Multiview geometry 1
Local Features
- 이미지에 중요한 부분
- Image representation
- Object appearance modeling
- Finding matches between multiple views
Model Fitting and RANSAC
- Finding vanishing points
- Image stitching
- 3D object recognition
- Fitting Technique: Least Square
- RANSAC
- Fitting Technique: Hough Transform
- Understanding camera models
- 2D transformations
- Translations
- Euclidean transformation
- Similarity transformation
- Affine transformation
- Projective transformation
- 3D Geometric Primitives
- points, lines, planes
- 3D Transformations
- 3D to 2D Projections
- Camera Models
- Camera
- Geometric Camera Models
- Pinhole Model
- Aperture shrinks to zero
- Inverted image is observed in image plane
Camera Calibration
- Geometric camera calibra:on
- Camera Matrix Estimation
- size and structure of the pattern are known
- Estimation of Intrinsic and Extrinsic Parameters
Two-View Geometry
- 2장의 이미지에서 대응 되는 기하학적 관계
- Homography
- collinearity is preserved
- Isotropic scaling transform
- Image Panorama Using Estimation
Epipolar Geometry
- epipolar
- converging image planes
- parallel image planes
- forward moving camera
- Two-View Relationship in Epipolar Geometry
- essential matrix
- Fundamental Matrix
Stereo Matching
- 두개의 센서로 부터 인지한 정보
- Parallel images
- Image Rectification
- 같은 선상에 이미지를 정렬
- Epipolar lines을 직선이 되도록 배치
- Basic Stereo Matching
- epipolar line
- depth vs disparity
- 비워있으면 더 진하게 나옴
- Correspondence Search
- Correspondence Search by Correlation
- 반복적인 패턴
- small diparity error
- window size effect
Wide-Baseline Matching
- Large baseline
- KeyNet
- detection 에 집중함
- Multi-Scale Index Proposal (M-SIP) Loss.
- Self-Supervised Equivariant Learning for Oriented Keypoint Detection
- Rotation-Equivariant Feature
- Window-based keypoint loss
- Dense orientation alignment loss
참조