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

doi:10.16182/j.issn1004731x.joss.24-0153

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 large-scale 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.

Key words: point cloud denoising, point cloud data, DBSCAN(density-based spatial clustering of applications with noise) clustering, distance consensus assessment, feature preservation

CLC Number:

TP391.9

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

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