Cosmo L, Rodola E, Albarelli A, Memoli F, Cremers D (2017)
Publication Type: Journal article
Publication year: 2017
Book Volume: 36
Pages Range: 209-221
Journal Issue: 1
DOI: 10.1111/cgf.12796
Recent efforts in the area of joint object matching approach the problem by taking as input a set of pairwise maps, which are then jointly optimized across the whole collection so that certain accuracy and consistency criteria are satisfied. One natural requirement is cycle-consistency—namely the fact that map composition should give the same result regardless of the path taken in the shape collection. In this paper, we introduce a novel approach to obtain consistent matches without requiring initial pairwise solutions to be given as input. We do so by optimizing a joint measure of metric distortion directly over the space of cycle-consistent maps; in order to allow for partially similar and extra-class shapes, we formulate the problem as a series of quadratic programs with sparsity-inducing constraints, making our technique a natural candidate for analysing collections with a large presence of outliers. The particular form of the problem allows us to leverage results and tools from the field of evolutionary game theory. This enables a highly efficient optimization procedure which assures accurate and provably consistent solutions in a matter of minutes in collections with hundreds of shapes.
APA:
Cosmo, L., Rodola, E., Albarelli, A., Memoli, F., & Cremers, D. (2017). Consistent Partial Matching of Shape Collections via Sparse Modeling. Computer Graphics Forum, 36(1), 209-221. https://dx.doi.org/10.1111/cgf.12796
MLA:
Cosmo, Luca, et al. "Consistent Partial Matching of Shape Collections via Sparse Modeling." Computer Graphics Forum 36.1 (2017): 209-221.
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