Adaptive Spot-Guided Transformer for Consistent Local Feature Matching
This work improves local feature matching for computer vision applications, but it appears incremental as it builds on existing detector-free Transformer methods.
The paper tackles the problem of local feature matching in images by addressing local consistency and scale variations, proposing an Adaptive Spot-Guided Transformer (ASTR) that achieves favorable performance against state-of-the-art methods on five standard benchmarks.
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.