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cs.CV2026
Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching
Feifan Luo, Ting Li, Zhao Li +1
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, whi…
cs.CV2026
From Feature Learning to Spectral Basis Learning: A Unifying and Flexible Framework for Efficient and Robust Shape Matching
Feifan Luo, Hongyang Chen
Shape matching is a fundamental task in computer graphics and vision, with deep functional maps becoming a prominent paradigm. However, existing methods primarily focus on learning…
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…