MATLAB Routine for Fundamental Matrix Computation

Fundamental matrix computation represents a critical step in computer vision research and learning applications. This MATLAB routine provides a practical implementation for calculating fundamental matrices, serving as both an educational reference and research tool. The code demonstrates essential MATLAB programming techniques including feature point matching using SIFT or SURF algorithms, robust estimation through RANSAC implementation, and linear algebra operations with built-in functions like svd() for singular value decomposition. Key implementation aspects include: - Normalization of input point coordinates for numerical stability - Eight-point algorithm implementation with algebraic minimization - Epipolar constraint enforcement through rank-2 matrix approximation - Validation methods for epipolar geometry verification Users can modify parameters such as RANSAC threshold values, input feature correspondences, and normalization schemes to observe how different configurations affect the fundamental matrix estimation accuracy. The routine also includes visualization components for plotting epipolar lines, enabling better understanding of geometric relationships between stereo image pairs. This implementation serves as an excellent foundation for advancing to more complex computer vision tasks like camera pose estimation, 3D reconstruction, and structure-from-motion pipelines.