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Differentials-Based Segmentation and Parameterization for Point-Sampled Surfaces

ISSN号：1000-9000
期刊名称：《计算机科学技术学报：英文版》
时间：0
分类：TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]State Key Laboratory of CADiz CG, Zhejiang University, Hangzhou 310027, China, [2]College of Science, Zhejiang University of Technology, Hangzhou 310032, China, [3]School of Computing Sciences, University of East Anglia, Norwich, NR4 7T J, U.K.
相关基金：This work is supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2002CB312101, the National Natural Science Foundation of China （NSFC） under Grant Nos. 60503056, 60333010, and the Natural Science Foundation of Zhejiang Province under Grant No. R106449.

作者：缪永伟[1,2], 冯结青[1], 肖青霞[1], 彭群生[1], A.R. Forrest[3]

关键词：计算机图形学, 点样本表面, 分割, 数字几何处理, computer graphics, point-sampled surface, segmentation, parameterization, k-means clustering, multidi- mensional scaling

中文摘要：

<正>Efficient parameterization of point-sampled surfaces is a flmdamental problem in the field of digital geometry processing.In order to parameterize a given point-sampled surface for minimal distance distortion,a differentials-based segmentation and parameterization approach is proposed in this paper.Our approach partitions the point-sampled geometry based on two criteria:variation of Euclidean distance between sample points,and angular difference between surface differential directions.According to the analysis of normal curvatures for some specified directions,a new projection approach is adopted to estimate the local surface differentials.Then a k-means clustering(k-MC)algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes.Finally,each chart is parameterized with a statistical method-multidimensional scaling(MDS)approach,and the parameterization results of all charts form an atlas for compact storage.更多还原

英文摘要：

Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a differentialslbased segmentation and parameterization approach is proposed in this paper. Our approach partitions the point-sampled geometry based on two criteria： variation of Euclidean distance between sample points, and angular difference between surface differential directions. According to the analysis of normal curvatures for some specified directions, a new projection approach is adopted to estimate the local surface differentials. Then a k-means clustering （k-MC） algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes. Finally, each chart is parameterized with a statistical method -- multidimensional scaling （MDS） approach, and the parameterization results of all charts form an atlas for compact storage.

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