A quasi-Monte Carlo method for computing areas of point-sampled surfaces

  • Authors:
  • Yu-Shen Liu;Jun-Hai Yong;Hui Zhang;Dong-Ming Yan;Jia-Guang Sun

  • Affiliations:
  • School of Software, Tsinghua University, Beijing 100084, People's Republic of China and Department of Computer Science and Technology, Tsinghua University, Beijing 100084, People's Republic of Chi ...;School of Software, Tsinghua University, Beijing 100084, People's Republic of China;School of Software, Tsinghua University, Beijing 100084, People's Republic of China;The University of Hong Kong, Hong Kong, China;School of Software, Tsinghua University, Beijing 100084, People's Republic of China and Department of Computer Science and Technology, Tsinghua University, Beijing 100084, People's Republic of Chi ...

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2006

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Abstract

A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy-Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.