1 citations · 1 across the 2 of their papers we have counts for
3 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…
cs.CG2021★ 1 cited
Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures
Shengjun Liu, Tao Liu, Qiang Zou +3
Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice struc…