1 citations · 1 across the 5 of their papers we have counts for
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An Integer Linear Programming Approach to Geometrically Consistent Partial-Partial Shape Matching
Viktoria Ehm, Paul Roetzer, Florian Bernard +1
The task of establishing correspondences between two 3D shapes is a long-standing challenge in computer vision. While numerous studies address full-full and partial-full 3D shape m…
Symmetry Informative and Agnostic Feature Disentanglement for 3D Shapes
Tobias Weißberg, Weikang Wang, Paul Roetzer +2
Shape descriptors, i.e., per-vertex features of 3D meshes or point clouds, are fundamental to shape analysis. Historically, various handcrafted geometry-aware descriptors and featu…
Partial-to-Partial Shape Matching with Geometric Consistency
Viktoria Ehm, Maolin Gao, Paul Roetzer +3
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. A prominent challenge are partial-to-partial shape matc…
Revisiting Map Relations for Unsupervised Non-Rigid Shape Matching
Dongliang Cao, Paul Roetzer, Florian Bernard
We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functional map methods and can be applied…
DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
Paul Roetzer, Ahmed Abbas, Dongliang Cao +2
In this work we propose to combine the advantages of learningbased and combinatorial formalisms for 3D shape matching. While learningbased methods lead to state-of-the-art matching…
Geometrically Consistent Partial Shape Matching
Viktoria Ehm, Paul Roetzer, Marvin Eisenberger +3
Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics, which is for example relevant for tasks like shape interpolation, pose transfer, or…