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Geodesic Shape Retrieval via Optimal Mass Transport

  • Julien Rabin
  • Gabriel Peyré
  • Laurent D. Cohen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

This paper presents a new method for 2-D and 3-D shape retrieval based on geodesic signatures. These signatures are high dimensional statistical distributions computed by extracting several features from the set of geodesic distance maps to each point. The resulting high dimensional distributions are matched to perform retrieval using a fast approximate Wasserstein metric. This allows to propose a unifying framework for the compact description of planar shapes and 3-D surfaces.

Keywords

Point Cloud Geodesic Distance Local Descriptor Global Descriptor Shape Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Julien Rabin
    • 1
  • Gabriel Peyré
    • 1
  • Laurent D. Cohen
    • 1
  1. 1.CEREMADEUniversité Paris-Dauphine 

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