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Support Vector Machines for 3D Shape Processing




We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SVMachine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.

Author(s): Steinke, F. and Schölkopf, B. and Blanz, V.
Journal: Computer Graphics Forum
Volume: 24
Number (issue): 3, EUROGRAPHICS 2005
Pages: 285-294
Year: 2005
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Support Vector Machines for 3D Shape Processing},
  author = {Steinke, F. and Sch{\"o}lkopf, B. and Blanz, V.},
  journal = {Computer Graphics Forum},
  volume = {24},
  number = {3, EUROGRAPHICS 2005},
  pages = {285-294},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2005},
  month_numeric = {9}