  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.
