Camera Parameter Estimation from Fundamental Matrix using RANSAC Algorithm

This approach utilizes the Random Sample Consensus (RANSAC) algorithm to estimate camera intrinsic and extrinsic parameters from the fundamental matrix. RANSAC is an iterative parameter estimation method specifically designed to handle noisy data containing outliers. The implementation involves randomly sampling minimal point correspondences (typically 7 or 8 point pairs) to compute candidate fundamental matrices, then evaluating these models against the entire dataset to identify inliers. Key algorithmic steps include: 1) Random sampling of feature point matches, 2) Fundamental matrix calculation using linear methods like the 8-point algorithm, 3) Inlier classification based on geometric error thresholds, and 4) Iterative refinement until convergence. The robust fundamental matrix obtained through this process enables accurate camera pose estimation and 3D reconstruction by decomposing the essential matrix, which requires prior knowledge of camera calibration parameters. The method effectively handles real-world scenarios with imperfect feature matching and occlusions.