Adaptive Assignment for Geometry Aware Local Feature Matching
Dihe Huang, Ying Chen, Yong Liu, Jianlin Liu, Shang Xu, Wenlong Wu, Yikang Ding, Fan Tang, Chengjie Wang
Abstract
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geometric inconsistency resulting from the application of the mutual nearest neighbour criterion (i.e., one-to-one assignment) in patch-level matching. Accordingly, we in-troduce AdaMatcher, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while es-timating the scales between images, and finally refines the co-visible matches through scale alignment and sub-pixel regression module. Extensive experiments show that AdaMatcher outperforms solid baselines and achieves state-of-the-art results on many downstream tasks. Ad-ditionally, the adaptive assignment and sub-pixel refinement module can be used as a refinement network for other matching methods, such as SuperGlue, to boost their performance further. The code will be publicly available at https://github.com/AbyssGaze/AdaMatcher.