Scene Flow

Camera Models

Camera Calibration

Single View Metrology

Epipolar Geometry

Stereo Systems

Structure from Motion

Active Stereo

Volumetric Stereo

Fitting

Representation Learning

Monocular Depth Estimation

Feature Tracking

Optimal Estimation

Neural Radiance Fields

Optical Flow

CS 231A: Computer Vision, From 3D Reconstruction to Recognition Stanford University Winter 2023 This course introduces concepts and applications in computer vision, focusing on geometry and 3D understanding. It covers topics like filtering, edge detection, segmentation, clustering, shape reconstruction from stereo, and high-level visual topics. Knowledge of linear algebra, basic probability, and statistics is required. An introduction to concepts and applications in computer vision primarily dealing with geometry and 3D understanding. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo; high-level vision topics such as learned low-level visual representations; depth estimation and optical/scene flow; 6D pose estimation and object tracking. Prerequisites: linear algebra, basic probability and statistics.   

CS 231A: Computer Vision, From 3D Reconstruction to Recognition

Computer Vision

Scene Flow

1 courses cover this concept

CS 231A: Computer Vision, From 3D Reconstruction to Recognition