@techreport{TR-IC-07-17,
  number = {IC-07-17},
  author = {Fernando de Goes and
            Siome Goldenstein and Luiz Velho},
  title = {Intrinsic Mesh Segmentation},
  month = {May},
  year = {2007}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 13 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Mesh segmentation offers a desirable divide-and-conquer
       strategy for many graphics applications. In this paper, we
       present a novel, efficient, and intrinsic method to segment
       meshes following the minima rule. The eigenfunctions of the
       Laplace-Beltrami operator define locality and volume-shape
       preserving functions over a surface. Inspired on Manifold
       learning theory, we use these functions as the basis of an
       embedding space for mesh vertices and group them using
       $k$-means clustering. We also present a new kind of
       segmentation hierarchy built from the analysis of the
       Laplace-Beltrami operator spectrum.
  }
}