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Consistent Spherical Parameterization

  • Arul Asirvatham
  • Emil Praun
  • Hugues Hoppe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)

Abstract

Many applications benefit from surface parameterization, including texture mapping, morphing, remeshing, compression, object recognition, and detail transfer, because processing is easier on the domain than on the original irregular mesh. We present a method for simultaneously parameterizing several genus-0 meshes possibly with boundaries onto a common spherical domain, while ensuring that corresponding user-highlighted features on each of the meshes map to the same domain locations. We obtain visually smooth parameterizations without any cuts, and the constraints enable us to directly associate semantically important features such as animal limbs or facial detail. Our method is robust and works well with either sparse or dense sets of constraints.

Keywords

Feature Point Simplicial Complex Planar Parameterization Texture Mapping Geometry Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Arul Asirvatham
    • 1
  • Emil Praun
    • 1
  • Hugues Hoppe
    • 2
  1. 1.School of ComputingUniversity of UtahUSA
  2. 2.Microsoft ResearchRedmondUSA

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