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Mesh analysis using geodesic mean-shift

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Abstract

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.

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Authors and Affiliations

  1. The interdisciplinary center, Efi Arazi School of Computer Science, Israel

    Ariel Shamir

  2. School of Computer Science, Tel-Aviv University, Israel

    Lior Shapira & Daniel Cohen-Or

Authors
  1. Ariel Shamir
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  2. Lior Shapira
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  3. Daniel Cohen-Or
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Correspondence to Ariel Shamir.

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Shamir, A., Shapira, L. & Cohen-Or, D. Mesh analysis using geodesic mean-shift. Visual Comput 22, 99–108 (2006). https://doi.org/10.1007/s00371-006-0370-2

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