Robust Computation of Fundamental Matrix Between Two Views Using RANSAC Algorithm

This article discusses the robustness of calculating the fundamental matrix between two views using the RANSAC (Random Sample Consensus) algorithm. This approach is widely adopted in computer vision due to its ability to handle significant noise and outliers in feature correspondences. The provided code implements two key methods: the 7-point algorithm (minimum solution requiring 7 correspondences) and the 8-point algorithm (linear solution using 8 or more points). Both algorithms compute the fundamental matrix that encapsulates the epipolar geometry between two views. The implementation includes proper normalization of point coordinates and robust estimation through RANSAC iteration, which randomly samples point correspondences, computes candidate matrices, and selects the best solution based on inlier count. These methods enable accurate calculation of geometric relationships between views, making them essential for applications such as 3D reconstruction, image stitching, and camera pose estimation. By leveraging RANSAC's robustness, we can effectively utilize fundamental computer vision principles in practical scenarios.