  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 feature before a sampling method is employed, such as RANSAC,
to verify initial matching results via repeated fitting of certain global
transformation among the images. However, incorrect matches may still exist.
Thus, a novel sampling scheme, Pentagon-Match (PMatch), is proposed in this
work to verify the correctness of initially matched keypoints using pentagons
randomly sampled from them. By ensuring shape and location of these pentagons
are view-invariant with various evaluations of cross-ratio (CR), incorrect
matches of keypoint can be identified easily with homography estimated from
correctly matched pentagons. Experimental results show that highly accurate
estimation of homography can be obtained efficiently for planar scenes of the
HPatches dataset, based on keypoint matching results provided by LoFTR.
Besides, accurate outlier identification for the above matching results and
possible extension of the approach for multi-plane situation are also
demonstrated.
