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

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.
Type de document :
Article dans une revue
Computer-Aided Design, Elsevier, 2006
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

Contributeur : Thss Tsinghua <>
Soumis le : vendredi 17 septembre 2010 - 05:03:08
Dernière modification le : vendredi 17 septembre 2010 - 07:55:28
Document(s) archivé(s) le : samedi 18 décembre 2010 - 02:48:23


Fichiers produits par l'(les) auteur(s)


  • HAL Id : inria-00518328, version 1


Yu-Shen Liu, Jun-Hai Yong, Hui Zhang, Dong-Ming Yan, Jia-Guang Sun. A quasi-Monte Carlo method for computing areas of point-sampled surfaces. Computer-Aided Design, Elsevier, 2006. 〈inria-00518328〉



Consultations de la notice


Téléchargements de fichiers