2 papers
cs.CV2026
MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence
Qinsong Li, Jing Meng, Haibo Wang +1
Deep functional map frameworks (DFM) for shape correspondence are powerful, yet fundamentally limited by their reliance on end-to-end differentiability. This constraint prevents th…
cs.CV2024
Deep Frequency-Aware Functional Maps for Robust Shape Matching
Feifan Luo, Qinsong Li, Ling Hu +4
Deep functional map frameworks are widely employed for 3D shape matching. However, most existing deep functional map methods cannot adaptively capture important frequency informati…