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Isometric multi-shape matching
Technical University of Munich, Munich, Germany.
Technical University of Munich, Munich, Germany; University of Siegen, Siegen, Germany.ORCID iD: 0000-0003-0599-094X
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-9738-4148
Technical University of Munich, Munich, Germany.
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2021 (English)In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC: IEEE, 2021, p. 14178-14188Conference paper, Published paper (Refereed)
Abstract [en]

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Our algorithm obtains multi-matchings that are by construction provably cycle-consistent. We demonstrate the superior performance of our method on various datasets and set the new state-ofthe-art in isometric multi-shape matching. © 2021 IEEE

Place, publisher, year, edition, pages
Washington, DC: IEEE, 2021. p. 14178-14188
Series
IEEE Conference on Computer Vision and Pattern Recognition. Proceedings, ISSN 1063-6919
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:hh:diva-50065DOI: 10.1109/CVPR46437.2021.01396ISI: 000742075004039Scopus ID: 2-s2.0-85117637730ISBN: 9781665445092 (print)OAI: oai:DiVA.org:hh-50065DiVA, id: diva2:1741824
Conference
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, 19-25 June 2021
Funder
Swedish Research Council, 2019-04769
Note

Funding text: The authors gracefully acknowledge the support from the ERC Advanced Grant SIMULACRON, the Munich Center for Machine Learning and the Swedish Research Council (2019-04769).

Available from: 2023-03-07 Created: 2023-03-07 Last updated: 2023-03-07Bibliographically approved

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Thunberg, Johan

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