MSPoint: Point Cloud Denoising Network Based on Multiscale Distribution Score

Hao Hu; Qibing Wang; Jiawei Lu; Hongye Su; Jiankun Lai; Gang Xiao

doi:10.3788/LOP222402

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 16 >Page 1615002 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 16, 1615002 (2023)

MSPoint: Point Cloud Denoising Network Based on Multiscale Distribution Score

Hao Hu^1,*, Qibing Wang¹, Jiawei Lu¹, Hongye Su²..., Jiankun Lai³ and Gang Xiao^1,**|Show fewer author(s)

Author Affiliations

¹College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, Zhejiang, China

²College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China

³Zhejiang Xin Zailing Technology Co., Ltd., Hangzhou 310051, Zhejiang, China

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DOI: 10.3788/LOP222402 Cite this Article Set citation alerts

Hao Hu, Qibing Wang, Jiawei Lu, Hongye Su, Jiankun Lai, Gang Xiao. MSPoint: Point Cloud Denoising Network Based on Multiscale Distribution Score[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1615002 Copy Citation Text

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Fig. 1. Overall network structure

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Fig. 2. Feature extraction module

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Fig. 3. Displacement prediction module

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Fig. 4. Relation between score and denoising effect

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Fig. 5. Score estimation unit

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Fig. 6. Effect of different loss functions on point cloud denoising results. (a)

L_{s}

; (b)

L_{a}

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Fig. 7. Point cloud dataset. (a) block; (b) blade; (c) column; (d) joint; (e) casting; (f) cube; (g) fandisk

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Fig. 8. Schematic diagrams of point cloud neighborhood extraction. (a) joint; (b) blade; (c) block; (d) column

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Fig. 9. Comparison of 0.5% noise casting point cloud model denoising results

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Fig. 10. Comparison of 0.5% noise cube point cloud model denoising results

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Fig. 11. Comparison of 0.5% noise fandisk point cloud model denoising results

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Fig. 12. Point Cloud data of a university library

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Fig. 13. Comparison of gate steps before and after noise removal. (a) Before noise removal; (b) after noise removal

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Fig. 14. Comparison of back corridor before and after noise removal. (a) Before noise removal; (b) after noise removal

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Fig. 15. Comparison of corner before and after noise removal. (a) Before noise removal; (b) after noise removal

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Fig. 16. Loss convergence of disturbances of different degrees. (a)

σ = 0

; (b)

σ = 0.1 %

; (c)

σ = 0.5 %

; (d)

σ = 1.0 %

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$r$	joint	blade	block	column
2%	5.751	5.164	7.222	4.697
5%	2.742	3.661	4.527	2.467
8%	3.826	5.219	4.056	2.926
10%	7.873	5.771	6.745	3.566

Table 1. Comparison of denoising effects of different neighborhood radii using D_CD error as the evaluation standard

Gaussian noise	D_CD error /×10^-5
Gaussian noise	Noisy	TD	PointCleanNet	Pointfilter	MSPoint
	casting
0.3%	1.916	2.234	1.754	1.401	1.266
0.5%	3.676	3.628	3.672	3.331	2.906
1.0%	9.033	9.581	8.260	7.391	7.113
	cube
0.3%	2.257	1.356	0.963	0.712	0.631
0.5%	4.388	1.691	1.512	1.537	1.236
1.0%	12.57	2.503	2.178	1.861	2.025
	fandisk
0.3%	1.744	2.901	1.631	0.853	0.672
0.5%	3.577	3.219	2.384	1.903	1.898
1.0%	11.13	4.196	3.012	3.599	2.891

Table 2. Comparison of denoising effects of different algorithms using D_CD error as evaluation standard

Gaussian noise	P2F error /×10^-3
Gaussian noise	Noisy	TD	PointCleanNet	Pointfilter	MSPoint
	casting
0.3%	2.389	3.258	2.369	1.870	1.774
0.5%	3.915	3.792	3.823	4.387	3.668
1.0%	13.27	14.18	9.337	9.701	8.487
	Cube
0.3%	2.675	1.934	1.430	1.309	0.903
0.5%	4.572	2.187	2.028	1.726	1.499
1.0%	15.92	3.066	2.273	2.837	1.968
	fandisk
0.3%	2.446	3.402	1.726	1.351	0.745
0.5%	4.473	3.776	3.222	2.878	2.182
1.0%	14.93	5.771	4.353	3.884	3.598