Efficient line matching with homography

Abstract

In this paper, we propose a novel approach to line matching based on homography. The basic idea is to use cheaply obtainable matched points to boost the similarity between two images. Two types of homography method, which are estimated by direct linear transformation, transform images and extract their similar parts, laying a foundation for the use of optical flow tracking. The merit of the similarity is that rapid matching can be achieved by regionalizing line segments and local searching. For multiple homography estimation that can perform better than one global homography, we introduced the rank-one modification method of singular value decomposition to reduce the computation cost. The proposed approach results in point-to-point matches, which can be utilized with state-of-the-art point-match-based structures from motion (SfM) frameworks seamlessly. The outstanding performance and feasible robustness of our approach are demonstrated in this paper.