A Genetic Algorithm for Fully Automatic Non-Isometric Shape Matching
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Automatically computing shape correspondence is a difficult problem, especially when the shapes are significantly different. In this paper we suggest a fully automatic method for shape correspondence, that is suitable for non isometric shapes and shapes of different topology. We tackle the combinatorial task of putting in correspondence two sparse sets of landmarks using a genetic algorithm. Our main observation is that optimizing an objective based on an induced dense functional correspondence, combined with geometric genetic operators, is highly effective for non isometric shape matching. The output of the genetic algorithm is a sparse landmark correspondence, as well as a corresponding functional map. Finally, an accurate pointwise map is extracted using existing semi-automatic methods. Our method is general, widely applicable, and outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively.
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