Zobrazeno 1 - 10
of 345
pro vyhledávání: '"Bernard Florian"'
Autor:
Ehm, Viktoria, Amrani, Nafie El, Xie, Yizheng, Bastian, Lennart, Gao, Maolin, Wang, Weikang, Sang, Lu, Cao, Dongliang, Lähner, Zorah, Cremers, Daniel, Bernard, Florian
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. While approaches based on machine learning dominate modern 3D shape matching, almost all existing (learning-based) methods re
Externí odkaz:
http://arxiv.org/abs/2411.03511
Most recent unsupervised non-rigid 3D shape matching methods are based on the functional map framework due to its efficiency and superior performance. Nevertheless, respective methods struggle to obtain spatially smooth pointwise correspondences due
Externí odkaz:
http://arxiv.org/abs/2407.08244
We consider the incomplete multi-graph matching problem, which is a generalization of the NP-hard quadratic assignment problem for matching multiple finite sets. Multi-graph matching plays a central role in computer vision, e.g., for matching images
Externí odkaz:
http://arxiv.org/abs/2406.18215
In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce c
Externí odkaz:
http://arxiv.org/abs/2405.17897
Autor:
Ehm, Viktoria, Gao, Maolin, Roetzer, Paul, Eisenberger, Marvin, Cremers, Daniel, Bernard, Florian
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 matching settings, which occur when the shapes to match are only observed
Externí odkaz:
http://arxiv.org/abs/2404.12209
Autor:
Alkhatib, Farah, Jamshidian, Mostafa, Liepvre, Donatien Le, Bernard, Florian, Minvielle, Ludovic, Fondanèche, Antoine, Gizewski, Elke, Gassner, Eva, Loizides, Alexander, Lutz, Maximilian, Enzmann, Florian, Mufty, Hozan, Fourneau, Inge, Wittek, Adam, Miller, Karol
In this study, we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtain
Externí odkaz:
http://arxiv.org/abs/2403.07238
Although 3D shape matching and interpolation are highly interrelated, they are often studied separately and applied sequentially to relate different 3D shapes, thus resulting in sub-optimal performance. In this work we present a unified framework to
Externí odkaz:
http://arxiv.org/abs/2402.18920
Autor:
Thunberg, Johan, Bernard, Florian
We propose a novel non-negative spherical relaxation for optimization problems over binary matrices with injectivity constraints, which in particular has applications in multi-matching and clustering. We relax respective binary matrix constraints to
Externí odkaz:
http://arxiv.org/abs/2310.13311
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 to a broad range of different challenging scenarios. Previous deep fu
Externí odkaz:
http://arxiv.org/abs/2310.11420
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 performance, they do not ensure geometric consistency, so that obtain
Externí odkaz:
http://arxiv.org/abs/2310.08230