# Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching

arXiv: https://arxiv.org/abs/2606.26557 · HTML: https://mlsanity.com/paper/2606.26557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 88 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [FUSE: A Flow-based Mapping Between Shapes](https://mlsanity.com/paper/2511.13431/) (2025)

## Abstract (arXiv)

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.