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Computational Aspects of the Gromov–Hausdorff Distance and its Application in Non-rigid Shape Matching

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Abstract

The Gromov–Hausdorff distance of two compact metric spaces is a measure for how far the spaces are from being isometric and has been extensively studied in the field of metric geometry. In recent years, a lot of attention has been devoted to computational aspects of the Gromov–Hausdorff distance. One of the most prominent applications is the problem of shape matching, where the goal is to decide whether two shapes given as polygonal meshes are equivalent under certain invariances. Therefore, many methods have been proposed which heuristically estimate the Gromov–Hausdorff distance of metric spaces induced by the shapes. However, the computational complexity of computing the Gromov–Hausdorff distance has not yet been thoroughly investigated. We show that—under standard complexity theoretic assumptions—determining the Gromov–Hausdorff distance of two finite metric spaces cannot be approximated within any reasonable bound in polynomial time. Furthermore, we discover attributes of metric spaces which have a major impact on the complexity of an instance. This enables us to develop an approximation algorithm which is fixed parameter tractable with respect to corresponding parameters.

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Acknowledgements

We would like to express our gratitude to the anonymous reviewers for their many helpful suggestions and comments.

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  1. Zentrum Mathematik, Technische Universität München, München, Germany

    Felix Schmiedl

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  1. Felix Schmiedl
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Schmiedl, F. Computational Aspects of the Gromov–Hausdorff Distance and its Application in Non-rigid Shape Matching. Discrete Comput Geom 57, 854–880 (2017). https://doi.org/10.1007/s00454-017-9889-4

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