Erosion thickness on medial axes of 3D shapes

Yajie Yan, Kyle Sykes, Erin Chambers, David Letscher, Tao Ju

Research output: Contribution to journalConference articlepeer-review

43 Scopus citations

Abstract

While playing a fundamental role in shape understanding, the medial axis is known to be sensitive to small boundary perturbations. Methods for pruning the medial axis are usually guided by some measure of significance. The majority of significance measures over the medial axes of 3D shapes are locally defined and hence unable to capture the scale of features. We introduce a global significance measure that generalizes in 3D the classical Erosion Thickness (ET) measure over the medial axes of 2D shapes. We give precise definition of ET in 3D, analyze its properties, and present an efficient approximation algorithm with bounded error on a piecewise linear medial axis. Experiments showed that ET outperforms local measures in differentiating small boundary noise from prominent shape features, and it is significantly faster to compute than existing global measures. We demonstrate the utility of ET in extracting clean, shape-revealing and topology-preserving skeletons of 3D shapes.

Original languageEnglish
Article numbera38
JournalACM Transactions on Graphics
Volume35
Issue number4
DOIs
StatePublished - Jul 11 2016
EventACM SIGGRAPH 2016 - Anaheim, United States
Duration: Jul 24 2016Jul 28 2016

Keywords

  • Medial axis
  • Shape analysis
  • Skeletons

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