MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence
Deep functional map frameworks (DFM) for shape correspondence are powerful, yet fundamentally limited by their reliance on end-to-end differentiability. This constraint prevents the integration of highly accurate, non-differentiable refinement techniques, capping their overall performance, especially on challenging non-isometric shapes. To overcome this, we introduce MDND, a novel DFM paradigm built on the principle of merging differentiable and non-differentiable components. Our framework facilitates unsupervised learning guided by an internal, non-differentiable refinement. Specifically, MDND employs a dual-branch architecture: a non-differentiable refinement branch leverages a novel, m...
| Authors | Qinsong Li, Jing Meng, Haibo Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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