基于曲率特征的点云去噪及定量评价方法研究

doi:10.13474/j.cnki.11-2246.2019.0195

测绘通报 ›› 2019, Vol. 0 ›› Issue (6): 105-108.doi: 10.13474/j.cnki.11-2246.2019.0195

基于曲率特征的点云去噪及定量评价方法研究

朱广堂, 叶珉吕

佛山市测绘地理信息研究院, 广东佛山 528000

收稿日期:2018-11-09 修回日期:2019-04-23 出版日期:2019-06-25 发布日期:2019-07-01
作者简介:朱广堂(1974-),男,博士,高级工程师,主要从事智慧城市和测绘工作。E-mail:zhugt@vip.qq.com

Research on the method of point cloud denoising based on curvature characteristics and quantitative evaluation

ZHU Guangtang, YE Minlü

Foshan Surveying, Mapping and Geoinformation Research Institute, Foshan 528000, China

Received:2018-11-09 Revised:2019-04-23 Online:2019-06-25 Published:2019-07-01

摘要/Abstract

摘要：

由于仪器测距、测角误差，物体反射率，人为操作，遮挡及环境变化等原因，扫描获取的点云数据中存在大量的噪声点。为了高效去除噪声并保持原始点云数据的特征信息，本文提出了基于移动最小二乘曲率特征的点云去噪算法。首先采用移动最小二乘法（MLS）精确计算点云的曲率信息，然后根据曲率信息进行点云去噪，最后利用基于信息熵理论的定量评价方法验证本文方法的可行性。

关键词: 曲率特征, 移动最小二乘, 点云去噪, 信息熵

Abstract:

Due to the errors of instrument ranging, angle measurement, reflectivity of objects, artificial operation, occlusion and environmental changes, there are a lot of noise in the point cloud data. In order to remove noise efficiently and keep the feature information of original point cloud data, the thesis proposes a new method of point cloud denoising based on moving least square curvature feature. Firstly, the curvature information of point cloud is accurately calculated by moving least squares method, and then the point cloud is denoised according to the curvature information. Finally, the feasibility of this method is verified by the quantitative evaluation method based on information entropy theory.

Key words: curvature characteristics, moving least squaves, point cloud denoising, information entropy

中图分类号:

P237

朱广堂, 叶珉吕. 基于曲率特征的点云去噪及定量评价方法研究[J]. 测绘通报, 2019, 0(6): 105-108.

ZHU Guangtang, YE Minlü. Research on the method of point cloud denoising based on curvature characteristics and quantitative evaluation[J]. 测绘通报, 2019, 0(6): 105-108.

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[6]	李明磊, 李广云, 宗文鹏. 激光扫描点云准确快速去噪方法[J]. 测绘通报, 2015, 0(12): 27-29.
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基于曲率特征的点云去噪及定量评价方法研究

Research on the method of point cloud denoising based on curvature characteristics and quantitative evaluation

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