A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching

Roetzer P, Swoboda P, Cremers D, Bernard F (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 428-438

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: New Orleans, LA, USA

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.00052

Abstract

We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant formalism proposed by Windheuser et al. [66] where 3D shape matching was formulated as an integer linear program over the space of orientation-preserving diffeomorphisms. Until now, the resulting formulation had limited practical applicability due to its complicated constraint structure and its large size. We propose a novel primal heuristic coupled with a Lagrange dual problem that is several orders of magnitudes faster compared to previous solvers. This allows us to handle shapes with substantially more triangles than previously solvable. We demonstrate compelling results on diverse datasets, and, even showcase that we can address the challenging setting of matching two partial shapes without availability of complete shapes. Our code is publicly available at http://github.com/paulOnoah/sm-comb.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Max Planck Institute for Informatics / Max-Planck-Institut für Informatik DE Germany (DE) Rheinische Friedrich-Wilhelms-Universität Bonn DE Germany (DE)

How to cite

APA:

Roetzer, P., Swoboda, P., Cremers, D., & Bernard, F. (2022). A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 428-438). New Orleans, LA, USA: IEEE Computer Society.

MLA:

Roetzer, Paul, et al. "A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 428-438.

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