Denoising of scattered point cloud data based on normal vector distance classification

被引：0
|
作者：
Wang X.-H. [1 ,2 ]
Wu L.-S. [1 ]
Chen H.-W. [3 ]
机构：
[1] School of Mechatronic Engineering, Nanchang University, Nanchang
[2] School of Architectural and Mechanical Engineering, Chifeng University, Chifeng
[3] School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 01期
关键词：
Bilateral filtering; Computer application; Normal vector distance; Point cloud denoising; Weighted local optimal projection;
D O I：
10.13229/j.cnki.jdxbgxb20180948
中图分类号：
学科分类号：
摘要：
In the denoising of 3D point cloud data, it is difficult to keep the features of sharp areas while making smooth areas highly smooth. To solve this problem, a denoising method based on normal vector distance classification is proposed. Firstly, the differential geometry information of point cloud data was calculated. A robust method was used to estimate the normal vectors, and the normal vectors were adjusted to the same direction. The curvature was estimated by fitting the local quadratic surface of the sampling point. Then, the point cloud data were divided into smooth areas and sharp areas by calculating the normal vector distance from the sampling point to its tangent plane. Finally the smooth areas and the sharp areas were denoised respectively by weighted local optimal projection algorithm and bilateral filtering algorithm. The Bunny and Fandisk models were tested using weighted local projection algorithm, bilateral filtering algorithm and the proposed method respectively. The test results show that the proposed method can eliminate the isolated points in the noise model, improve the uniformity of point cloud distribution, and enhance the smoothness of the smooth areas. At the same time, it can also keep the geometric features of sharp areas, avoids excessive smoothing and detail feature distortion. Compared with the test data, the error and deviation of the point cloud model after noise reduction are smaller, the average error of the Bunny model is 0.001 1 mm, and the average error of the Fandisk model is 0.000 7 mm. © 2020, Jilin University Press. All right reserved.
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页码：278 / 288
页数：10
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