Research on feature-preserving method of point-sampled model simplification

doi:10.3778/j.issn.1002-8331.2010.21.058

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (21): 202-204.DOI: 10.3778/j.issn.1002-8331.2010.21.058

• 图形、图像、模式识别 • Previous Articles Next Articles

Research on feature-preserving method of point-sampled model simplification

NI Tong-guang，GU Xiao-qing，YANG Chang-chun

School of Information Science & Engineering，Jiangsu Polytechnic University，Changzhou，Jiangsu 213164，China

Received:2009-01-09 Revised:2009-03-23 Online:2010-07-21 Published:2010-07-21
Contact: NI Tong-guang

特征保持的点模型简化技术研究

倪彤光，顾晓清，杨长春

江苏工业学院信息科学与工程学院，江苏常州　213164

通讯作者: 倪彤光

Abstract

Abstract: A feature-preserving method to reduce point cloud data from different scans is proposed.The source data may include no additional information other than coordinates of the measured points.Based on the curvature estimation，the cloud data can be simplified with mean-shift clustering algorithm effectively.Experimental results show that the algorithm is efficient and features of the original mesh can be preserved perfectly.

Key words: point-sampled model simplification, curvatures on surface, mean-shift

摘要： 提出一种以物体表面上不附加任何几何和拓扑信息的散乱点集为处理对象，特征保持的点云数据简化的方法。通过直接在散乱点上计算曲率的方法，将数据点分为特征点和非特征点两类，分别应用不同参数的均值漂移聚类算法进行简化。实验结果表明算法既能有效简化点云数据，而且很好地保留了原网格模型的特征信息。

关键词: 点模型简化, 曲率, 均值漂移

CLC Number:

TP391.41

NI Tong-guang，GU Xiao-qing，YANG Chang-chun. Research on feature-preserving method of point-sampled model simplification[J]. Computer Engineering and Applications, 2010, 46(21): 202-204.

倪彤光，顾晓清，杨长春.

特征保持的点模型简化技术研究

[J]. 计算机工程与应用, 2010, 46(21): 202-204.

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Research on feature-preserving method of point-sampled model simplification

特征保持的点模型简化技术研究

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