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.
Originalsprache | Englisch |
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Seiten (von - bis) | 209-221 |
Seitenumfang | 13 |
Fachzeitschrift | Computer Graphics Forum |
Jahrgang | 36 |
Ausgabenummer | 1 |
DOIs | |
Publikationsstatus | Veröffentlicht - 1 Jan. 2017 |