The classical matching pipeline used for visual localization typically
involves three steps: (i) local feature detection and description, (ii) feature
matching, and (iii) outlier rejection. Recently emerged correspondence networks
propose to perform those steps inside a single network but suffer from low
matching resolution due to the memory bottleneck. In this work, we propose a
new perspective to estimate correspondences in a detect-to-refine manner, where
we first predict patch-level match proposals and then refine them. We present
Patch2Pix, a novel refinement network that refines match proposals by
regressing pixel-level matches from the local regions defined by those
proposals and jointly rejecting outlier matches with confidence scores.
Patch2Pix is weakly supervised to learn correspondences that are consistent
with the epipolar geometry of an input image pair. We show that our refinement
network significantly improves the performance of correspondence networks on
image matching, homography estimation, and localization tasks. In addition, we
show that our learned refinement generalizes to fully-supervised methods
without re-training, which leads us to state-of-the-art localization
performance. The code is available at this https URL

本文提出了基于检测再细化的新视角来估计对应关系，并介绍了一种新颖的细化网络 Patch2Pix，它通过对匹配传播的精细调整和置信度分数的拒绝来提高对应关系网络的性能，实现了图像匹配、单应性估计和定位任务的最优性能。