Robust Fundamental Matrix Estimation with Accurate Outlier Detection

The estimation of fundamental matrix from two-view images has been an important topic of research in 3D computer vision. In this paper, we present an improved robust algorithm for fundamental matrix estimation via modification of the RANSAC algorithm. The proposed algorithm is based on constructing a voting array for all the point correspondence pairs to record the consistency votes for each point correspondence from a number of the fundamental matrix estimations determined from randomly selected subsets of correspondence pairs to facilitate the identification of outliers. The boundary between the inliers and outliers in the sorted voting array are determined through a hypothesis testing procedure. With this strategy, we can accurately determine the outliers from all pairs of point correspondences, thus leading to accurate and robust fundamental matrix estimation under noisy feature correspondences. Through experimental comparison with previous methods on simulated and real image data, we show the proposed algorithm in general outperforms other best-performed methods to date.