Two-View Geometry Estimation Using RANSAC With Locality Preserving Constraint
Gang Wang, Xiaoliang Sun, Yang Shang, Zi Wang, Zhongchen Shi, Qifeng Yu
Abstract
The random sample consensus (RANSAC) based algorithm is widely used in estimating the two-view geometry from image point correspondences. However, it often becomes extremely slow when the data is contaminated by a large percentage of incorrect matches. To address this problem, the paper proposes a new modification of RANSAC called LP-RANSAC that is robust to varying inlier ratios and achieves large computational savings without deterioration in accuracy. LP-RANSAC integrates the locality preserving constraint into the universal RANSAC framework, which prunes most of the unreliable correspondences before the hypothesize-and-verify loop and guides non-uniform sampling to generate and verify promising models earlier. Unlike other guided sampling strategies, the proposed method is simple to implement and does not require any prior information. Extensive experiments performed on the publicly available datasets reveal that LP-RANSAC can achieve more accurate and stable solutions at much lower computational cost (in milliseconds on standard CPU) than state-of-the-art methods, particularly when handling problems with low inlier ratios.