Consistent Partial Matching of Shape Collections via Sparse Modeling

L. Cosmo, E. Rodolà, A. Albarelli, F. Mémoli, D. Cremers

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

41 Zitate (Scopus)

Abstract

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.

OriginalspracheEnglisch
Seiten (von - bis)209-221
Seitenumfang13
FachzeitschriftComputer Graphics Forum
Jahrgang36
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - 1 Jan. 2017

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