Feedback-based iterative sampling denoising framework for point clouds with high-level noise

doi:10.11996/JG.j.2095-302X.2025030614

Abstract

Abstract:

During the 3D point cloud collection process, point cloud data is easily interfered by noise due to factors such as measurement anomalies, edge scattering, and the material properties of the measured object. However, the current depth point cloud denoising algorithms perform poorly under high-level noise conditions and can easily lead to smoothing of sharp features. To address this problem, a feedback-based sampling denoising framework for point clouds with high-level noise was proposed, with the aim of enhancing the performance of existing supervised denoising algorithms under high-level noise conditions. First, the noisy point cloud was denoised using the existing supervised noise network to obtain a pre-denoised point cloud. Second, the original noisy point cloud and the pre-denoised point cloud were jointly input into the sampling module to obtain a fusion point cloud containing geometric details and edge features. Third, the feedback-aware refinement network denoised the fused point cloud under the guidance of feedback from the pre-denoised point cloud to obtain the denoising result for this round of iterations. Finally, by using the denoised result from the current iteration as the feedback for the next round and as input to the sampling fusion module, the process was iterated progressively until the final denoising result was obtained. Experimental results demonstrated that this framework enhanced the performance of existing supervised point cloud noise denoising algorithms under high-level noise conditions, exhibiting excellent denoising effects and feature retention capabilities.

Key words: point cloud denoising, point cloud sampling, high-level noise, feedback mechanism, iteration

CLC Number:

TP391

WANG Changchang, JIANG Kun, JIANG Kai, ZHANG Peng, SU Zhiyong. Feedback-based iterative sampling denoising framework for point clouds with high-level noise[J]. Journal of Graphics, 2025, 46(3): 614-624.

Figures/Tables 18

References 26

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模型	噪声点云	DMR	Score	PCN	PF	本文方法
Tetrahedron	40.19	46.37	46.64	49.42	50.73	50.86
Boxunion	40.46	46.29	46.13	48.59	49.10	49.07
Cube	40.07	46.40	46.47	48.07	48.48	48.54
Fandisk	41.28	47.33	47.26	49.98	50.44	50.84
Horse	38.89	43.35	44.06	45.62	45.68	47.05
Joint	38.52	43.42	44.40	45.78	46.16	46.04
Mask	45.26	50.22	52.05	51.99	51.88	52.76
Screwdriver	36.19	40.57	40.67	43.93	44.96	46.10

模型	噪声点云	DMR	Score	PCN	PF	本文方法
Tetrahedron	40.19	46.37	46.64	49.42	50.73	50.86
Boxunion	40.46	46.29	46.13	48.59	49.10	49.07
Cube	40.07	46.40	46.47	48.07	48.48	48.54
Fandisk	41.28	47.33	47.26	49.98	50.44	50.84
Horse	38.89	43.35	44.06	45.62	45.68	47.05
Joint	38.52	43.42	44.40	45.78	46.16	46.04
Mask	45.26	50.22	52.05	51.99	51.88	52.76
Screwdriver	36.19	40.57	40.67	43.93	44.96	46.10

模型	噪声点云	DMR	Score	PCN	PF	本文方法
Tetrahedron	11.81	6.02	5.92	4.32	3.87	3.83
Boxunion	11.80	6.30	6.52	4.83	4.53	4.55
Cube	12.88	6.42	6.38	5.17	4.98	4.91
Fandisk	11.55	5.94	6.07	4.35	4.02	3.98
Horse	11.69	6.34	6.15	5.04	4.61	4.39
Joint	12.93	7.18	6.65	5.56	5.20	5.23
Mask	13.77	8.23	6.75	6.75	6.31	5.96
Screwdriver	10.91	6.32	6.44	6.44	3.79	3.56

模型	噪声点云	DMR	Score	PCN	PF	本文方法
Tetrahedron	11.81	6.02	5.92	4.32	3.87	3.83
Boxunion	11.80	6.30	6.52	4.83	4.53	4.55
Cube	12.88	6.42	6.38	5.17	4.98	4.91
Fandisk	11.55	5.94	6.07	4.35	4.02	3.98
Horse	11.69	6.34	6.15	5.04	4.61	4.39
Joint	12.93	7.18	6.65	5.56	5.20	5.23
Mask	13.77	8.23	6.75	6.75	6.31	5.96
Screwdriver	10.91	6.32	6.44	6.44	3.79	3.56

模型	噪声点云	DMR	Score	PCN	PF	本文方法
Tetrahedron	34.46	36.51	36.24	45.26	47.17	47.69
Boxunion	34.65	36.92	35.95	43.07	42.57	43.47
Cube	34.52	37.61	36.53	44.87	44.85	45.97
Fandisk	35.33	37.57	37.07	45.12	45.08	45.13
Horse	32.70	35.75	35.12	41.64	42.39	42.48
Joint	33.67	35.23	34.77	40.20	41.38	41.82
Mask	38.88	46.11	44.15	49.63	50.87	49.98
Screwdriver	30.74	33.04	31.81	38.54	39.70	40.20