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Computer Science
Computer Vision
Computer Vision and Image Analysis
1. Foundations of Computer Vision
2. Image Formation and Representation
3. Fundamental Image Processing
4. Feature Detection and Description
5. Image Segmentation
6. Classical Object Recognition
7. Deep Learning for Computer Vision
8. Motion and Video Analysis
9. 3D Computer Vision
10. Computer Vision Applications
9.
3D Computer Vision
9.1.
Geometric Foundations
9.1.1.
3D Coordinate Systems
9.1.1.1.
World Coordinates
9.1.1.2.
Camera Coordinates
9.1.1.3.
Image Coordinates
9.1.2.
Homogeneous Coordinates
9.1.2.1.
3D Transformations
9.1.2.2.
Projection Matrices
9.1.2.3.
Coordinate Conversions
9.1.3.
Camera Geometry
9.1.3.1.
Single Camera Model
9.1.3.2.
Multiple Camera Systems
9.1.3.3.
Camera Calibration
9.2.
Epipolar Geometry
9.2.1.
Two-view Geometry
9.2.1.1.
Epipolar Constraint
9.2.1.2.
Essential Matrix
9.2.1.3.
Fundamental Matrix
9.2.2.
Epipolar Lines
9.2.2.1.
Geometric Interpretation
9.2.2.2.
Rectification Process
9.2.2.3.
Stereo Matching
9.3.
Stereo Vision
9.3.1.
Stereo Camera Setup
9.3.1.1.
Baseline Configuration
9.3.1.2.
Camera Synchronization
9.3.1.3.
Calibration Requirements
9.3.2.
Correspondence Problem
9.3.2.1.
Matching Algorithms
9.3.2.2.
Correlation Methods
9.3.2.3.
Dynamic Programming
9.3.3.
Disparity Computation
9.3.3.1.
Disparity Maps
9.3.3.2.
Depth Calculation
9.3.3.3.
Disparity Refinement
9.3.4.
3D Reconstruction
9.3.4.1.
Triangulation Methods
9.3.4.2.
Point Cloud Generation
9.3.4.3.
Surface Reconstruction
9.4.
Structure from Motion
9.4.1.
Feature Matching
9.4.1.1.
Multi-view Correspondence
9.4.1.2.
Track Generation
9.4.1.3.
Outlier Rejection
9.4.2.
Camera Pose Estimation
9.4.2.1.
Relative Pose
9.4.2.2.
Absolute Pose
9.4.2.3.
Pose Refinement
9.4.3.
3D Point Triangulation
9.4.3.1.
Linear Methods
9.4.3.2.
Nonlinear Optimization
9.4.3.3.
Bundle Adjustment
9.4.4.
Reconstruction Pipeline
9.4.4.1.
Incremental SfM
9.4.4.2.
Global SfM
9.4.4.3.
Hierarchical Methods
9.5.
SLAM Systems
9.5.1.
Visual SLAM Components
9.5.1.1.
Frontend Processing
9.5.1.2.
Backend Optimization
9.5.1.3.
Loop Closure Detection
9.5.2.
Mapping Strategies
9.5.2.1.
Sparse Maps
9.5.2.2.
Dense Maps
9.5.2.3.
Semantic Maps
9.5.3.
Localization Methods
9.5.3.1.
Feature-based Localization
9.5.3.2.
Direct Methods
9.5.3.3.
Hybrid Approaches
9.6.
3D Data Representation
9.6.1.
Point Clouds
9.6.1.1.
Acquisition Methods
9.6.1.2.
Processing Algorithms
9.6.1.3.
Visualization Techniques
9.6.2.
Voxel Representations
9.6.2.1.
3D Grids
9.6.2.2.
Octree Structures
9.6.2.3.
Sparse Representations
9.6.3.
Mesh Representations
9.6.3.1.
Triangle Meshes
9.6.3.2.
Surface Reconstruction
9.6.3.3.
Mesh Processing
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8. Motion and Video Analysis
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10. Computer Vision Applications