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

✍ Scribed by Yu-Shen Liu; Jun-Hai Yong; Hui Zhang; Dong-Ming Yan; Jia-Guang Sun

Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
706 KB
Volume
38
Category
Article
ISSN
0010-4485

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✦ Synopsis

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.

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