特征保持点云数据精简

袁小翠; 吴禄慎; 陈华伟

doi:10.3788/OPE.20152309.2666

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特征保持点云数据精简

信息科学 | 更新时间：2020-08-12

- 特征保持点云数据精简
- Feature preserving point cloud simplification
- 光学精密工程 2015年23卷第9期页码：2666-2676
- 作者机构：
  
  南昌大学机电工程学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51365037,No.51065021)()
- DOI：10.3788/OPE.20152309.2666
  中图分类号： TP391.7
- 收稿日期：2015-03-30，
  
  修回日期：2015-06-03，
  
  纸质出版日期：2015-09-25
- 稿件说明：
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袁小翠, 吴禄慎, 陈华伟. 特征保持点云数据精简[J]. 光学精密工程, 2015,23(9): 2666-2676

YUAN Xiao-cui, WU Lu-shen, CHEN Hua-wei. Feature preserving point cloud simplification[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2666-2676
袁小翠, 吴禄慎, 陈华伟. 特征保持点云数据精简[J]. 光学精密工程, 2015,23(9): 2666-2676 DOI： 10.3788/OPE.20152309.2666.

YUAN Xiao-cui, WU Lu-shen, CHEN Hua-wei. Feature preserving point cloud simplification[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2666-2676 DOI： 10.3788/OPE.20152309.2666.

摘要

由于三维扫描设备采集的点云数据庞大

本文提出了一种特征保持的点云精简方法以在减少冗余数据的同时更好地保持原始曲面的几何特征。首先

利用

均值聚类法在空间域对点云全局聚类

对点云构建

-d树并以

-d树的部分节点作为初始化聚类中心。然后

用主成分分析法估计点云法矢和候选特征点

遍历每个聚类

若类中包含特征点则将该类细分为多个子类

细分时将聚类映射到高斯球。最后

基于自适应均值漂移法对高斯球上的数据进行分类

高斯球上的聚类结果对应为空间聚类细分结果

各聚类中心的集合为精简结果。以多个实物模型为例验证了算法的有效性。结果表明

本文方法精简的点云在平坦区域保留少数点

在高曲率区域保留更多的点。相比于非均匀网格、层次聚类、

均值点云精简法

该方法对包含尖锐特征的曲面精简误差最小

更好地保留了原始曲面的几何特征。

Abstract

3D scanning devices generally produce a large amount of dense points. This paper presents a feature preserving point cloud simplification method to reduce redundant points while preserving original geometric features well. Firstly

-mean clustering algorithm was employed to globally gather similar points in a spatial domain. By constructing a

K-d

tree structure for the point cloud

some nodes of the

K-d

tree were used as initial clustering centroids. Then

normal vector of point cloud and candidate feature points were estimated with principal component analysis method. Traversing every cluster

if feature points were contained in the cluster

the cluster was subdivided into a series of sub-clusters and the cluster was mapped to a Gaussian sphere. Finally

adaptive mean shift algorithm was employed to classify the data in Gaussian sphere and the clusters in Gaussian sphere were corresponded to the sub-clusters in the spatial domain. The cluster centroids present the simplification data. Several real object models were used to verify the effectiveness of the proposed method. The experiment results demonstrate that the proposed method generates sparse sampling points in flat areas and high density points in high curvature regions. As comparing with the non-uniform grid

hierarchical agglomerative

and

-means methods

the proposed method obtains the smallest simplification error and preserves original geometric features.

关键词

Keywords

references

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