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Image matching is a classic and fundamental task in computer vision. In thispaper, under the hypothesis that the areas outside the co-visible regions carrylittle information, we propose a matching key-points crop (MKPC) algorithm. TheMKPC locates, proposes and crops the critical regions, which are the co-visibleareas with great efficiency and accuracy. Furthermore, building upon MKPC, wepropose a general two-stage pipeline for image matching, which is compatible toany image matching models or combinations. We experimented with pluggingSuperPoint + SuperGlue into the two-stage pipeline, whose results show that ourmethod enhances the performance for outdoor pose estimations. What's more, in afair comparative condition, our method outperforms the SOTA on Image MatchingChallenge 2022 Benchmark, which represents the hardest outdoor benchmark ofimage matching currently.

Efficient and Accurate Co-Visible Region Localization with Matching  Key-Points Crop (MKPC): A Two-Stage Pipeline for Enhancing Image Matching  Performance