Section Point Cloud Denoising Method Based on Enhanced DBSCAN and Distance Consensus Evaluation

Authors

Chengpeng Ge, School of Naval Architecture & Ocean Engineering, Jiangsu University of Science & Technology, Zhenjiang 212003, China
Dong Zhao, School of Naval Architecture & Ocean Engineering, Jiangsu University of Science & Technology, Zhenjiang 212003, China
Rui Wang, School of Naval Architecture & Ocean Engineering, Jiangsu University of Science & Technology, Zhenjiang 212003, China
Qinghua Ma, Jiangsu Hantong Wing Heavy Industry Co, Nantong 226010, China

Abstract

Abstract: A denoising method based on the improved DBSCAN(density-based spatial clustering of applications with noise) algorithm is proposed to address the problem of removing noise points in point cloud data. The statistical filtering method is applied to pre-screen isolated outliers and remove largescale noise from the point cloud. The DBSCAN algorithm is optimized to reduce computational time complexity and achieve adaptive parameter adjustment, thereby dividing the point cloud into normal clusters, suspected clusters and abnormal clusters, and immediately removing abnormal clusters. Distance consensus assessment is applied, and suspect clusters are further evaluated. By calculating the distance between the suspected point and its nearest normal point fitting surface, it is determined whether the suspected point is abnormal, effectively maintaining the key features of the data and model sensitivity. This approach outperforms other algorithms in denoising efficiency and feature retention by being implemented on hull point clouds, which ensures the integrity of the point cloud data's geometric properties.

Recommended Citation

Ge, Chengpeng; Zhao, Dong; Wang, Rui; and Ma, Qinghua (2024) "Section Point Cloud Denoising Method Based on Enhanced DBSCAN and Distance Consensus Evaluation," Journal of System Simulation: Vol. 36: Iss. 8, Article 6.
DOI: 10.16182/j.issn1004731x.joss.24-0153
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss8/6

First Page

1800

Last Page

1809

CLC

TP391.9

Recommended Citation

Ge Chengpeng, Zhao Dong, Wang Rui, et al. Section Point Cloud Denoising Method Based on Enhanced DBSCAN and Distance Consensus Evaluation[J]. Journal of System Simulation, 2024, 36(8): 1800-1809.

Corresponding Author

Zhao Dong

DOI

10.16182/j.issn1004731x.joss.24-0153