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Paper

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

MPS-Authors
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Bernard,  Florian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Suri,  Zeeshan Khan
Computer Graphics, MPI for Informatics, Max Planck Society;

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Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:2002.12623.pdf
(Preprint), 11MB

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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0007-E00C-F
Abstract
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.