Hierarchical point cloud denoising algorithm

Fu-qun ZHAO

doi:10.37188/OPE.20202807.1618

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Hierarchical point cloud denoising algorithm

Information Sciences | 更新时间：2020-08-13

- Hierarchical point cloud denoising algorithm
- Optics and Precision Engineering Vol. 28, Issue 7, Pages: 1618-1625(2020)
- 作者机构：
  
  1.西安财经大学信息学院, 陕西西安 710010
  2.西北大学信息科学与技术学院, 陕西西安 710127
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20202807.1618
  CLC： TP391
- Received：11 February 2020，
  
  Revised：01 April 2020，
  
  Accepted：01 April 2020，
  
  Published：15 July 2020
- 稿件说明：
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Fu-qun ZHAO. Hierarchical point cloud denoising algorithm[J]. Optics and precision engineering, 2020, 28(7): 1618-1625.
DOI：

Fu-qun ZHAO. Hierarchical point cloud denoising algorithm[J]. Optics and precision engineering, 2020, 28(7): 1618-1625. DOI： 10.37188/OPE.20202807.1618.

摘要

三维激光扫描设备获取的初始点云模型中含有较多的噪声点，不利于后期的点云处理，需要将其进行剔除。为了有效地保持点云的尖锐几何特征，本文提出一种由粗到精的层次化点云去噪算法。首先构造点及其邻域点的张量投票矩阵，通过计算该矩阵的特征值和特征向量构造扩散张量，并基于该扩散张量利用各向异性扩散方程进行循环滤波，从而实现点云初始粗去噪；然后计算滤波后点云的曲率特征，并根据曲率值进一步删除点云中的噪声点，从而实现点云精确去噪；最后通过计算点云熵值对去噪算法进行定量评价。实验结果表明，本文提出的点云去噪算法具有较大的熵值、较小的去噪误差和较高的执行效率。因此说，该层次化点云去噪算法在保持尖锐几何特征的同时，可以快速精确剔除噪声点，是一种有效的点云去噪算法。

Abstract

The initial point cloud model acquired by 3D laser scanning equipment contains more noise points that is not good for the later point cloud processing. Therefore

the noise needs to be deleted. A hierarchical point cloud coarse-to-fine denoising algorithm was proposed for effective retention of the sharp geometric features of the point cloud. The tensor voting matrix of the points and their neighbors was constructed. In addition

the diffusion tensor was constructed by calculating the eigenvalues and eigenvectors of the matrix. The diffusion tensor-based anisotropic diffusion equation was applied for cyclic filtering

to realize the initial coarse denoising of the point cloud. Further

the curvature feature of the point cloud was calculated post-filtering. To achieve fine denoising

the noise points in the point cloud were further deleted according to the curvature value. Finally

the point cloud entropy was calculated for quantitative evaluation of the denoising algorithm. The experimental results demonstrate that the proposed point cloud denoising algorithm exhibited a smaller denoising error

higher entropy value

and high execution efficiency. The proposed hierarchical point cloud denoising algorithm can quickly and accurately delete noise points

while retaining sharper geometric features of the point cloud. Therefore

it is an effective point cloud denoising algorithm.

关键词

Keywords

references

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