An Enhanced Eight-Point Algorithm for Fundamental Matrix Estimation

This is an improved eight-point algorithm used for calculating the fundamental matrix. The enhanced algorithm incorporates Random Sample Consensus (RANSAC) techniques to minimize errors and effectively exclude outliers from the calculation process. The implementation is developed in MATLAB, providing an accessible and modifiable framework for researchers and developers. The algorithm implementation includes key functions such as normalized eight-point computation, RANSAC-based outlier detection, and robust estimation procedures. Through systematic sampling and consensus evaluation, the method significantly improves fundamental matrix accuracy by iteratively selecting optimal point correspondences while discarding erroneous matches. By utilizing this algorithm, users can achieve more precise fundamental matrix calculations, thereby enhancing the performance and accuracy of computer vision applications such as stereo vision, 3D reconstruction, and camera calibration. The MATLAB implementation offers clear modular structure, allowing easy customization of parameters like RANSAC iteration count and error thresholds to suit specific application requirements.