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A Reflective Symmetry Descriptor

  • Michael Kazhdan
  • Bernard Chazelle
  • David Dobkin
  • Adam Finkelstein
  • Thomas Funkhouser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vision and computational geometry. Most prior work has focused on finding the main axes of symmetry, or determining that none exists. In this paper, we introduce a new reflective symmetry descriptor that represents a measure of reflective symmetry for an arbitrary 3D voxel model for all planes through the model’s center of mass (even if they are not planes of symmetry). The main benefits of this new shape descriptor are that it is defined over a canonical parameterization (the sphere) and describes global properties of a 3D shape. Using Fourier methods, our algorithm computes the symmetry descriptor in O(N 4 log N) time for an N × N × N voxel grid, and computes a multiresolution approximation in O(N 3 log N) time. In our initial experiments, we have found the symmetry descriptor to be useful for registration, matching, and classification of shapes.

Keywords

Shape Descriptor North Pole Voxel Model Symmetry Detection Voxel Grid 
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 2002

Authors and Affiliations

  • Michael Kazhdan
    • 1
  • Bernard Chazelle
    • 1
  • David Dobkin
    • 1
  • Adam Finkelstein
    • 1
  • Thomas Funkhouser
    • 1
  1. 1.Princeton UniversityPrincetonUSA

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