  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.
