2(R=np.array([[0, 1], [1, 0]]), t=np.array([-5, 5]), s=0.1)
+    save_fpath = Path(NamedTemporaryFile().name)
+    bSc.save_as_json(save_fpath=save_fpath)
+
+    bSc_ = Sim2.from_json(save_fpath)
+    assert bSc_ == bSc
diff --git a/tutorials/generate_egoview_overlaid_lidar.py b/tutorials/generate_egoview_overlaid_lidar.py
new file mode 100644
index 00000000..8e91a8aa
--- /dev/null
+++ b/tutorials/generate_egoview_overlaid_lidar.py
@@ -0,0 +1,198 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Lidar egoview visualization."""
+
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Final
+
+import click
+import cv2
+import numpy as np
+
+import av2.rendering.color as color_utils
+import av2.rendering.rasterize as raster_rendering_utils
+import av2.rendering.video as video_utils
+import av2.utils.io as io_utils
+import av2.utils.raster as raster_utils
+from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
+from av2.datasets.sensor.constants import RingCameras
+from av2.map.map_api import ArgoverseStaticMap
+from av2.rendering.color import GREEN_HEX, RED_HEX
+from av2.utils.typing import NDArrayByte, NDArrayFloat, NDArrayInt
+
+logger = logging.getLogger(__name__)
+
+
+NUM_RANGE_BINS: Final[int] = 50
+RING_CAMERA_FPS: Final[int] = 20
+
+
+def generate_egoview_overlaid_lidar(
+    data_root: Path, output_dir: Path, log_id: str, render_ground_pts_only: bool, dump_single_frames: bool
+) -> None:
+    """Render LiDAR points from a particular camera's viewpoint (color by ground surface, and apply ROI filtering).
+
+    Args:
+        data_root: path to directory where the logs live on disk.
+        output_dir: path to directory where renderings will be saved.
+        log_id: unique ID for AV2 scenario/log.
+        render_ground_pts_only: whether to only render LiDAR points located close to the ground surface.
+        dump_single_frames: Whether to save to disk individual RGB frames of the rendering, in addition to generating
+            the mp4 file.
+
+    Raises:
+        RuntimeError: If vehicle log data is not present at `data_root` for `log_id`.
+    """
+    loader = AV2SensorDataLoader(data_dir=data_root, labels_dir=data_root)
+
+    log_map_dirpath = data_root / log_id / "map"
+    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)
+
+    # repeat red to green colormap every 50 m.
+    colors_arr_rgb = color_utils.create_colormap(color_list=[RED_HEX, GREEN_HEX], n_colors=NUM_RANGE_BINS)
+    colors_arr_rgb = (colors_arr_rgb * 255).astype(np.uint8)  # type: ignore
+    colors_arr_bgr: NDArrayByte = np.fliplr(colors_arr_rgb)  # type: ignore
+
+    for _, cam_name in enumerate(list(RingCameras)):
+        cam_im_fpaths = loader.get_ordered_log_cam_fpaths(log_id, cam_name)
+        num_cam_imgs = len(cam_im_fpaths)
+
+        video_list = []
+        for i, im_fpath in enumerate(cam_im_fpaths):
+            if i % 50 == 0:
+                logging.info(f"\tOn file {i}/{num_cam_imgs} of camera {cam_name} of {log_id}")
+
+            cam_timestamp_ns = int(im_fpath.stem)
+            city_SE3_ego = loader.get_city_SE3_ego(log_id, cam_timestamp_ns)
+            if city_SE3_ego is None:
+                logger.exception("missing LiDAR pose")
+                continue
+
+            # load feather file path, e.g. '315978406032859416.feather"
+            lidar_fpath = loader.get_closest_lidar_fpath(log_id, cam_timestamp_ns)
+            if lidar_fpath is None:
+                logger.info("No LiDAR sweep found within the synchronization interval for %s, so skipping...", cam_name)
+                continue
+
+            img_bgr = io_utils.read_img(im_fpath, channel_order="BGR")
+
+            lidar_points_ego = io_utils.read_lidar_sweep(lidar_fpath, attrib_spec="xyz")
+            lidar_timestamp_ns = int(lidar_fpath.stem)
+
+            # put into city coords, then prune away ground and non-RoI points
+            lidar_points_city = city_SE3_ego.transform_point_cloud(lidar_points_ego)
+            lidar_points_city = avm.remove_non_drivable_area_points(lidar_points_city)
+            is_ground_logicals = avm.get_ground_points_boolean(lidar_points_city)
+            lidar_points_city = lidar_points_city[is_ground_logicals if render_ground_pts_only else ~is_ground_logicals]
+            lidar_points_ego = city_SE3_ego.inverse().transform_point_cloud(lidar_points_city)
+
+            # motion compensate always
+            uv, points_cam, is_valid_points = loader.project_ego_to_img_motion_compensated(
+                points_lidar_time=lidar_points_ego,
+                cam_name=cam_name,
+                cam_timestamp_ns=cam_timestamp_ns,
+                lidar_timestamp_ns=lidar_timestamp_ns,
+                log_id=log_id,
+            )
+
+            if is_valid_points is None or uv is None or points_cam is None:
+                continue
+
+            if is_valid_points.sum() == 0:
+                continue
+
+            uv_int: NDArrayInt = np.round(uv[is_valid_points]).astype(np.int32)  # type: ignore
+            points_cam = points_cam[is_valid_points]
+            pt_ranges: NDArrayFloat = np.linalg.norm(points_cam[:, :3], axis=1)  # type: ignore
+            color_bins: NDArrayInt = np.round(pt_ranges).astype(np.int32)  # type: ignore
+            # account for moving past 100 meters, loop around again
+            color_bins = color_bins % NUM_RANGE_BINS
+            uv_colors_bgr = colors_arr_bgr[color_bins]
+
+            img_empty = np.full_like(img_bgr, fill_value=255)
+            img_empty = raster_rendering_utils.draw_points_xy_in_img(img_empty, uv_int, uv_colors_bgr, diameter=10)
+            blended_bgr = raster_utils.blend_images(img_bgr, img_empty)
+            frame_rgb = blended_bgr[:, :, ::-1]
+
+            if dump_single_frames:
+                save_dir = output_dir / log_id / cam_name
+                os.makedirs(save_dir, exist_ok=True)
+                cv2.imwrite(str(save_dir / f"{cam_name}_{lidar_timestamp_ns}.jpg"), blended_bgr)
+
+            video_list.append(frame_rgb)
+
+        if len(video_list) == 0:
+            raise RuntimeError("No video frames were found; log data was not found on disk.")
+
+        video: NDArrayByte = np.stack(video_list).astype(np.uint8)
+        video_output_dir = output_dir / "videos"
+        video_utils.write_video(video=video, dst=video_output_dir / f"{log_id}_{cam_name}.mp4", fps=RING_CAMERA_FPS)
+
+
+@click.command(help="Generate LiDAR + map visualizations from the Argoverse 2 Sensor Dataset.")
+@click.option(
+    "-d",
+    "--data-root",
+    required=True,
+    help="Path to local directory where the Argoverse 2 Sensor Dataset logs are stored.",
+    type=click.Path(exists=True),
+)
+@click.option(
+    "-o",
+    "--output-dir",
+    required=True,
+    help="Path to local directory where renderings will be saved.",
+    type=str,
+)
+@click.option(
+    "-l",
+    "--log-id",
+    default="00a6ffc1-6ce9-3bc3-a060-6006e9893a1a",
+    help="unique log identifier.",
+    type=str,
+)
+@click.option(
+    "-g",
+    "--render-ground-pts-only",
+    default=True,
+    help="Boolean argument whether to only render LiDAR points located close to the ground surface.",
+    type=bool,
+)
+@click.option(
+    "-s",
+    "--dump-single-frames",
+    default=False,
+    help="Whether to save to disk individual RGB frames of the rendering, in addition to generating the mp4 file"
+    "(defaults to False). Note: can quickly generate 100s of MBs, for 200 KB frames.",
+    type=bool,
+)
+def run_generate_egoview_overlaid_lidar(
+    data_root: str, output_dir: str, log_id: str, render_ground_pts_only: bool, dump_single_frames: bool
+) -> None:
+    """Click entry point for visualizing LiDAR returns rendered on top of sensor imagery."""
+    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+    data_root_path = Path(data_root)
+    output_dir_path = Path(output_dir)
+
+    logger.info(
+        "data_root: %s, output_dir: %s, log_id: %s, render_ground_pts_only: %s, dump_single_frames: %s",
+        data_root_path,
+        output_dir_path,
+        log_id,
+        render_ground_pts_only,
+        dump_single_frames,
+    )
+    generate_egoview_overlaid_lidar(
+        data_root=data_root_path,
+        output_dir=output_dir_path,
+        log_id=log_id,
+        render_ground_pts_only=render_ground_pts_only,
+        dump_single_frames=dump_single_frames,
+    )
+
+
+if __name__ == "__main__":
+    run_generate_egoview_overlaid_lidar()
diff --git a/tutorials/generate_egoview_overlaid_vector_map.py b/tutorials/generate_egoview_overlaid_vector_map.py
new file mode 100644
index 00000000..057ed7db
--- /dev/null
+++ b/tutorials/generate_egoview_overlaid_vector_map.py
@@ -0,0 +1,343 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Generate MP4 videos with map entities rendered on top of sensor imagery, for all cameras, for a single log.
+
+We use a inferred depth map from LiDAR to render only visible map entities (lanes and pedestrian crossings).
+"""
+
+import logging
+import os
+import sys
+import time
+from enum import Enum
+from pathlib import Path
+from typing import Final, List, Tuple
+
+import click
+import numpy as np
+
+import av2.geometry.interpolate as interp_utils
+import av2.rendering.video as video_utils
+import av2.utils.io as io_utils
+import av2.utils.raster as raster_utils
+from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
+from av2.datasets.sensor.constants import RingCameras
+from av2.map.map_api import ArgoverseStaticMap
+from av2.rendering.color import BLUE_BGR
+from av2.rendering.map import EgoViewMapRenderer
+from av2.utils.typing import NDArrayByte
+
+RING_CAMERA_FPS: Final[int] = 20
+
+logger = logging.getLogger(__name__)
+
+
+def generate_egoview_overlaid_map(
+    data_root: Path,
+    output_dir: Path,
+    log_id: str,
+    max_range_m: float,
+    use_depth_map_for_occlusion: bool,
+    dump_single_frames: bool,
+    cam_names: List[RingCameras],
+) -> None:
+    """Render the map from a particular camera's viewpoint for each camera frame.
+
+    Args:
+        data_root: path to where the AV2 logs live.
+        output_dir: path to directory where renderings will be saved.
+        log_id: unique ID for AV2 scenario/log.
+        max_range_m: maximum range of map entities from egovehicle to consider for rendering (by l-infinity norm).
+        use_depth_map_for_occlusion: whether to use an inferred depth map for rendering occluded elements.
+        dump_single_frames: Whether to save to disk individual RGB frames of the rendering, in addition to generating
+            the mp4 file.
+        cam_names: list of camera names. For each camera, its viewport will be used to render the map.
+    """
+    loader = AV2SensorDataLoader(data_dir=data_root, labels_dir=data_root)
+
+    log_map_dirpath = data_root / log_id / "map"
+    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)
+
+    for _, cam_enum in enumerate(cam_names):
+        cam_name = cam_enum.value
+        pinhole_cam = loader.get_log_pinhole_camera(log_id, cam_name)
+
+        cam_im_fpaths = loader.get_ordered_log_cam_fpaths(log_id, cam_name)
+        num_cam_imgs = len(cam_im_fpaths)
+
+        video_list = []
+        for i, img_fpath in enumerate(cam_im_fpaths):
+            if i % 50 == 0:
+                logging.info(f"\tOn file {i}/{num_cam_imgs} of camera {cam_name} of {log_id}")
+
+            cam_timestamp_ns = int(img_fpath.stem)
+            city_SE3_ego = loader.get_city_SE3_ego(log_id, cam_timestamp_ns)
+            if city_SE3_ego is None:
+                logger.info("missing LiDAR pose")
+                continue
+
+            # load feather file path, e.g. '315978406032859416.feather"
+            lidar_fpath = loader.get_closest_lidar_fpath(log_id, cam_timestamp_ns)
+            if lidar_fpath is None:
+                # without depth map, can't do this accurately
+                continue
+
+            lidar_points = io_utils.read_lidar_sweep(lidar_fpath, attrib_spec="xyz")
+            lidar_timestamp_ns = int(lidar_fpath.stem)
+
+            if use_depth_map_for_occlusion:
+                depth_map = loader.get_depth_map_from_lidar(
+                    lidar_points=lidar_points,
+                    cam_name=cam_name,
+                    log_id=log_id,
+                    cam_timestamp_ns=cam_timestamp_ns,
+                    lidar_timestamp_ns=lidar_timestamp_ns,
+                )
+            else:
+                depth_map = None
+
+            egoview_renderer = EgoViewMapRenderer(
+                depth_map=depth_map, city_SE3_ego=city_SE3_ego, pinhole_cam=pinhole_cam, avm=avm
+            )
+            frame_rgb = render_egoview(
+                output_dir=output_dir,
+                img_fpath=img_fpath,
+                egoview_renderer=egoview_renderer,
+                cam_timestamp_ns=cam_timestamp_ns,
+                log_id=log_id,
+                max_range_m=max_range_m,
+                dump_single_frames=dump_single_frames,
+            )
+            video_list.append(frame_rgb)
+
+        video: NDArrayByte = np.stack(video_list).astype(np.uint8)
+        video_output_dir = output_dir / "videos"
+        video_utils.write_video(
+            video=video,
+            dst=video_output_dir / f"{log_id}_{cam_name}.mp4",
+            fps=RING_CAMERA_FPS,
+            preset="medium",
+        )
+
+
+def render_egoview(
+    output_dir: Path,
+    img_fpath: Path,
+    egoview_renderer: EgoViewMapRenderer,
+    cam_timestamp_ns: int,
+    log_id: str,
+    max_range_m: float,
+    dump_single_frames: bool,
+) -> NDArrayByte:
+    """Synthetically manipulate a vector map, render the map in the ego-view, and save rendering to disk.
+
+    Args:
+        output_dir: path to directory where renderings will be saved.
+        img_fpath: path to RGB image, from one of the ring or stereo cameras.
+        egoview_renderer: rendering engine for map elements in the ego-view.
+        cam_timestamp_ns: nanosecond camera timestamp when image was captured.
+        log_id: unique ID for AV2 scenario/log.
+        max_range_m: maximum range of map entities from egovehicle to consider for rendering (by l-infinity norm).
+        dump_single_frames: Whether to save to disk individual RGB frames of the rendering, in addition to generating
+            the mp4 file.
+
+    Returns:
+        array of shape (H,W,3) and type uint8 representing a RGB image.
+    """
+    save_dir = output_dir / log_id
+    if dump_single_frames:
+        # we only create log-specific directories, if dumping individual frames.
+        save_dir.mkdir(exist_ok=True, parents=True)
+
+    img_fname = f"{egoview_renderer.pinhole_cam.cam_name}_{cam_timestamp_ns}_vectormap.jpg"
+    save_fpath = save_dir / img_fname
+
+    if save_fpath.exists():
+        logger.info("Rendered image already exists, skipping")
+        img: NDArrayByte = io_utils.read_img(save_fpath)
+        return img
+
+    start = time.time()
+
+    img_rgb: NDArrayByte = io_utils.read_img(img_fpath)
+
+    # to prevent washing out, can pass in black image, and get just mask back, or can overlay directly.
+    img_h, img_w, _ = img_rgb.shape
+    img_empty: NDArrayByte = np.full(
+        (img_h, img_w, 3), fill_value=128, dtype=np.uint8
+    )  # pure white polylines will disappear @ 255
+
+    img_empty = render_egoview_with_occlusion_checks(
+        img_canvas=img_empty,
+        egoview_renderer=egoview_renderer,
+        max_range_m=max_range_m,
+    )
+    end = time.time()
+    duration = end - start
+    logger.info(f"Rendering single image took {duration:.2f} sec.")
+
+    frame_rgb = raster_utils.blend_images(img_rgb, img_empty, alpha=0.45)
+
+    if dump_single_frames:
+        io_utils.write_img(save_fpath, frame_rgb, channel_order="RGB")
+    return frame_rgb
+
+
+def render_egoview_with_occlusion_checks(
+    img_canvas: NDArrayByte, egoview_renderer: EgoViewMapRenderer, max_range_m: float, line_width_px: int = 10
+) -> NDArrayByte:
+    """Render pedestrian crossings and lane segments in the ego-view.
+
+    Pedestrian crossings (crosswalks) will be rendered in blue, and lane markings will be colored according to their
+    marking color, or otherwise red, if markings are implicit.
+
+    Args:
+        img_canvas: array of shape (H,W,3) representing BGR canvas to rasterize map elements onto.
+        egoview_renderer: rendering engine for map elements in the ego-view.
+        max_range_m: maximum range of map entities from egovehicle to consider for rendering (by l-infinity norm).
+        line_width_px: thickness (in pixels) to use for rendering each polyline.
+
+    Returns:
+        array of shape (H,W,3) and type uint8 representing a RGB image.
+    """
+    for ls in egoview_renderer.avm.get_scenario_lane_segments():
+        img_canvas = egoview_renderer.render_lane_boundary_egoview(img_canvas, ls, "right", line_width_px)
+        img_canvas = egoview_renderer.render_lane_boundary_egoview(img_canvas, ls, "left", line_width_px)
+
+    for pc in egoview_renderer.avm.get_scenario_ped_crossings():
+
+        EPS = 1e-5
+        crosswalk_color = BLUE_BGR
+        # render ped crossings (pc's)
+        xwalk_polygon = pc.polygon
+        # prevent duplicate first and last coords
+        xwalk_polygon[:-1] += EPS
+        N_INTERP_PTS = 100
+        # For pixel-perfect rendering, querying crosswalk boundary ground height at waypoints throughout
+        # the street is much more accurate than 3d linear interpolation using only the 4 annotated corners.
+        polygon_city_frame = interp_utils.interp_arc(t=N_INTERP_PTS, points=xwalk_polygon[:, :2])
+        polygon_city_frame = egoview_renderer.avm.append_height_to_2d_city_pt_cloud(points_xy=polygon_city_frame)
+        egoview_renderer.render_polyline_egoview(
+            polygon_city_frame,
+            img_canvas,
+            crosswalk_color,
+            thickness_px=line_width_px,
+        )
+
+    # convert BGR to RGB
+    img_rgb: NDArrayByte = img_canvas[:, :, ::-1]
+    return img_rgb
+
+
+def parse_camera_enum_types(cam_names: Tuple[str, ...]) -> List[RingCameras]:
+    """Convert a list of CLI string types, to enums of type RingCameras, and validate each input.
+
+    Args:
+        cam_names: Tuple of camera names to use for rendering the map.
+
+    Returns:
+        List of camera enums to use for rendering the map.
+
+    Raises:
+        ValueError: If an invalid camera name is provided.
+    """
+    valid_ring_cams = set([x.value for x in list(RingCameras)])
+
+    cam_enums: List[RingCameras] = []
+    for cam_name in list(cam_names):
+        if cam_name in valid_ring_cams:
+            cam_enums.append(RingCameras(cam_name))
+        else:
+            raise ValueError("Must provide _valid_ camera names!")
+    return cam_enums
+
+
+@click.command(help="Generate map visualizations on ego-view imagery from the Argoverse 2 Sensor Dataset.")
+@click.option(
+    "-d",
+    "--data-root",
+    required=True,
+    help="Path to local directory where the Argoverse 2 Sensor Dataset logs are stored.",
+    type=click.Path(exists=True),
+)
+@click.option(
+    "-o",
+    "--output-dir",
+    required=True,
+    help="Path to local directory where renderings will be saved.",
+    type=str,
+)
+@click.option(
+    "-l",
+    "--log-id",
+    default="00a6ffc1-6ce9-3bc3-a060-6006e9893a1a",
+    help="unique log identifier.",
+    type=str,
+)
+@click.option(
+    "-r",
+    "--max-range-m",
+    type=float,
+    default=100,
+    help="Maximum range of map entities from egovehicle to consider for rendering (by l-infinity norm).",
+)
+@click.option(
+    "-d",
+    "--use-depth-map-for_occlusion",
+    default=True,
+    help="Whether to use an inferred depth map for rendering occluded elements (defaults to True).",
+    type=bool,
+)
+@click.option(
+    "-s",
+    "--dump-single-frames",
+    default=False,
+    help="Whether to save to disk individual RGB frames of the rendering, in addition to generating the mp4 file"
+    "(defaults to False). Note: can quickly generate 100s of MBs, for 200 KB frames.",
+    type=bool,
+)
+@click.option(
+    "-c",
+    "--cam-names",
+    default=tuple(x.value for x in list(RingCameras)),
+    help="List of camera viewpoints to render the map from.",
+    multiple=True,
+    type=str,
+)
+def run_generate_egoview_overlaid_map(
+    data_root: "os.PathLike[str]",
+    output_dir: "os.PathLike[str]",
+    log_id: str,
+    max_range_m: float,
+    use_depth_map_for_occlusion: bool,
+    dump_single_frames: bool,
+    cam_names: Tuple[str, ...],
+) -> None:
+    """Click entry point for visualizing map entities rendered on top of sensor imagery."""
+    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+    data_root = Path(data_root)
+    output_dir = Path(output_dir)
+
+    logger.info(
+        "data_root: %s, output_dir: %s, log_id: %s, max_range_m: %f, "
+        "use_depth_map_for_occlusion: %s, dump_single_frames %s",
+        data_root,
+        output_dir,
+        log_id,
+        max_range_m,
+        use_depth_map_for_occlusion,
+        dump_single_frames,
+    )
+    generate_egoview_overlaid_map(
+        data_root=data_root,
+        output_dir=output_dir,
+        log_id=log_id,
+        max_range_m=max_range_m,
+        use_depth_map_for_occlusion=use_depth_map_for_occlusion,
+        dump_single_frames=dump_single_frames,
+        cam_names=parse_camera_enum_types(cam_names),
+    )
+
+
+if __name__ == "__main__":
+    run_generate_egoview_overlaid_map()
diff --git a/tutorials/generate_forecasting_scenario_visualizations.py b/tutorials/generate_forecasting_scenario_visualizations.py
new file mode 100644
index 00000000..f94de042
--- /dev/null
+++ b/tutorials/generate_forecasting_scenario_visualizations.py
@@ -0,0 +1,122 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+"""Script to generate dynamic visualizations from a directory of Argoverse scenarios."""
+
+from enum import Enum, unique
+from pathlib import Path
+from typing import Final
+
+import click
+import numpy as np
+from joblib import Parallel, delayed
+from rich.progress import track
+
+from av2.datasets.motion_forecasting import scenario_serialization
+from av2.datasets.motion_forecasting.viz.scenario_visualization import visualize_scenario
+from av2.map.map_api import ArgoverseStaticMap
+
+_DEFAULT_N_JOBS: Final[int] = -2  # Use all but one CPUs
+
+
+@unique
+class SelectionCriteria(str, Enum):
+    """Valid criteria used to select Argoverse scenarios for visualization."""
+
+    FIRST: str = "first"
+    RANDOM: str = "random"
+
+
+def generate_scenario_visualizations(
+    argoverse_scenario_dir: Path,
+    viz_output_dir: Path,
+    num_scenarios: int,
+    selection_criteria: SelectionCriteria,
+    *,
+    debug: bool = False,
+) -> None:
+    """Generate and save dynamic visualizations for selected scenarios within `argoverse_scenario_dir`.
+
+    Args:
+        argoverse_scenario_dir: Path to local directory where Argoverse scenarios are stored.
+        viz_output_dir: Path to local directory where generated visualizations should be saved.
+        num_scenarios: Maximum number of scenarios for which to generate visualizations.
+        selection_criteria: Controls how scenarios are selected for visualization.
+        debug: Runs preprocessing in single-threaded mode when enabled.
+    """
+    Path(viz_output_dir).mkdir(parents=True, exist_ok=True)
+    all_scenario_files = sorted(argoverse_scenario_dir.rglob("*.parquet"))
+    scenario_file_list = (
+        all_scenario_files[:num_scenarios]
+        if selection_criteria == SelectionCriteria.FIRST
+        else np.random.choice(all_scenario_files, size=num_scenarios).tolist()  # type: ignore
+    )  # Ignoring type here because type of "choice" is partially unknown.
+
+    # Build inner function to generate visualization for a single scenario.
+    def generate_scenario_visualization(scenario_path: Path) -> None:
+        """Generate and save dynamic visualization for a single Argoverse scenario.
+
+        NOTE: This function assumes that the static map is stored in the same directory as the scenario file.
+
+        Args:
+            scenario_path: Path to the parquet file corresponding to the Argoverse scenario to visualize.
+        """
+        scenario_id = scenario_path.stem.split("_")[-1]
+        static_map_path = scenario_path.parents[0] / f"log_map_archive_{scenario_id}.json"
+        viz_save_path = viz_output_dir / f"{scenario_id}.mp4"
+
+        scenario = scenario_serialization.load_argoverse_scenario_parquet(scenario_path)
+        static_map = ArgoverseStaticMap.from_json(static_map_path)
+        visualize_scenario(scenario, static_map, viz_save_path)
+
+    # Generate visualization for each selected scenario in parallel (except if running in debug mode)
+    if debug:
+        for scenario_path in track(scenario_file_list):
+            generate_scenario_visualization(scenario_path)
+    else:
+        Parallel(n_jobs=_DEFAULT_N_JOBS)(
+            delayed(generate_scenario_visualization)(scenario_path) for scenario_path in track(scenario_file_list)
+        )
+
+
+@click.command(help="Generate visualizations from a directory of Argoverse scenarios.")
+@click.option(
+    "--argoverse-scenario-dir",
+    required=True,
+    help="Path to local directory where Argoverse scenarios are stored.",
+    type=click.Path(exists=True),
+)
+@click.option(
+    "--viz-output-dir",
+    required=True,
+    help="Path to local directory where generated visualizations should be saved.",
+    type=click.Path(),
+)
+@click.option(
+    "-n",
+    "--num-scenarios",
+    default=100,
+    help="Maximum number of scenarios for which to generate visualizations.",
+    type=int,
+)
+@click.option(
+    "-s",
+    "--selection-criteria",
+    default="first",
+    help="Controls how scenarios are selected for visualization - either the first available or at random.",
+    type=click.Choice(["first", "random"], case_sensitive=False),
+)
+@click.option("--debug", is_flag=True, default=False, help="Runs preprocessing in single-threaded mode when enabled.")
+def run_generate_scenario_visualizations(
+    argoverse_scenario_dir: str, viz_output_dir: str, num_scenarios: int, selection_criteria: str, debug: bool
+) -> None:
+    """Click entry point for generation of Argoverse scenario visualizations."""
+    generate_scenario_visualizations(
+        Path(argoverse_scenario_dir),
+        Path(viz_output_dir),
+        num_scenarios,
+        SelectionCriteria(selection_criteria.lower()),
+        debug=debug,
+    )
+
+
+if __name__ == "__main__":
+    run_generate_scenario_visualizations()
diff --git a/tutorials/generate_per_camera_videos.py b/tutorials/generate_per_camera_videos.py
new file mode 100644
index 00000000..52497f27
--- /dev/null
+++ b/tutorials/generate_per_camera_videos.py
@@ -0,0 +1,113 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Generate an mp4 video for each ring camera, for each AV2 log."""
+
+import logging
+import sys
+from pathlib import Path
+from typing import Final, List
+
+import click
+import numpy as np
+from joblib import Parallel, delayed
+
+import av2.rendering.video as video_utils
+import av2.utils.io as io_utils
+from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
+from av2.datasets.sensor.constants import RingCameras
+from av2.utils.typing import NDArrayByte
+
+RING_CAMERA_FPS: Final[int] = 20
+
+# for every 50 images, print a status message
+PRINT_EVERY: Final[int] = 50
+
+logger = logging.getLogger(__name__)
+
+
+def generate_per_camera_videos(data_root: Path, output_dir: Path, num_workers: int) -> None:
+    """Launch jobs to render ring camera .mp4 videos for all sensor logs available on disk.
+
+    Args:
+        data_root: Path to local directory where the Argoverse 2 Sensor Dataset logs are stored.
+        output_dir: Path to local directory where videos will be saved.
+        num_workers: Number of worker processes to use for rendering.
+    """
+    loader = AV2SensorDataLoader(data_dir=data_root, labels_dir=data_root)
+    log_ids = loader.get_log_ids()
+
+    if num_workers > 1:
+        Parallel(n_jobs=num_workers)(
+            delayed(render_log_ring_camera_videos)(output_dir, loader, log_id) for log_id in log_ids
+        )
+    else:
+        for log_id in log_ids:
+            render_log_ring_camera_videos(output_dir, loader, log_id)
+
+
+def render_log_ring_camera_videos(output_dir: Path, loader: AV2SensorDataLoader, log_id: str) -> None:
+    """Render .mp4 videos for all ring cameras of a single log.
+
+    Args:
+        output_dir: Path to local directory where videos will be saved.
+        loader: data loader for sensor data.
+        log_id: unique ID for AV2 scenario/log.
+    """
+    # The logger must be initialized here, otherwise no output will be emitted to stdout.
+    # joblib overrides the logger config, see: https://github.com/joblib/joblib/issues/692
+    logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+    logger.info("Generating ring camera videos for %s", log_id)
+    for camera_name in list(RingCameras):
+        video_save_fpath = Path(output_dir) / f"{log_id}_{camera_name}.mp4"
+        if video_save_fpath.exists():
+            logger.info("Video already exists for %s, %s, so skipping...", log_id, camera_name)
+            continue
+
+        cam_im_fpaths = loader.get_ordered_log_cam_fpaths(log_id, camera_name)
+        num_cam_imgs = len(cam_im_fpaths)
+
+        video_list: List[NDArrayByte] = []
+        for i, im_fpath in enumerate(cam_im_fpaths):
+            if i % PRINT_EVERY == 0:
+                logger.info(f"\tOn file {i}/{num_cam_imgs} of camera {camera_name} of {log_id}")
+            img_rgb = io_utils.read_img(im_fpath, channel_order="RGB")
+            video_list.append(img_rgb)
+
+        video: NDArrayByte = np.stack(video_list).astype(np.uint8)
+        video_utils.write_video(
+            video=video,
+            dst=video_save_fpath,
+            fps=RING_CAMERA_FPS,
+            preset="medium",
+        )
+
+
+@click.command(help="Generate map visualizations on ego-view imagery from the Argoverse 2 Sensor Dataset.")
+@click.option(
+    "-d",
+    "--data-root",
+    required=True,
+    help="Path to local directory where the Argoverse 2 Sensor Dataset logs are stored.",
+    type=click.Path(exists=True),
+)
+@click.option(
+    "-o",
+    "--output-dir",
+    required=True,
+    help="Path to local directory where videos will be saved.",
+    type=str,
+)
+@click.option(
+    "--num-workers",
+    required=True,
+    help="Number of worker processes to use for rendering.",
+    type=int,
+)
+def run_generate_per_camera_videos(data_root: str, output_dir: str, num_workers: int) -> None:
+    """Click entry point for ring camera .mp4 video generation."""
+    logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+    generate_per_camera_videos(data_root=Path(data_root), output_dir=Path(output_dir), num_workers=num_workers)
+
+
+if __name__ == "__main__":
+    run_generate_per_camera_videos()
diff --git a/tutorials/map_teaser_notebook.py b/tutorials/map_teaser_notebook.py
new file mode 100644
index 00000000..d37fc942
--- /dev/null
+++ b/tutorials/map_teaser_notebook.py
@@ -0,0 +1,372 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Quick tutorial on AV2 map functionality, with visualizations of various map elements."""
+
+import logging
+import sys
+from pathlib import Path
+from typing import Final, Sequence, Tuple, Union
+
+import click
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.axes import Axes
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+import av2.geometry.polyline_utils as polyline_utils
+import av2.rendering.vector as vector_plotting_utils
+from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
+from av2.map.lane_segment import LaneSegment
+from av2.map.map_api import ArgoverseStaticMap
+from av2.utils.typing import NDArrayFloat
+
+logger = logging.getLogger(__name__)
+
+
+SAVE_DIR = Path(__file__).resolve().parent.parent / "argoverse2_map_figures"
+
+# scaled to [0,1] for matplotlib.
+PURPLE_RGB: Final[Tuple[int, int, int]] = (201, 71, 245)
+PURPLE_RGB_MPL: Final[Tuple[float, float, float]] = (PURPLE_RGB[0] / 255, PURPLE_RGB[1] / 255, PURPLE_RGB[2] / 255)
+
+DARK_GRAY_RGB: Final[Tuple[int, int, int]] = (40, 39, 38)
+DARK_GRAY_RGB_MPL: Final[Tuple[float, float, float]] = (
+    DARK_GRAY_RGB[0] / 255,
+    DARK_GRAY_RGB[1] / 255,
+    DARK_GRAY_RGB[2] / 255,
+)
+
+OVERLAID_MAPS_ALPHA: Final[float] = 0.1
+
+
+def single_log_teaser(data_root: Path, log_id: str, save_figures: bool) -> None:
+    """For a single log, render all local lane segments in green, and pedestrian crossings in purple, in a bird's eye view.
+
+    Args:
+        data_root: path to where the AV2 logs live.
+        log_id: unique ID for the AV2 vehicle log.
+        save_figures: whether to save each generated figure to disk.
+    """
+    log_map_dirpath = data_root / log_id / "map"
+    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)
+
+    fig = plt.figure(figsize=(10, 10))
+    ax = fig.add_subplot()
+
+    for _, ls in avm.vector_lane_segments.items():
+        vector_plotting_utils.draw_polygon_mpl(ax, ls.polygon_boundary, color="g", linewidth=0.5)
+        vector_plotting_utils.plot_polygon_patch_mpl(ls.polygon_boundary, ax, color="g", alpha=0.2)
+
+    # plot all pedestrian crossings
+    for _, pc in avm.vector_pedestrian_crossings.items():
+        vector_plotting_utils.draw_polygon_mpl(ax, pc.polygon, color="m", linewidth=0.5)
+        vector_plotting_utils.plot_polygon_patch_mpl(pc.polygon, ax, color="m", alpha=0.2)
+
+    plt.axis("equal")
+    plt.tight_layout()
+    if save_figures:
+        plt.savefig(SAVE_DIR / f"lane_graph_ped_crossings_{log_id}_bev.jpg", dpi=500)
+    plt.show()
+
+    # Plot drivable areas in light gray on a new plot.
+    fig = plt.figure(figsize=(10, 10))
+    ax = fig.add_subplot()
+    for da in list(avm.vector_drivable_areas.values()):
+        vector_plotting_utils.draw_polygon_mpl(ax, da.xyz, color="gray", linewidth=0.5)
+        vector_plotting_utils.plot_polygon_patch_mpl(da.xyz, ax, color="gray", alpha=0.2)
+
+    plt.axis("equal")
+    plt.tight_layout()
+    if save_figures:
+        plt.savefig(SAVE_DIR / f"drivable_areas_{log_id}_bev.jpg", dpi=500)
+    plt.show()
+    plt.close("all")
+
+
+def visualize_raster_layers(data_root: Path, log_id: str, save_figures: bool) -> None:
+    """Visualize the ground surface height/elevation map, w/ a colorbar indicating the value range.
+
+    Also, visualize side-by-side plots of the 3 raster arrays -- ground height, drivable area, ROI.
+
+    Args:
+        data_root: path to where the AV2 logs live.
+        log_id: unique ID for the AV2 vehicle log.
+        save_figures: whether to save each generated figure to disk.
+
+    Raises:
+        ValueError: If `self.raster_ground_height_layer`,`self.raster_drivable_area_layer`,
+            or `self.raster_roi_layer` is `None`.
+    """
+    log_map_dirpath = data_root / log_id / "map"
+    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)
+
+    if avm.raster_ground_height_layer is None:
+        raise ValueError("Raster ground height is not loaded!")
+    if avm.raster_drivable_area_layer is None:
+        raise ValueError("Raster drivable area is not loaded!")
+    if avm.raster_roi_layer is None:
+        raise ValueError("Raster ROI is not loaded!")
+
+    height_array = avm.raster_ground_height_layer.array
+    ax = plt.subplot()
+    plt.title("Ground surface height (@ 30 centimeter resolution).")
+    img = plt.imshow(np.flipud(height_array))  # type: ignore
+
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    plt.colorbar(img, cax=cax)
+    if save_figures:
+        plt.savefig(SAVE_DIR / f"ground_surface_height_{log_id}_bev.jpg", dpi=500)
+    plt.show()
+    plt.close("all")
+
+    fig = plt.figure(figsize=(10, 10))
+    plt.subplot(1, 3, 1)
+    plt.imshow(np.flipud(height_array))  # type: ignore
+    plt.title("Ground Surface Height")
+
+    plt.subplot(1, 3, 2)
+    da_array = avm.raster_drivable_area_layer.array
+    plt.imshow(np.flipud(da_array))  # type: ignore
+    plt.title("Drivable Area (rasterized \nfrom vector polygons)")
+
+    plt.subplot(1, 3, 3)
+    roi_array = avm.raster_roi_layer.array
+    plt.imshow(np.flipud(roi_array))  # type: ignore
+    plt.title("Region of Interest (ROI)")
+
+    fig.tight_layout()
+    if save_figures:
+        plt.savefig(SAVE_DIR / f"all_raster_layers_{log_id}_bev.jpg", dpi=500)
+    plt.show()
+    plt.close("all")
+
+
+def overlaid_maps_all_logs_teaser(data_root: Path) -> None:
+    """Render local maps rendered in the egovehicle frame from all logs on top of one another.
+
+    The egovehicle points towards the +x axis in each.
+
+    Args:
+        data_root: path to where the AV2 logs live.
+    """
+    loader = AV2SensorDataLoader(data_dir=data_root, labels_dir=data_root)
+    log_ids = loader.get_log_ids()
+
+    fig = plt.figure(1, figsize=(10, 10), dpi=90)
+    ax = fig.add_subplot(111)
+
+    for i, log_id in enumerate(log_ids):
+        log_map_dirpath = data_root / log_id / "map"
+
+        avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)
+        lane_segments = avm.get_scenario_lane_segments()
+
+        lidar_fpaths = loader.get_ordered_log_lidar_fpaths(log_id=log_id)
+        timestamp0 = int(lidar_fpaths[0].stem)
+
+        city_SE3_egot0 = loader.get_city_SE3_ego(log_id, timestamp0)
+
+        color = DARK_GRAY_RGB_MPL
+
+        linestyle: Union[str, Tuple[int, Tuple[int, int]]] = ""
+
+        for ls in lane_segments:
+            pts_city = ls.polygon_boundary
+            pts_ego = city_SE3_egot0.inverse().transform_point_cloud(pts_city)
+
+            for bound_type, bound_city in zip(
+                [ls.left_mark_type, ls.right_mark_type], [ls.left_lane_boundary, ls.right_lane_boundary]
+            ):
+                if "YELLOW" in bound_type:
+                    mark_color = "y"
+                elif "WHITE" in bound_type:
+                    mark_color = "w"
+                elif "BLUE" in bound_type:
+                    mark_color = "b"
+                else:
+                    mark_color = "grey"  # mark color could be "None"
+
+                LOOSELY_DASHED = (0, (5, 10))
+
+                if "DASHED" in bound_type:
+                    linestyle = LOOSELY_DASHED
+                else:
+                    linestyle = "solid"
+
+                bound_ego = city_SE3_egot0.inverse().transform_point_cloud(bound_city.xyz)
+                ax.plot(
+                    bound_ego[:, 0],
+                    bound_ego[:, 1],
+                    mark_color,
+                    alpha=OVERLAID_MAPS_ALPHA,
+                    linestyle=linestyle,
+                    zorder=i,
+                )
+
+            vector_plotting_utils.plot_polygon_patch_mpl(
+                polygon_pts=pts_ego, ax=ax, color=color, alpha=OVERLAID_MAPS_ALPHA, zorder=i
+            )
+
+    plt.axis("equal")
+    plt.xlim(-30, 30)
+    plt.ylim(-30, 30)
+    plt.show()
+    plt.close("all")
+
+
+def plot_lane_segments(
+    ax: Axes, lane_segments: Sequence[LaneSegment], lane_color: Tuple[float, float, float] = DARK_GRAY_RGB_MPL
+) -> None:
+    """Plot lane segments onto a Matplotlib canvas, according to their lane marking boundary type/color.
+
+    Note: we use an approximation for SOLID_DASHED and other mixed pattern/color marking types.
+
+    Args:
+        ax: matplotlib figure to use as drawing canvas.
+        lane_segments: lane segment objects. The lane markings along their boundaries will be rendered.
+        lane_color: Color of the lane.
+    """
+    for ls in lane_segments:
+        pts_city = ls.polygon_boundary
+        ALPHA = 1.0  # 0.1
+        vector_plotting_utils.plot_polygon_patch_mpl(
+            polygon_pts=pts_city, ax=ax, color=lane_color, alpha=ALPHA, zorder=1
+        )
+
+        mark_color: str = ""
+        linestyle: Union[str, Tuple[int, Tuple[int, int]]] = ""
+        for bound_type, bound_city in zip(
+            [ls.left_mark_type, ls.right_mark_type], [ls.left_lane_boundary, ls.right_lane_boundary]
+        ):
+            if "YELLOW" in bound_type:
+                mark_color = "y"
+            elif "WHITE" in bound_type:
+                mark_color = "w"
+            elif "BLUE" in bound_type:
+                mark_color = "b"
+            else:
+                mark_color = "grey"
+
+            LOOSELY_DASHED = (0, (5, 10))
+
+            if "DASHED" in bound_type:
+                linestyle = LOOSELY_DASHED
+            else:
+                linestyle = "solid"
+
+            if "DOUBLE" in bound_type:
+                left, right = polyline_utils.get_double_polylines(
+                    polyline=bound_city.xyz[:, :2], width_scaling_factor=0.1
+                )
+                ax.plot(left[:, 0], left[:, 1], color=mark_color, alpha=ALPHA, linestyle=linestyle, zorder=2)
+                ax.plot(right[:, 0], right[:, 1], color=mark_color, alpha=ALPHA, linestyle=linestyle, zorder=2)
+            else:
+                ax.plot(
+                    bound_city.xyz[:, 0],
+                    bound_city.xyz[:, 1],
+                    mark_color,
+                    alpha=ALPHA,
+                    linestyle=linestyle,
+                    zorder=2,
+                )
+
+
+def visualize_ego_pose_and_lane_markings(data_root: Path, log_id: str, save_figures: bool) -> None:
+    """Visualize both ego-vehicle poses and the per-log local vector map.
+
+    Crosswalks are plotted in purple. Lane segments plotted in dark gray. Ego-pose in red.
+
+    Args:
+        data_root: path to where the AV2 logs live.
+        log_id: unique ID for the AV2 vehicle log.
+        save_figures: whether to save each generated figure to disk.
+    """
+    loader = AV2SensorDataLoader(data_dir=data_root, labels_dir=data_root)
+
+    fig = plt.figure(1, figsize=(10, 10))
+    ax = fig.add_subplot(111)
+
+    log_map_dirpath = data_root / log_id / "map"
+    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)
+
+    # retain every pose first.
+    traj_ns = loader.get_subsampled_ego_trajectory(log_id, sample_rate_hz=1e9)
+    # now, sample @ 1 Hz
+    traj_1hz = loader.get_subsampled_ego_trajectory(log_id, sample_rate_hz=1.0)
+    med: NDArrayFloat = np.median(traj_ns, axis=0)  # type: ignore
+    med_x, med_y = med
+
+    # Derive plot area from trajectory (with radius defined in infinity norm).
+    # A larger distance traveled during trajectory means we should have a larger viewing window size.
+    view_radius_m: float = float(np.linalg.norm(traj_ns[-1] - traj_ns[0])) + 20  # type: ignore
+    xlims = [med_x - view_radius_m, med_x + view_radius_m]
+    ylims = [med_y - view_radius_m, med_y + view_radius_m]
+
+    crosswalk_color = PURPLE_RGB_MPL
+    CROSSWALK_ALPHA = 0.6
+    for pc in avm.get_scenario_ped_crossings():
+        vector_plotting_utils.plot_polygon_patch_mpl(
+            polygon_pts=pc.polygon[:, :2],
+            ax=ax,
+            color=crosswalk_color,
+            alpha=CROSSWALK_ALPHA,
+            zorder=3,
+        )
+
+    plot_lane_segments(ax=ax, lane_segments=avm.get_scenario_lane_segments())
+
+    # Plot nearly continuous line for ego-pose, and show the AV's pose @ 1 Hz w/ red unfilled circles.
+    ax.plot(traj_ns[:, 0], traj_ns[:, 1], color="r", zorder=4, label="Ego-vehicle pose")
+    ax.scatter(x=traj_1hz[:, 0], y=traj_1hz[:, 1], s=100, marker="o", facecolors="none", edgecolors="r", zorder=4)
+
+    plt.axis("equal")
+    plt.xlim(*xlims)
+    plt.ylim(*ylims)
+    plt.title(f"Log {log_id}")
+    plt.axis("off")
+    plt.legend()
+    plt.tight_layout()
+    if save_figures:
+        plt.savefig(SAVE_DIR / f"egovehicle_pose_on_map_{log_id}_bev.jpg", dpi=500)
+    plt.show()
+    plt.close("all")
+
+
+@click.command(help="Run map tutorial to visualize maps for the Argoverse 2 Datasets.")
+@click.option(
+    "-d",
+    "--data-root",
+    required=True,
+    help="Path to local directory where the Argoverse 2 Sensor Dataset logs are stored.",
+    type=click.Path(exists=True),
+)
+@click.option(
+    "-l",
+    "--log-id",
+    default="00a6ffc1-6ce9-3bc3-a060-6006e9893a1a",
+    help="unique log identifier.",
+    type=str,
+)
+@click.option(
+    "--save-figures",
+    default=False,
+    help="whether to save each generated figure to disk.",
+    type=bool,
+)
+def run_map_tutorial(data_root: str, log_id: str, save_figures: bool) -> None:
+    """Click entry point for map tutorial."""
+    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+    data_root_path = Path(data_root)
+
+    SAVE_DIR.mkdir(exist_ok=True, parents=True)
+
+    logger.info("data_root: %s, log_id: %s", data_root_path, log_id)
+    single_log_teaser(data_root=data_root_path, log_id=log_id, save_figures=save_figures)
+    visualize_raster_layers(data_root=data_root_path, log_id=log_id, save_figures=save_figures)
+    visualize_ego_pose_and_lane_markings(data_root=data_root_path, log_id=log_id, save_figures=save_figures)
+    overlaid_maps_all_logs_teaser(data_root=data_root_path)
+
+
+if __name__ == "__main__":
+    run_map_tutorial()
diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb
new file mode 100644
index 00000000..29c82ce8
--- /dev/null
+++ b/tutorials/map_tutorial.ipynb
@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7c092a3b",
+   "metadata": {},
+   "source": [
+    "In this tutorial, we'll demonstrate the Argoverse 2.0 map API, and visualize some of the map data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f25e1e66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from types import SimpleNamespace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d70c7396",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path to where the logs live\n",
+    "dataroot = \"/Users/jlambert/Downloads/av2-v7-2022-01-15\"\n",
+    "\n",
+    "# unique log identifier\n",
+    "log_id = \"adcf7d18-0510-35b0-a2fa-b4cea13a6d76\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f919b29b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "args = SimpleNamespace(**{\"dataroot\": dataroot, \"log_id\": log_id})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15a8196d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "from pathlib import Path\n",
+    "from typing import List\n",
+    "\n",
+    "import matplotlib\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "\n",
+    "import av2.geometry.polyline_utils as polyline_utils\n",
+    "import av2.rendering.vector as vector_plotting_utils\n",
+    "from av2.datasets.sensor_dataset.av2_sensor_dataloader import AV2SensorDataLoader\n",
+    "from av2.map.map_api import ArgoverseStaticMap, LaneSegment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88d1aaf7",
+   "metadata": {},
+   "source": [
+    "First, we'll plot the lane graph and crosswalks, with crosswalks colored purple, and the lanes colored green."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a02b3b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scaled to [0,1] for matplotlib.\n",
+    "PURPLE_RGB = [201, 71, 245]\n",
+    "PURPLE_RGB_MPL = np.array(PURPLE_RGB) / 255\n",
+    "\n",
+    "DARK_GRAY_RGB = [40, 39, 38]\n",
+    "DARK_GRAY_RGB_MPL = np.array(DARK_GRAY_RGB) / 255"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d5ae748",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def single_log_teaser(args: argparse.Namespace) -> None:\n",
+    "    \"\"\"\n",
+    "    For a single log, render all local crosswalks in green, and pedestrian crossings in purple,\n",
+    "    in a bird's eye view.\n",
+    "    \"\"\"\n",
+    "    log_map_dirpath = Path(args.dataroot) / args.log_id / \"map\"\n",
+    "    avm = ArgoverseStaticMap.from_json(log_map_dirpath, build_raster=False)\n",
+    "\n",
+    "    fig = plt.figure(figsize=(10, 10))\n",
+    "    ax = fig.add_subplot()\n",
+    "\n",
+    "    for id, ls in avm.vector_lane_segments.items():\n",
+    "\n",
+    "        # right_ln_bnd\n",
+    "        # left_ln_bnd\n",
+    "        vector_plotting_utils.draw_polygon_mpl(ax, ls.polygon_boundary, color=\"g\", linewidth=0.5)\n",
+    "        vector_plotting_utils.plot_polygon_patch_mpl(ls.polygon_boundary, ax, color=\"g\", alpha=0.2)\n",
+    "\n",
+    "        x, y, z = ls.polygon_boundary.mean(axis=0)\n",
+    "        # plt.text(x, y, str(id))\n",
+    "\n",
+    "    # plot all pedestrian crossings\n",
+    "    for id, pc in avm.vector_pedestrian_crossings.items():\n",
+    "        vector_plotting_utils.draw_polygon_mpl(ax, pc.polygon, color=\"m\", linewidth=0.5)\n",
+    "        vector_plotting_utils.plot_polygon_patch_mpl(pc.polygon, ax, color=\"m\", alpha=0.2)\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e27fe09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_log_teaser(args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56a98bd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_raster_layers(args: argparse.Namespace) -> None:\n",
+    "    \"\"\"Visualize the ground surface height/elevation map, w/ a colorbar indicating the value range.\n",
+    "\n",
+    "    Also, visualize side-by-side plots of the 3 raster arrays -- ground height, drivable area, ROI.\n",
+    "    \"\"\"\n",
+    "    log_map_dirpath = Path(args.dataroot) / args.log_id / \"map\"\n",
+    "    avm = ArgoverseStaticMap.from_json(log_map_dirpath, build_raster=True)\n",
+    "\n",
+    "    height_array = avm.raster_ground_height_layer.array\n",
+    "    ax = plt.subplot()\n",
+    "    plt.title(\"Ground surface height (@ 30 centimeter resolution).\")\n",
+    "    img = plt.imshow(np.flipud(height_array))\n",
+    "\n",
+    "    divider = make_axes_locatable(ax)\n",
+    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "    plt.colorbar(img, cax=cax)\n",
+    "    plt.show()\n",
+    "\n",
+    "    plt.figure(figsize=(20, 10))\n",
+    "    plt.subplot(1, 3, 1)\n",
+    "    plt.imshow(np.flipud(height_array))\n",
+    "    plt.title(\"Ground Surface Height\")\n",
+    "\n",
+    "    plt.subplot(1, 3, 2)\n",
+    "    da_array = avm.raster_drivable_area_layer.array\n",
+    "    plt.imshow(np.flipud(da_array))\n",
+    "    plt.title(\"Drivable Area (rasterized \\nfrom vector polygons)\")\n",
+    "\n",
+    "    plt.subplot(1, 3, 3)\n",
+    "    roi_array = avm.raster_roi_layer.array\n",
+    "    plt.imshow(np.flipud(roi_array))\n",
+    "    plt.title(\"Region of Interest (ROI)\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bdc3926",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "visualize_raster_layers(args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f45e82da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_lane_segments(\n",
+    "    ax: matplotlib.axes.Axes, lane_segments: List[LaneSegment], lane_color: np.ndarray = DARK_GRAY_RGB_MPL\n",
+    ") -> None:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    Args:\n",
+    "        ax:\n",
+    "        lane_segments:\n",
+    "    \"\"\"\n",
+    "    for ls in lane_segments:\n",
+    "        pts_city = ls.polygon_boundary\n",
+    "        ALPHA = 1.0  # 0.1\n",
+    "        vector_plotting_utils.plot_polygon_patch_mpl(\n",
+    "            polygon_pts=pts_city, ax=ax, color=lane_color, alpha=ALPHA, zorder=1\n",
+    "        )\n",
+    "\n",
+    "        for bound_type, bound_city in zip(\n",
+    "            [ls.left_mark_type, ls.right_mark_type], [ls.left_lane_boundary, ls.right_lane_boundary]\n",
+    "        ):\n",
+    "            if \"YELLOW\" in bound_type:\n",
+    "                mark_color = \"y\"\n",
+    "            elif \"WHITE\" in bound_type:\n",
+    "                mark_color = \"w\"\n",
+    "            else:\n",
+    "                mark_color = \"grey\"  # \"b\" lane_color #\n",
+    "\n",
+    "            LOOSELY_DASHED = (0, (5, 10))\n",
+    "\n",
+    "            if \"DASHED\" in bound_type:\n",
+    "                linestyle = LOOSELY_DASHED\n",
+    "            else:\n",
+    "                linestyle = \"solid\"\n",
+    "\n",
+    "            if \"DOUBLE\" in bound_type:\n",
+    "                left, right = polyline_utils.get_double_polylines(\n",
+    "                    polyline=bound_city.xyz[:, :2], width_scaling_factor=0.1\n",
+    "                )\n",
+    "                ax.plot(left[:, 0], left[:, 1], mark_color, alpha=ALPHA, linestyle=linestyle, zorder=2)\n",
+    "                ax.plot(right[:, 0], right[:, 1], mark_color, alpha=ALPHA, linestyle=linestyle, zorder=2)\n",
+    "            else:\n",
+    "                ax.plot(\n",
+    "                    bound_city.xyz[:, 0],\n",
+    "                    bound_city.xyz[:, 1],\n",
+    "                    mark_color,\n",
+    "                    alpha=ALPHA,\n",
+    "                    linestyle=linestyle,\n",
+    "                    zorder=2,\n",
+    "                )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f7dec27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def argoverse2_sensor_dataset_teaser(args: argparse.Namespace, save_plot: bool = False) -> None:\n",
+    "    \"\"\"\n",
+    "    Visualize both ego-vehicle poses and the per-log local vector map.\n",
+    "\n",
+    "    Crosswalks are plotted in purple. Lane segments plotted in dark gray. Ego-pose in red.\n",
+    "    \"\"\"\n",
+    "    loader = AV2SensorDataLoader(data_dir=args.dataroot, labels_dir=args.dataroot)\n",
+    "\n",
+    "    fig = plt.figure(1, figsize=(10, 10))\n",
+    "    ax = fig.add_subplot(111)\n",
+    "\n",
+    "    log_map_dirpath = Path(args.dataroot) / args.log_id / \"map\"\n",
+    "\n",
+    "    avm = ArgoverseStaticMap.from_json(log_map_dirpath, build_raster=False)\n",
+    "\n",
+    "    # retain every pose first.\n",
+    "    traj_ns = loader.get_subsampled_ego_trajectory(args.log_id, sample_rate_hz=1e9)\n",
+    "    # now, sample @ 1 Hz\n",
+    "    traj_1hz = loader.get_subsampled_ego_trajectory(args.log_id, sample_rate_hz=1.0)\n",
+    "    med_x, med_y = np.median(traj_ns, axis=0)\n",
+    "\n",
+    "    # Derive plot area from trajectory (with radius defined in infinity norm).\n",
+    "    view_radius_m = 50\n",
+    "    xlims = [med_x - view_radius_m, med_x + view_radius_m]\n",
+    "    ylims = [med_y - view_radius_m, med_y + view_radius_m]\n",
+    "\n",
+    "    crosswalk_color = PURPLE_RGB_MPL\n",
+    "    CROSSWALK_ALPHA = 0.6\n",
+    "    for pc in avm.get_scenario_ped_crossings():\n",
+    "        vector_plotting_utils.plot_polygon_patch_mpl(\n",
+    "            polygon_pts=pc.polygon[:, :2],\n",
+    "            ax=ax,\n",
+    "            color=crosswalk_color,\n",
+    "            alpha=CROSSWALK_ALPHA,\n",
+    "            zorder=3,\n",
+    "        )\n",
+    "\n",
+    "    plot_lane_segments(ax=ax, lane_segments=avm.get_scenario_lane_segments())\n",
+    "\n",
+    "    ax.plot(traj_ns[:, 0], traj_ns[:, 1], color=\"r\", zorder=4, label=\"Ego-vehicle pose\")\n",
+    "    ax.scatter(\n",
+    "        traj_1hz[:, 0], traj_1hz[:, 1], 100, facecolors=\"none\", edgecolors=\"r\", zorder=4\n",
+    "    )  # marker='o', color=\"r\")\n",
+    "\n",
+    "    plt.axis(\"equal\")\n",
+    "    plt.xlim(*xlims)\n",
+    "    plt.ylim(*ylims)\n",
+    "    plt.title(f\"Log {args.log_id}\")\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac070630",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "argoverse2_sensor_dataset_teaser(args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b3cc09c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/untar_tbv.py b/tutorials/untar_tbv.py
new file mode 100644
index 00000000..10fc4d76
--- /dev/null
+++ b/tutorials/untar_tbv.py
@@ -0,0 +1,89 @@
+# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
+
+"""Script to untar tar.gz files from the Trust but Verify (TbV) dataset."""
+
+import subprocess
+from pathlib import Path
+from typing import Final, Optional, Tuple
+
+import click
+import joblib
+from joblib import Parallel, delayed
+
+NUM_TBV_SHARDS: Final[int] = 21
+
+
+def run_command(cmd: str, return_output: bool = False) -> Tuple[Optional[bytes], Optional[bytes]]:
+    """Excute a system call, and block until the system call completes.
+
+    Args:
+        cmd: string, representing shell command
+        return_output: whether to return STDOUT and STDERR data.
+
+    Returns:
+        Tuple of (stdout, stderr) output if return_output is True, else None
+    """
+    print(cmd)
+    (stdout_data, stderr_data) = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).communicate()
+
+    if return_output:
+        return stdout_data, stderr_data
+    return None, None
+
+
+def untar_tbv_dataset(num_workers: int, shard_dirpath: Path, desired_tbv_dataroot: Path) -> None:
+    """Untar each of the tar.gz archives.
+
+    Args:
+        num_workers: Number of worker processes to use for simultaneous extraction
+        shard_dirpath: Path to local directory where shard tar.gz files where downloaded.
+        desired_tbv_dataroot: Path to local directory, where TbV logs will be extracted.
+
+    Raises:
+        RuntimeError: If one of the expected tar.gz shard files is missing.
+    """
+    desired_tbv_dataroot.mkdir(exist_ok=True, parents=True)
+
+    jobs = []
+    for i in range(NUM_TBV_SHARDS):
+        shard_fpath = shard_dirpath / f"TbV_v1.0_shard{i}.tar.gz"
+        if not shard_fpath.exists():
+            raise RuntimeError(f"Shard file not found {shard_fpath}")
+        cmd = f"tar -xvzf {shard_fpath} --directory {desired_tbv_dataroot}"
+        jobs.append(cmd)
+
+    if num_workers > 1:
+        Parallel(n_jobs=num_workers)(delayed(run_command)(cmd) for cmd in jobs)
+    else:
+        for cmd in jobs:
+            run_command(cmd)
+
+
+@click.command(help="Extract TbV tar.gz archives that were previously downloaded to a local disk.")
+@click.option(
+    "--num-workers",
+    required=True,
+    help="Number of worker processes to use for simultaneous extraction.",
+    type=int,
+)
+@click.option(
+    "--shard-dirpath",
+    required=True,
+    help="Path to local directory where shard tar.gz files were previously downloaded.",
+    type=click.Path(exists=True),
+)
+@click.option(
+    "--desired-tbv-dataroot",
+    required=True,
+    help="Path to local directory, where TbV logs will be extracted.",
+    type=str,
+)
+def run_untar_tbv_dataset(num_workers: int, shard_dirpath: str, desired_tbv_dataroot: str) -> None:
+    """Click entry point for TbV tar.gz file extraction."""
+    untar_tbv_dataset(
+        num_workers=num_workers, shard_dirpath=Path(shard_dirpath), desired_tbv_dataroot=Path(desired_tbv_dataroot)
+    )
+
+
+if __name__ == "__main__":
+    run_untar_tbv_dataset()