Efficient large-scale geometric verification for structure from motion

Abstract

Geometric verification is a fundamental problem in epipolar geometry, which estimates fundamental matrix and homography matrix to confirm that image pairs share a common 3D structure. However, it still suffers from low efficiency when it encounters large-scale Structure from Motion (SfM). In this paper, we adopt the linear congruence algorithm to sample point-pairs in parallel. Then, we propose to simultaneously estimate a certain number of candidate fundamental/homography matrices in GPU to avoid the iterative random point sets sampling and perform matrix estimation based on these point sets, followed by best matrix selection and further parallel refinement. Experiments on extensive datasets show that our GPU-based geometric verification is up to seventy times faster than original iteration method while maintaining comparable satisfactory 3D reconstruction results.