3D Reconstruction from Two Disparity Images Using Epipolar Geometry

When performing 3D reconstruction from two disparity images using MATLAB, we employ epipolar geometry relations, which enable precise determination of object positions and shapes in images. Epipolar geometry represents a fundamental computer vision technique that utilizes geometric relationships between feature points across images. Through analysis and computation of these relationships, we achieve comprehensive 3D reconstruction. In our implementation, we utilize MATLAB's Computer Vision Toolbox functions including: - detectSURFFeatures() for robust feature point detection - estimateFundamentalMatrix() to compute the epipolar geometry constraints - triangulate() function for 3D point cloud generation from matched features The reconstruction pipeline involves: 1. Feature detection and matching between stereo image pairs 2. Fundamental matrix estimation to establish epipolar constraints 3. Disparity map calculation using block matching or semi-global matching algorithms 4. 3D point cloud reconstruction through triangulation methods By processing and analyzing disparity images through this methodological approach, we obtain highly accurate and detailed 3D reconstruction results. This technology and methodology hold significant application potential in computer image processing and computer vision fields, particularly in areas such as autonomous navigation, 3D modeling, and augmented reality systems.