Research on Point Cloud Simplification with Preserved Features

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Research on Point Cloud Simplification with Preserved Features

更新时间：2025-07-09

- Research on Point Cloud Simplification with Preserved Features
- Vol. 44, Issue 11, Pages: 37-40(2010)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH16
- Online First：10 November 2010，
  
  Published：2010
- 稿件说明：
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Research on Point Cloud Simplification with Preserved Features[J]. 2010, 44(11): 37-40.
DOI：

Research on Point Cloud Simplification with Preserved Features[J]. 2010, 44(11): 37-40. DOI：

摘要

为了在保持特征的基础上有效地简化点云数据

提出了基于聚类的点云精简算法.对点云进行三维栅格剖分

在每个栅格中选取1个代表点作为初始类核心

然后将点云中其他数据点归入欧氏距离最近的初始类中

遍历各个类

若类内某两点的法向量偏差大于给定带宽则对该类进行迭代细分

并对各个类进行均值漂移处理

将得到的局部模态点取代该类

从而实现点云简化.以手机外壳、人头、麻花钻为典型实例

对具有不同表面特征的点云数据进行了验证.结果表明

该算法能对点云数据进行直接而有效的精简

在曲率变化大、附加特征多的表面仍能很好地保留原始模型的几何形状.

Abstract

To simplify the point cloud while preserving small features

a novel algorithm based on clustering is proposed. The whole point could is divided into a series of initial sub-clusters with the 3-D grid subdivision method

and in each initial sub-cluster one representative point is selected as the centroid. The points other than those representatives are distributed to their nearest initial sub-cluster centroids according to Euclidean distance

and new clusters are generated. Traversing all new formed clusters

if the normal vector deviation of any two inner points is greater than the given threshold

the cluster is necessarily subdivided

then each cluster is processed iteratively by mean shift to obtain the local mode points

which are adopted to substitute the clusters. Some typical cases with various surface features

such as mobile shell

human-head sculpture and twisted drill

are chosen to verify this method. The result indicates that the new algorithm enables to reduce data directly and efficiently while maintaining the geometry of the original model

especially for the surfaces with sharp edges and complex additional features.

关键词

Keywords

references

LEE K H, WOO H, SUK T. Point data reduction using 3D grids [J]. Advanced Manufacturing Technology, 2001,18:201-210.

SONG H, FENG H Y. A global clustering approach to point cloud simplification with a specified data reduction ratio [J]. Computer-Aided Design, 2008,40:281-292.

洪军,丁玉成,曹亮,等. 逆向工程中的测量数据精简技术研究 [J]. 西安交通大学学报,2004,38(7):661-664.

HONG Jun, DING Yucheng, CAO Liang, et al. Data cloud direct reduction in reverse engineering [J]. Journal of Xi'an Jiaotong University, 2004,38(7):661-664.

王仁芳,张三元,叶修梓. 基于相似性的点模型简化算法 [J].浙江大学学报,2009,43(3):448-454.

WANG Renfang, ZHANG Sanyuan, YE Xiuzi. Similarity-based simplification of point sampled surfaces [J]. Journal of Zhejiang University, 2009,43(3):448-454.

COMANICIU D, MEER P. Mean shift: a robust approach toward feature space analysis [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619.

FUKUNAGA K, HOSTETLER L. The estimation of the gradient of a density function, with applications in pattern recognition [J]. IEEE Trans Info Theory, 1975,21:32-40.

BENTLEY J L. Multidimensional binary search trees used for associative searching [J]. Communications of the ACM, 1975,18(9):509-517.

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