Mesh analysis using geodesic mean-shift

Ariel Shamir, Lior Shapira, Daniel Cohen-Or

Research output: Contribution to journalArticlepeer-review


In this paper, we introduce a versatile and robust method for analyzing the feature space associated with a given mesh surface. The method is based on the mean-shift operator, which was shown to be successful in image and video processing. Its strength lies in the fact that it works in a single joint space of geometry and attributes called the feature-space. The mean-shift procedure works as a gradient ascend finding maxima of an estimated probability density function in feature-space. Our method for using the mean-shift technique on surfaces solves several difficulties. First, meshes as opposed to images do not present a regular and uniform sampling of domain. Second, on surface meshes the shifting procedure must be constrained to stay on the surface and preserve geodesic distances. We define a special local geodesic parameterization scheme, and use it to generalize the mean-shift procedure to unstructured surface meshes. Our method can support piecewise linear attribute definitions as well as piecewise constant attributes.

Original languageEnglish
Pages (from-to)99-108
Number of pages10
JournalVisual Computer
Issue number2
StatePublished - Feb 2006


  • Feature extraction
  • Mean-shift
  • Meshes
  • Segmentation


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