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:本地特性匹配的目的是在一对图像之间建立稀少的对应关系。 最近, 无探测器的方法表现一般比较好, 但在图像配对中则不令人满意。 在本文中, 我们提议使用分区( PATS) 的 Patch 地区交通( PATS) 解决这个问题。 我们不是建立昂贵的图像金字塔, 而是开始将原始图像配对分割成同等大小的补丁, 并逐步调整和将其细分为同一规模的较小补丁。 但是, 估计这些补丁之间的比例差异是非三角的, 因为比例差异是由相对的相机配置和场景结构决定的, 因而在空间上差异很大。 此外, 很难获得真实场景的地面真相 。 为此, 我们提议了补丁地区交通, 从而能够以自我超强的方式学习比例差异 。 与只处理一到一对一匹配的双面图形匹配相比, 我们的补丁地区交通可以处理许多到多个关系 。 PATS 改进了比例和覆盖范围, 并显示下游任务中的高级性表现, 例如相对的估测算、 本地化和光学流估测算 源 。</s>