An Optimized Method Based on RANSAC for Fundamental Matrix Estimation
Wenjiang Wu, Wen Liu · 2018
Fundamental matrix estimation based on RANSAC will encounter the problems of computational inefficiency and low accuracy when outlier ratio is high. In this paper, an optimized method via modification of the RANSAC algorithm is proposed to solve these problems. First, an isolation forest-based algorithm is performed to detect outliers from putative SIFT correspondences according to distribution consistency of features in location, scale and orientation. Then, a number of obvious outliers are eliminated from putative correspondences, which will enhance the inlier ratio efficiently. Finally, fundamental matrix is estimated with the optimized set. Repeated experiments indicate that the proposed method has testified result in speed and accuracy.