171 citations · 535 across the 42 of their papers we have counts for
4 papers · 2 filters
SIGMA: Scale-Invariant Global Sparse Shape Matching
Maolin Gao, Paul Roetzer, Marvin Eisenberger +4
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected Lapl…
Non-Separable Multi-Dimensional Network Flows for Visual Computing
Viktoria Ehm, Daniel Cremers, Florian Bernard
Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to…
Unsupervised Learning of Robust Spectral Shape Matching
Dongliang Cao, Paul Roetzer, Florian Bernard
We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous dee…
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching
Dongliang Cao, Florian Bernard
The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common…