  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 (\ie, one-to-one assignment) in patch-level matching.Accordingly, we
introduce 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 estimating 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. Additionally, 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.
