提出粗到精混合自监督方法，提升非刚性3D形状匹配精度与稳定性。
Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching
- 采用双分支架构，结合拉普拉斯与弹性基函数进行功能映射学习。
- 在非等距变形和拓扑噪声下均达当前最优性能，计算效率高。
- 对比能量有效增强特征区分性，可通用提升现有方法效果。
非刚性3D形状匹配是计算机视觉与图形学中的基础任务。本文提出一种基于粗到精策略的混合自监督方法，确保粗略映射与细化模块生成的对应关系一致。该架构采用双分支设计，包含两个对称的功能映射学习流：一个基于拉普拉斯基，另一个利用弹性基。大量实验表明，该方法不仅保持计算高效，且在多种挑战性场景下（包括非等距变形和拓扑噪声）均达到当前最优性能。进一步严格证明了对比能量能促进特征区分性。将这些能量集成至现有方法中，均带来一致提升，验证了方法的整体有效性。代码已公开于 https://github.com/LuoFeifan77/Coarse-to-Fine-Hybrid-Self-Supervised-Matching。
原文摘要 · Abstract (English)
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.
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