PATS：用于局部特征匹配的带细分的块区域传输
PATS: Patch Area Transportation with Subdivision for Local Feature Matching
AI总结：
针对无检测器局部特征匹配在大尺度差异图像对上的不足，提出PATS，通过图像块细分与自监督块区域传输建模多对多尺度关系，提升匹配精度、覆盖率及下游任务性能。
摘要：
局部特征匹配旨在在一对图像之间建立稀疏对应关系。近来，无检测器方法通常表现出更好的性能，但在具有较大尺度差异的图像对上效果并不令人满意。本文中，我们提出带细分的块区域传输（Patch Area Transportation with Subdivision，PATS）来解决这一问题。我们没有构建代价高昂的图像金字塔，而是首先将原始图像对分割成等大小的图像块，并逐渐调整其大小，将它们细分为具有相同尺度的更小图像块。然而，估计这些图像块之间的尺度差异并非易事，因为尺度差异由相对相机位姿和场景结构共同决定，因此在图像对上会随空间变化。此外，真实场景中的真值也很难获得。为此，我们提出块区域传输，使模型能够以自监督方式学习尺度差异。与只能处理一对一匹配的二分图匹配不同，我们的块区域传输可以处理多对多关系。PATS提高了匹配准确率和覆盖率，并在相对位姿估计、视觉定位和光流估计等下游任务中表现出优越性能。源代码可在 https://zju3dv.github.io/pats/ 获取。
英文摘要：
Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free 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 is available at \url{https://zju3dv.github.io/pats/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
