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An Efficient Implementation of RBF-Based Progressive Point-Sampled Geometry

  • Yong-Jin Liu
  • Kai Tang
  • Joneja Ajay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)

Abstract

In this paper we address practical implementation issues of generating a progressive representation of point-sampled geometry. Fast and stable algorithms are proposed to efficiently implement a progressive version of the modified RBF Shepard’s method. Comparisons with several well-known methods are presented, showing that the proposed algorithms can achieve good balance between geometric error reduction and time efficiency.

Keywords

Radial Basis Function Geometric Error Progressive Representation Move Little Square Range Tree 
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 2006

Authors and Affiliations

  • Yong-Jin Liu
    • 1
  • Kai Tang
    • 2
  • Joneja Ajay
    • 2
  1. 1.Tsinghua UniversityBeijingP.R. China
  2. 2.The Hong Kong University of Science and TechnologyHong Kong

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