A general framework for progressive point-sampled geometry

查看全文 作　　者：LIU Yong-[1]jin;TANG [2]Kai;JONEJA [3]Ajay

机构地区：[1]Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;[2]Department of Mechanical Engineering, the Hong Kong University of Science and Technology, Hong Kong, China;[3]Department of Industrial Engineering and Logistic Management, the Hong Kong University of Science and Technology, Hong Kong, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》索引2006年第7卷第7期,共9页

摘　　要：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 pro- gressive 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. 关键词：顺序模型几何距离误差测量形状显示

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A general framework for progressive point-sampled geometry

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