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arXiv 2002.126232020-02-28cs.CVcs.GRcs.LGmath.OC

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

MPI Informatics Saarland Informatics Campus（萨尔州大学信息学研究所（MPI Informatics）萨兰兹信息学园区（Saarland Informatics Campus））

Florian Bernard, Zeeshan Khan Suri, Christian Theobalt

备注：: to appear at CVPR

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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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