Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detectorfree methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code will be released to benefit the community.

翻译：局部特征匹配旨在建立图像对之间的稀疏对应关系。近年来，无检测器方法通常表现更优，但在尺度差异较大的图像对上效果不佳。本文提出基于细分补丁区域传输方法（PATS）以解决该问题。我们不构建昂贵的图像金字塔，而是将原始图像对分割为等尺寸补丁，逐步调整其尺寸并将其细分为相同尺度的更小补丁。然而，由于尺度差异由相对相机位姿与场景结构共同决定，在图像对间具有空间变化性，因此估计补丁间尺度差异颇具挑战。此外，真实场景的尺度差异真值难以获取。为此，我们提出补丁区域传输方法，以自监督方式学习尺度差异。与仅处理一对一匹配的二分图匹配不同，本方法可处理多对多关系。PATS同时提升匹配精度与覆盖范围，在相对位姿估计、视觉定位及光流估计等下游任务中表现优异。源代码将公开以回馈社区。