Coarse-to-fine Contrast: A Hybrid Self-supervised Method for Non-rigid 3D Shape Matching

Non-rigid 3D shape matching is a fundamental task in com-puter vision and graphics. In this paper, we propose a hybrid self-supervisedmethod based on a coarse-to-fine strategy, which ensures consistencybetween the coarse mapping and the refined correspondence producedby our refinement module. The architecture features a dual-branch de-sign, consisting of two symmetric functional map learning streams: onebased on the Laplacian basis and the other utilizing the elastic ba-sis. Extensive experiments show that our approach not only maintainscomputational efficiency, but also achieves state-of-the-art performanceacross a variety of challenging scenarios, including non-isometric defor-mations and topological noise. Finally, we rigorously demonstrate thatcontrastive energies promote feature discrimination. Furthermore, inte-grating these energies with existing methods yields consistent improve-ments, validating the overall efficacy of our approach. Our code is avail-able at https://github.com/LuoFeifan77/Coarse-to-Fine-Hybrid-Self-Supervised-Matching.