Current feature matching methods focus on point-level matching, pursuing
better representation learning of individual features, but lacking further
understanding of the scene. This results in significant performance degradation
when handling challenging scenes such as scenes with large viewpoint and
illumination changes. To tackle this problem, we propose a novel model named
SAM, which applies attentional grouping to guide Scene-Aware feature Matching.
SAM handles multi-level features, i.e., image tokens and group tokens, with
attention layers, and groups the image tokens with the proposed token grouping
module. Our model can be trained by ground-truth matches only and produce
reasonable grouping results. With the sense-aware grouping guidance, SAM is not
only more accurate and robust but also more interpretable than conventional
feature matching models. Sufficient experiments on various applications,
including homography estimation, pose estimation, and image matching,
demonstrate that our model achieves state-of-the-art performance.

该研究提出了一种名为 SAM 的新模型，应用注意力分组指导场景感知特征匹配，处理具有大视角和光照变化等挑战性场景。通过引入感知感觉的分组指导，该模型不仅更准确和稳健，而且更易解释，实现了最先进的性能。