Correspondence matching plays a crucial role in numerous robotics
applications. In comparison to conventional hand-crafted methods and recent
data-driven approaches, there is significant interest in plug-and-play
algorithms that make full use of pre-trained backbone networks for multi-scale
feature extraction and leverage hierarchical refinement strategies to generate
matched correspondences. The primary focus of this paper is to address the
limitations of deep feature matching (DFM), a state-of-the-art (SoTA)
plug-and-play correspondence matching approach. First, we eliminate the
pre-defined threshold employed in the hierarchical refinement process of DFM by
leveraging a more flexible nearest neighbor search strategy, thereby preventing
the exclusion of repetitive yet valid matches during the early stages. Our
second technical contribution is the integration of a patch descriptor, which
extends the applicability of DFM to accommodate a wide range of backbone
networks pre-trained across diverse computer vision tasks, including image
classification, semantic segmentation, and stereo matching. Taking into account
the practical applicability of our method in real-world robotics applications,
we also propose a novel patch descriptor distillation strategy to further
reduce the computational complexity of correspondence matching. Extensive
experiments conducted on three public datasets demonstrate the superior
performance of our proposed method. Specifically, it achieves an overall
performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with
respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches
dataset, outperforming all other SoTA algorithms. Our source code, demo video,
and supplement are publicly available at mias.group/GCM.

本文的主要关注点是解决深度特征匹配（DFM）的局限性，通过采用更灵活的最近邻搜索策略并集成图像补丁描述符，扩展了 DFM 的适用范围，同时提出了一种新颖的图像补丁描述符精炼策略，进一步降低了对应匹配的计算复杂度，实验证明我们的方法在实际机器人应用中具有卓越的性能。