Fast and Robust Algorithm for Fundamental Matrix Estimation

Fundamental matrix estimation from two views plays an important role in 3D computer vision. In this paper, a fast and robust algorithm is proposed for the fundamental matrix estimation in the presence of outliers. Instead of algebra error, the reprojection error is adopted to evaluate the confidence of the fundamental matrix. Assuming Gaussian image noise, it is proved that the reprojection error can be described by a chi-square distribution, and thus, the outliers can be eliminated using the 3-sigma principle. With this strategy, the inlier set is robustly established in only two steps. Compared to classical RANSAC-based strategies, the proposed algorithm is very efficient with higher accuracy. Experimental evaluations and comparisons with previous methods demonstrate the effectiveness and advantages of the proposed approach.