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  MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment

Bernard, F., Suri, Z. K., & Theobalt, C. (2020). MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment. Retrieved from https://arxiv.org/abs/2002.12623.

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Genre: Forschungspapier
Latex : MINA: {C}onvex Mixed-Integer Programming for Non-Rigid Shape Alignment

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arXiv:2002.12623.pdf (Preprint), 11MB
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 Urheber:
Bernard, Florian1, Autor           
Suri, Zeeshan Khan1, Autor           
Theobalt, Christian1, Autor           
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

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Schlagwörter: Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Graphics, cs.GR,Computer Science, Learning, cs.LG,Mathematics, Optimization and Control, math.OC
 Zusammenfassung: We present a convex mixed-integer programming formulation for non-rigid shape
matching. To this end, we propose a novel shape deformation model based on an
efficient low-dimensional discrete model, so that finding a globally optimal
solution is tractable in (most) practical cases. Our approach combines several
favourable properties: it is independent of the initialisation, it is much more
efficient to solve to global optimality compared to analogous quadratic
assignment problem formulations, and it is highly flexible in terms of the
variants of matching problems it can handle. Experimentally we demonstrate that
our approach outperforms existing methods for sparse shape matching, that it
can be used for initialising dense shape matching methods, and we showcase its
flexibility on several examples.

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Sprache(n): eng - English
 Datum: 2020-02-282020
 Publikationsstatus: Online veröffentlicht
 Seiten: 14 p.
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: arXiv: 2002.12623
BibTex Citekey: Bernard_arXiv2002.12623
URI: https://arxiv.org/abs/2002.12623
 Art des Abschluß: -

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