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

• URL: http://arxiv.org/abs/2305.17463v1
• Date: Sat, 27 May 2023 12:41:23 GMT
• Title: Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature for Local Feature Matching-Based Homography Estimation
• Authors: Yueh-Cheng Huang, Chen-Tao Hsu, and Jen-Hui Chuang
• Abstract summary: 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. Pentagon-Match (PMatch) is proposed in this work to verify the correctness of initially matched keypoints using pentagons randomly sampled from them.
• Score: 2.240487187855135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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