Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching
It addresses the problem of non-rigid 3D shape matching for computer vision and graphics applications, offering a method that is both efficient and robust to difficult deformations.
The paper proposes a hybrid self-supervised method for non-rigid 3D shape matching using a coarse-to-fine strategy with dual-branch functional map learning, achieving state-of-the-art performance on challenging scenarios including non-isometric deformations and topological noise.
Non-rigid 3D shape matching is a fundamental task in computer vision and graphics. In this paper, we propose a hybrid self-supervised method based on a coarse-to-fine strategy, which ensures consistency between the coarse mapping and the refined correspondence produced by our refinement module. The architecture features a dual-branch design, consisting of two symmetric functional map learning streams: one based on the Laplacian basis and the other utilizing the elastic basis. Extensive experiments show that our approach not only maintains computational efficiency, but also achieves state-of-the-art performance across a variety of challenging scenarios, including non-isometric deformations and topological noise. Finally, we rigorously demonstrate that contrastive energies promote feature discrimination. Furthermore, integrating these energies with existing methods yields consistent improvements, validating the overall efficacy of our approach. Our code is available at https://github.com/LuoFeifan77/Coarse-to-Fine-Hybrid-Self-Supervised-Matching.