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| A general framework for progressive point-sampled geometry |
| LIU Yong-jin, TANG Kai, JONEJA Ajay |
| Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Department of Mechanical Engineering, the Hong Kong University of Science and Technology, Hong Kong, China; Department of Industrial Engineering and Logistic Management, the Hong Kong University of Science and Technology, Hong Kong, China |
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Abstract 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 progressive 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.
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Received: 17 April 2006
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