no code implementations • 17 Oct 2023 • Dongliang Cao, Paul Roetzer, Florian Bernard
To this end, we propose a self-adaptive functional map solver to adjust the functional map regularisation for different shape matching scenarios, together with a vertex-wise contrastive loss to obtain more discriminative features.
1 code implementation • 12 Oct 2023 • Paul Roetzer, Ahmed Abbas, Dongliang Cao, Florian Bernard, Paul Swoboda
In this work we propose to combine the advantages of learning-based and combinatorial formalisms for 3D shape matching.
no code implementations • 10 Sep 2023 • Viktoria Ehm, Paul Roetzer, Marvin Eisenberger, Maolin Gao, Florian Bernard, Daniel Cremers
Moreover, while in practice one often has only access to partial observations of a 3D shape (e. g. due to occlusion, or scanning artifacts), there do not exist any methods that directly address geometrically consistent partial shape matching.
no code implementations • ICCV 2023 • Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Moeller, Daniel Cremers, Florian Bernard
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes.
1 code implementation • 27 Apr 2023 • Dongliang Cao, Paul Roetzer, Florian Bernard
In contrast, building upon recent insights about the relation between functional maps and point-wise maps, we propose a novel unsupervised loss to couple the functional maps and point-wise maps, and thereby directly obtain point-wise maps without any post-processing.
1 code implementation • CVPR 2023 • 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.
1 code implementation • CVPR 2022 • Paul Roetzer, Paul Swoboda, Daniel Cremers, Florian Bernard
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes.
no code implementations • 4 Feb 2022 • Paul Swoboda, Bjoern Andres, Andrea Hornakova, Florian Bernard, Jannik Irmai, Paul Roetzer, Bogdan Savchynskyy, David Stein, Ahmed Abbas
In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes.