Adaptive Assignment for Geometry Aware Local Feature Matching
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Local image feature matching, aiming to identify and correspond similar regions from image pairs, is an essential concept in computer vision. Most existing image matching approaches follow a one-to-one assignment principle and employ mutual nearest neighbor to guarantee unique correspondence between local features across images. However, images from different conditions may hold large-scale variations or viewpoint diversification so that one-to-one assignment may cause ambiguous or missing representations in dense matching. In this paper, we introduce AdaMatcher, a novel detector-free local feature matching method, which first correlates dense features by a lightweight feature interaction module and estimates co-visible area of the paired images, then performs a patch-level many-to-one assignment to predict match proposals, and finally refines them based on a one-to-one refinement module. Extensive experiments show that AdaMatcher outperforms solid baselines and achieves state-of-the-art results on many downstream tasks. Additionally, the many-to-one assignment and one-to-one refinement module can be used as a refinement network for other matching methods, such as SuperGlue, to boost their performance further. Code will be available upon publication.
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