42 citations · 49 across the 13 of their papers we have counts for
13 papers
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Aleksei Zhuravlev, Lennart Bastian, Dongliang Cao +12
Estimating correspondences between deformed shape instances is a long-standing problem in computer graphics; numerous applications, from texture transfer to statistical modelling,…
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…
Fast Globally Optimal and Geometrically Consistent 3D Shape Matching
Paul Roetzer, Florian Bernard
Geometric consistency, i.e. the preservation of neighbourhoods, is a natural and strong prior in 3D shape matching. Geometrically consistent matchings are crucial for many downstre…
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…
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…
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…
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching
Paul Roetzer, Zorah Lähner, Florian Bernard
We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a short…
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
Paul Roetzer, Paul Swoboda, Daniel Cremers +1
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant forma…
Structured Prediction Problem Archive
Paul Swoboda, Bjoern Andres, Andrea Hornakova +6
Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one pl…