论文标题
几何学意识到本地功能匹配的自适应分配
Adaptive Assignment for Geometry Aware Local Feature Matching
论文作者
论文摘要
由于其出色的表现，无探测器的功能匹配方法目前引起了极大的关注。 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在估计图像之间的尺度的同时，通过尺度对齐和子像素回归模块进行了贴片级匹配。扩展的实验表明，Adamatcher的表现优于固体基线，并在许多下游任务上实现最先进的基线结果。此外，自适应分配和子像素细化模块可以用作其他匹配方法（例如Superglue）的改进网络，以进一步提高其性能。该代码将在https://github.com/abyssgaze/adamatcher上公开获取。
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.
