Image matching is a classic and fundamental task in computer vision. In this paper, under the hypothesis that the areas outside the co-visible regions carry little information, we propose a matching key-points crop (MKPC) algorithm. The MKPC locates, proposes and crops the critical regions, which are the co-visible areas with great efficiency and accuracy. Furthermore, building upon MKPC, we propose a general two-stage pipeline for image matching, which is compatible to any image matching models or combinations. We experimented with plugging SuperPoint + SuperGlue into the two-stage pipeline, whose results show that our method enhances the performance for outdoor pose estimations. What's more, in a fair comparative condition, our method outperforms the SOTA on Image Matching Challenge 2022 Benchmark, which represents the hardest outdoor benchmark of image matching currently.

翻译：图像匹配是计算机视觉中的一项经典基础任务。本文基于共视区域外区域信息量少的假设，提出匹配关键点裁剪（MKPC）算法。该算法能够高效精准地定位、提议并裁剪关键区域（即共视区域）。进一步地，我们基于MKPC构建了通用的两阶段图像匹配流程，该流程兼容任意图像匹配模型或其组合。将SuperPoint+SuperGlue嵌入两阶段流程的实验结果表明，该方法可提升室外位姿估计性能。此外，在公平对比条件下，本方法在目前最具挑战性的室外图像匹配基准——Image Matching Challenge 2022 Benchmark上超越了当前最优方法（SOTA）。