Journal of Zhejiang University-SCIENCE A

, Volume 7, Issue 7, pp 1201–1209 | Cite as

A general framework for progressive point-sampled geometry

  • Liu Young-jin 
  • Tang Kai 
  • Joneja Ajay 


Recently unstructured dense point sets have become a new representation of geometric shapes. In this paper we introduce a novel framework within which several usable error metrics are analyzed and the most basic properties of the progressive point-sampled geometry are characterized. Another distinct feature of the proposed framework is its compatibility with most previously proposed surface inference engines. Given the proposed framework, the performances of four representative well-reputed engines are studied and compared.

Key words

Progressive model Point-sample geometry Geometric distance Error measure Shape representation 

CLC number



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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Liu Young-jin 
    • 1
  • Tang Kai 
    • 2
  • Joneja Ajay 
    • 3
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Department of Mechanical Engineeringthe Hong Kong University of Science and TechnologyHong KongChina
  3. 3.Department of Industrial Engineering and Logistic Managementthe Hong Kong University of Science and TechnologyHong KongChina

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