Point Cloud Denoising Algorithm Combined with Improved Radius Filtering and Local Plane Fitting

Changlong Guo; Zhenping Xia; Chaochao Li; Hao Chen; Yuanshen Zhang

doi:10.3788/LOP231597

Laser & Optoelectronics Progress, Volume. 61, Issue 12, 1215003(2024)

Point Cloud Denoising Algorithm Combined with Improved Radius Filtering and Local Plane Fitting

Changlong Guo¹, Zhenping Xia^1,2、*, Chaochao Li¹, Hao Chen², and Yuanshen Zhang²

¹College of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu , China

²School of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu , China

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Figures & Tables(7)

Fig. 1. Point cloud distribution

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Fig. 2. Flow chart of point cloud denoising algorithm

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Fig. 3. Diagram of radius filtering

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Fig. 4. Denoising results of the different methods when 20% noise is added to the original point clouds. (a) The Bun000 model; (b) the Chair model; (c) the Horse model

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Fig. 5. Denoising results of the different methods when 40% noise is added to the original point clouds. (a) The Bun000 model; (b) the Chair model; (c) the Horse model

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Table 1. Performance evaluation of different denoising methods under different noise levels

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Table 1. Performance evaluation of different denoising methods under different noise levels

Evaluation parameter	Method	20% noise proportion				40% noise proportion
Evaluation parameter	Method	Bun000	Chair	Horse	Average	Bun000	Chair	Horse	Average
P_N	KNN	0.9302	0.9143	0.9205	0.9217	0.9335	0.9093	0.9222	0.9217
	RF-SF	0.9308	0.9147	0.9209	0.9221	0.9337	0.9100	0.9225	0.9221
	DB-SF	0.9399	0.9130	0.9293	0.9274	0.9419	0.9115	0.9364	0.9229
	Ours	0.9704	0.9400	0.9715	0.9606	0.9704	0.9493	0.9715	0.9637
P_R	KNN	0.8978	0.8564	0.9261	0.8934	0.8938	0.8745	0.8952	0.8878
	RF-SF	0.8986	0.8622	0.9298	0.8966	0.9001	0.8738	0.9027	0.8922
	DB-SF	0.9011	0.8621	0.9300	0.8977	0.9277	0.8805	0.9168	0.9083
	Ours	0.9607	0.9553	0.9688	0.9616	0.9685	0.9271	0.9677	0.9544
P	KNN	0.9032	0.8660	0.9251	0.8981	0.9052	0.8844	0.9029	0.8975
	RF-SF	0.9040	0.8710	0.9275	0.9008	0.9097	0.8841	0.9083	0.9007
	DB-SF	0.9075	0.8706	0.9298	0.9026	0.9317	0.8894	0.9224	0.9145
	Ours	0.9623	0.9527	0.9692	0.9614	0.9690	0.9334	0.9688	0.9571
T/s	KNN	19.45	44.89	30.66	31.67	24.54	56.96	38.21	39.90
	RF-SF	2.12	2.50	2.34	2.32	2.47	2.86	2.72	2.68
	DB-SF	78.75	156.61	126.22	120.53	76.22	176.99	111.08	121.43
	Ours	2.25	3.29	2.78	2.77	2.54	3.13	2.85	2.84

Table 2. Results of far-noise denoising in proposed method

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Table 2. Results of far-noise denoising in proposed method

Evaluation parameter	20% noise proportion			40% noise proportion
Evaluation parameter	Bun000	Chair	Horse	Bun000	Chair	Horse
P_N	0.8189	0.7687	0.7886	0.8201	0.8492	0.8604
P_R	0.9905	0.9874	0.9994	0.9910	0.9481	0.9982
P	0.9619	0.9510	0.9643	0.9421	0.9198	0.9588
T/s	1.89	2.70	2.12	2.14	2.67	2.35

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Changlong Guo, Zhenping Xia, Chaochao Li, Hao Chen, Yuanshen Zhang. Point Cloud Denoising Algorithm Combined with Improved Radius Filtering and Local Plane Fitting[J]. Laser & Optoelectronics Progress, 2024, 61(12): 1215003

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Paper Information

Category: Machine Vision

Received: Jun. 25, 2023

Accepted: Aug. 30, 2023

Published Online: Jun. 5, 2024

The Author Email: Zhenping Xia (xzp@usts.edu.cn)

DOI:10.3788/LOP231597

CSTR:32186.14.LOP231597

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology