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基于散乱点云加权邻域采样点的简化算法
引用本文:尹丹,杨林,王胜春. 基于散乱点云加权邻域采样点的简化算法[J]. 计算机与现代化, 2010, 0(6): 3-5. DOI: 10.3969/j.issn.1006-2475.2010.06.002
作者姓名:尹丹  杨林  王胜春
作者单位:湖南师范大学计算机教学部,湖南,长沙,410081
基金项目:湖南师范大学自然科学青年基金资助项目(60908)
摘    要:提出一种基于散乱点云的邻域采样点数目加权的聚类简化算法,此算法以曲面变化度和聚类中采样点的数目加权共同进行阈值控制,能够在简化过程中更偏向于将包含采样点数比较多且有一定曲率的聚类进行划分,得到更合理的简化效果。

关 键 词:散乱点云  曲面变化度  聚类  邻域

Simplification Algorithm with a Weight of Neighborhood Point Samples Based on Unorganized Point Cloud
YIN Dan,YANG Lin,WANG Sheng-chun. Simplification Algorithm with a Weight of Neighborhood Point Samples Based on Unorganized Point Cloud[J]. Computer and Modernization, 2010, 0(6): 3-5. DOI: 10.3969/j.issn.1006-2475.2010.06.002
Authors:YIN Dan  YANG Lin  WANG Sheng-chun
Affiliation:Department of Computer Education/a>;Hunan Normal University/a>;Changsha 410081/a>;China
Abstract:This paper proposes a clustering simplification algorithm,which has a weight of neighborhood point samples. This algorithm treats the surface variation and the quantity of point samples in the neighborhood as a threshold. So the user can control the threshold to be apt to split the clusters which have more point samples and a certain curvature,and get a more suitable simplification.
Keywords:unorganized point cloud  surface variation  clustering  neighborhood  
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