A reflective symmetry descriptor

Michael Kazhdan, Bernard Chazelle, David Dobkin, Adam Finkelstein, Thomas Funkhouser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

93 Scopus citations


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(N4 logN) time for an N × N × N voxel grid, and computes a multiresolution approximation in O(N3 logN) time. In our initial experiments, we have found the symmetry descriptor to be useful for registration, matching, and classification of shapes.

Original languageEnglish (US)
Title of host publicationComputer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings
EditorsAnders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783540437444
StatePublished - 2002
Event7th European Conference on Computer Vision, ECCV 2002 - Copenhagen, Denmark
Duration: May 28 2002May 31 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th European Conference on Computer Vision, ECCV 2002

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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