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CS231A Computer Vision

From 3D Reconstruction to Recognition

(Winter 2022)

Course homepage http://web.stanford.edu/class/cs231a/index.html

Content

  1. Problem set 0
    • Basic Matrix/Vector Manipulation (skipped)
    • Basic Image Manipulations
    • Singular Value Decomposition
  2. Problem set 1
    • Projective Geometry Problems
    • Affine Camera Calibration
    • Single View Geometry
  3. Problem set 2
    • Fundamental Matrix Estimation From Point Correspondences
    • Matching Homographies for Image Rectification
    • The Factorization Method
    • Triangulation in Structure From Motion
  4. Problem set 3
    • Space Carving
    • Representation Learning
    • Supervised Monocular Depth Estimation
    • Unsupervised Monocular Depth Estimation (skipped)
    • Tracking with Optical and Scene flow
  5. Problem set 4
    • Extended Kalman Filter with a Nonlinear Observation Model
    • From Monocular to Stereo Vision
    • Linear Kalman Filter with a Learned Inverse Observation Model

See reference solutions at https://github.com/mikucy/CS231A (Solutions for problems 1.3, 2.1 have inaccuracies. Problem set 4 and half of 3 is different, from 2018y.)