CVPR 2023 - Adaptive Spot-Guided Transformer for Consistent Local Feature Matching

In this episode we discuss Adaptive Spot-Guided Transformer for Consistent Local Feature Matching by Jiahuan Yu, Jiahao Chang, Jianfeng He, Tianzhu Zhang, Feng Wu. The paper proposes Adaptive Spot-Guided Transformer (ASTR), a new approach for local feature matching that models both local consistency and scale variations in a coarse-to-fine architecture. ASTR uses a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation and an adaptive scaling module to adjust the size of grids according to depth information. The method outperforms state-of-the-art approaches on five standard benchmarks. Code for ASTR will be released on https://astr2023.github.io.