期刊文献+

基于聚类平面特征的三维点云数据精简算法 被引量:16

Reduction Algorithm for 3D Scattered Points Cloud Data Based on Clustering Plane Feature
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摘要 根据建筑物在高度方向截面上的点云数据必定位于其轮廓线的原理,提出基于聚类平面特征的点云数据精简算法。该算法无需对扫描对象进行表面重构,而是在保持建筑物高度方向数据精度的前提下,对点云数据分层聚类简化,保留满足条件的特征点,删除其余的点。通过实例证明该算法可以在保持建筑物外形特征的同时,达到较高的精简比率。 According to the theory that the scattered point cloud data of buildings is certainly located on their contour line,this paper proposes a data reduction algorithm of clustering plane feature so that the scattered point cloud data of large group of ancient buildings can achieve a higher reduction ratio on the basis of maintaining the shape feature.Appling it to reconstruct the Small Wild Goose Pagoda with point cloud techniques achieves good modeling results.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第10期249-251,254,共4页 Computer Engineering
基金 国家"863"计划基金资助项目"三维模型智能处理与检索平台"(2008AA01Z301) 虚拟现实应用教育部工程研究中心开放基金资助项目(MEOBNUEVRA200902)
关键词 三维点云 聚类 平面特征 轮廓线 数据精简 3D scattered points cloud clustering plane feature contour line data reduction
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参考文献5

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二级参考文献13

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