Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 278-288.doi: 10.13229/j.cnki.jdxbgxb20180948

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

Xiao-hui WANG1,2(),Lu-shen WU1(),Hua-wei CHEN3   

  1. 1. School of Mechatronic Engineering, Nanchang University, Nanchang 330031, China
    2. School of Architectural and Mechanical Engineering, Chifeng University, Chifeng 024000, China
    3. School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China
  • Received:2018-09-17 Online:2020-01-01 Published:2020-02-06
  • Contact: Lu-shen WU E-mail:babywxh@126.com;wulushen@163.com

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Abstract:

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.

Key words: computer application, point cloud denoising, normal vector distance, weighted local optimal projection, bilateral filtering

CLC Number: 

  • TP391.41

Fig.1

Flow chart of proposed method"

Fig.2

Normal vector estimation and direction adjustment results of different point cloud models"

Fig.3

Classification effect of different point cloud models based on normal vector distance"

Table 1

Denoising parameter settings of bilateral filtering algorithm"

点云模型 双边滤波参数
σ c σ s
Bunny A1 3 0.1
A2 3 1.0
A3 10 0.1
Fandisk

B1

B2

B3

0.3

0.3

1.5

0.01

0.10

0.01

Table 2

Denoising parameter settings of WLOP algorithm"

点云模型 WLOP
S u b h μ I t e r
Bunny C1 10 0.1 0.45 50
C2 10 2.0 0.45 50
C2 20 2.0 0.45 50
Fandisk

D1

D2

D3

5

5

10

0.1

1.0

0.1

0.45

0.45

0.45

50

50

50

Table 3

Optimal parameter settings used by proposed method"

点云模型 点云数目 R S N /dB σ c σ s S u b h μ
Bunny 32 201 30 3 3 10 0.56 0.45
Fandisk 53 521 40 0.1 0.1 5 0.13 0.45

Fig.4

Bilateral filtering denoising effect of Bunny point cloud model with 30 dB noise under different denoising parameters"

Fig.5

WLOP denoising effect of Bunny point cloud model with 30 dB noise under different denoising parameters"

Fig.6

Bilateral filtering denoising effect of Fandisk point cloud model with 40 dB noise under different denoising parameters"

Fig.7

WLOP denoising effect of Fandisk point cloud model with 40 dB noise under different denoising parameters"

Fig.8

Bunny original point cloud model and noise model"

Fig.9

Comparison of denoising results of Bunny model"

Fig.10

Fandisk original point cloud model and noise model"

Fig.11

Comparison of denoising results of Fandisk model"

Table 4

Denoising parameter settings of bilateral filtering algorithm and WLOP algorithm"

点云

模型

R S N

/dB

 双边滤波 WLOP
σ c σ s S u b h μ I t e r
Bunny 30 5

5

0.3

 10 1 0.45 50
Fandisk 40 0.3 5 0.36 0.45 50

Table 5

Deviation comparison of denoising results using three methods"

点云

模型

去噪

方法

最大距离

/mm

平均距离

/mm

标准偏差

/mm

运行时间

/s
Bunny WLOP 0.667 6 0.011 2 0.071 5 4.570
双边滤波 0.574 1 0.005 2 0.015 5 0.117
本文方法 0.508 4 0.001 1 0.013 2 1.210
Fandisk WLOP 0.262 8 0.002 4 0.010 0 29.780
双边滤波 0.239 6 0.000 9 0.004 4 0.1790
本文方法 0.035 3 0.000 7 0.004 0 4.4760

Table 6

Parameter settings used by proposed method"

点云模型 R S N /dB σ c σ s S u b h μ I t e r
Bunny 25 5 5 10 0.86 0.45 50
35 3 3 10 0.56 0.45 50
Fandisk

35

45

0.5

0.1

0.5

0.1

5

5

0.33

0.13

0.45

0.45

50

50

Fig.12

Bunny point cloud model with different intensity noise and its denoising results"

Fig.13

Fandisk point cloud model with different intensity noise and its denoising results"

Table 7

Deviation analysis of denoising results for different intensity noise model"

点云模型 R S N /dB

最大距离

/mm

平均距离

/mm

标准偏差

/mm
Bunny 25 0.404 5 0.006 9 0.022 3
35 0.329 1 0.000 6 0.009 5
Fandisk 35 0.230 4 0.001 5 0.006 4
45 0.058 9 0.001 0 0.004 3

Fig. 14

Deviation chromatogram after denoising of different intensity noise models"

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