Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods. Our code will be released on https://astr2023.github.io.

本文提出了一种自适应的、采用了诸多优点的区域引导 Transformer 网络(ASTR)来解决图像的局部特征匹配问题，同时考虑了局部一致性和尺度变化的因素，实验结果证明 ASTR 在五个标准基准数据集上的表现超越了现有最先进的方法。

自适应点引导变形注意力网络用于本地特征一致匹配