Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature for Local Feature Matching-Based Homography Estimation

In computer vision, finding correct point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research works focus on the matching of local features before a sampling method is employed, such as RANSAC, to verify initial matching results via repeated fitting of certain global transformation among the images. In this paper, a novel sampling scheme, Pentagon-Match (PMatch), is proposed for more efficient verification of the correctness of initially matched keypoints using pentagons randomly sampled from them. By ensuring shape and location of some sampled pentagons are view-invariant with various evaluations of cross-ratio (CR), incorrect matches of keypoint can then be identified easily with homography estimated from these pentagons. Experimental results show that highly accurate estimation of homography can be obtained efficiently and more accurately for planar scenes of the HPatches dataset, based on keypoint matching results provided by LoFTR. Besides, a new way of showing inliers (and outlier) for the above matching results and possible extension of the approach for multi-plane situation are also demonstrated.