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Multi-view Feature Fusion

Overview

Here we provide instructions for multi-view feature fusion on different dataset, including ScanNet, Matterport3D, nuScenes, and Replica. This corresponds to Section 3.1 in our paper.

Note: Here we provide only codes for multiview fusion with the OpenSeg model. For LSeg per-pixel feature extraction and multi-view fusion, Check this repo.

Prerequisites

Data preprocessing

Follow this instruction to obtain the processed 2D and 3D data.

  • 3D: Point clouds in the pytorch format .pth
  • 2D: RGB-D images with their intrinsic and extrinsic parameters

Envinroment

You can simply activate the openscene conda environment, or alternatively, make sure the following package installed:

  • torch
  • tensorflow v2 (for OpenSeg feature extraction)
  • numpy
  • imageio
  • tqdm

To use OpenSeg as the feature extractor, you can either take the demo model inside the jupyter notebook from their official repo, or download from here.

Run the code

Take ScanNet as an example, to perform multi-view feature fusion your can run:

python scannet_openseg.py \
            --data_dir PATH/TO/scannet_processed \
            --output_dir PATH/TO/OUTPUT_DIR \
            --openseg_model PATH/TO/OPENSEG_MODEL \
            --process_id_range 0,100\
            --split train

where:

  • data_dir: path to the pre-processed 2D&3D data from here
  • output_dir: output directory to save your fused features
  • openseg_model: path to the OpenSeg model
  • process_id_range: only process scenes within the range
  • split: choose from train/val/test data to process

replica_openseg.py does not have process_id_range and split.

Customized Dataset

For your own customized dataset, one can use the provided codes as a reference. We recommend you to modify replica_openseg.py, especially if you only care about inferecing your own point clouds without 3D distillation. If you want to do 3D distillation, please check and modify scannet_openseg.py.

Suggestions & Troubleshooting

  • For using the OpenSeg model, you need to make sure you have an NVIDIA GPU with >30G memory.

For additional help, please refer to the code documentation or contact the author.