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基于k-means聚类的点云精简方法

Point Cloud Simplification Method Based on k-Means Clustering

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摘要

提出了一种基于k均值(k-means)聚类的点云精简方法。与包围盒法相比,在压缩率近似相同的条件下,k-means聚类方法能较好地保留细节特征,与原始数据的稠密稀疏分布更加一致,所建模型表面更光滑。

Abstract

A point cloud simplification method is proposed based on k-means clustering. Compared with the bounding box method with a similar compression rate, the k-means clustering method can preserve the details better, and the result is more consistent with the dense and sparse distribution of the original data. Moreover, the surface of the constructed model is smoother.

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中图分类号:TP391

DOI:10.3788/lop56.091002

所属栏目:图像处理

收稿日期:2018-10-17

修改稿日期:2018-11-10

网络出版日期:2018-11-30

作者单位    点击查看

贺一波:大同煤炭职业技术学院建筑工程系, 山西 大同 037003
陈冉丽:石家庄铁路职业技术学院测绘工程系, 河北 石家庄 050041
吴侃:中国矿业大学环境与测绘学院, 江苏 徐州 221116
段志鑫:中国矿业大学环境与测绘学院, 江苏 徐州 221116

联系人作者:贺一波(511149199@qq.com)

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引用该论文

He Yibo,Chen Ranli,Wu Kan,Duan Zhixin. Point Cloud Simplification Method Based on k-Means Clustering[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091002

贺一波,陈冉丽,吴侃,段志鑫. 基于k-means聚类的点云精简方法[J]. 激光与光电子学进展, 2019, 56(9): 091002

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