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pro vyhledávání: '"Roetzer A"'
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
In this work we propose to combine the advantages of learning-based and combinatorial formalisms for 3D shape matching. While learning-based shape matching solutions lead to state-of-the-art matching performance, they do not ensure geometric consiste
Externí odkaz:
http://arxiv.org/abs/2310.08230
Akademický článek
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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
Autor:
Ehm, Viktoria, Roetzer, Paul, Eisenberger, Marvin, Gao, Maolin, Bernard, Florian, Cremers, Daniel
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 texture transfer. An often neglected but essential property of matchin
Externí odkaz:
http://arxiv.org/abs/2309.05013
Autor:
Gao, Maolin, Roetzer, Paul, Eisenberger, Marvin, Lähner, Zorah, Moeller, Michael, Cremers, Daniel, Bernard, Florian
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 Laplace-Beltrami operator (PLBO) which combines intrinsic and extrinsic ge
Externí odkaz:
http://arxiv.org/abs/2308.08393
Autor:
Roetzer, James1 (AUTHOR) jroetzer@charlotte.edu, Li, Xingjie2 (AUTHOR) xli47@charlotte.edu, Hall, John1 (AUTHOR) john.hall@charlotte.edu
Publikováno v:
Energies (19961073). Aug2024, Vol. 17 Issue 16, p3897. 20p.
Autor:
Hanks, Patrick, Lenarčič, Simon
Publikováno v:
Dictionary of American Family Names, 2 ed., 2022.
Publikováno v:
ACM Transactions on Graphics 2023
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 deep functional map methods mainly focus on predicting optimised function
Externí odkaz:
http://arxiv.org/abs/2304.14419
Autor:
Lazen, Philipp, Cardoso, Pedro Lima, Sharma, Sukrit, Cadrien, Cornelius, Roetzer-Pejrimovsky, Thomas, Furtner, Julia, Strasser, Bernhard, Hingerl, Lukas, Lipka, Alexandra, Preusser, Matthias, Marik, Wolfgang, Bogner, Wolfgang, Widhalm, Georg, Rössler, Karl, Trattnig, Siegfried, Hangel, Gilbert
Publikováno v:
Cancers 2024, 16(5), 943
This paper investigates the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from
Externí odkaz:
http://arxiv.org/abs/2304.05254