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Research on 3D Point Cloud Classification Based on Density-Based Spatial Clustering of Algorithm with Noise

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Bibliographic Details
Title: Research on 3D Point Cloud Classification Based on Density-Based Spatial Clustering of Algorithm with Noise
Authors: Keren He, Hang Chen
Source: Journal of Nanoelectronics and Optoelectronics. 18:978-984
Publisher Information: American Scientific Publishers, 2023.
Publication Year: 2023
Subject Terms: 0103 physical sciences, 02 engineering and technology, 0210 nano-technology, 01 natural sciences
Description: The classification of three-dimensional point clouds is a complex task because of its disorder and uneven density. This paper proposes that in the point-cloud preprocessing stage, the Density-Based Spatial Clustering of Algorithm with Noise (DBSCAN) is added to cluster the three-dimensional point cloud, then the clustering results are extracted through the PointNet deep learning network to extract the characteristics of the local area, thus outputting the classification results of the point cloud. This method can not only reflect the feature distribution of point cloud in three-dimensional space, but also can be divided into several classes according to the different shape features of point cloud. Verified in the ModelNet10 and ModelNet40 point cloud dataset, the classification accuracy of this method on both ModelNet10 and ModelNet40 can reach more than 92.5%.
Document Type: Article
Language: English
ISSN: 1555-130X
DOI: 10.1166/jno.2023.3469
Accession Number: edsair.doi...........04b0b96e8ab57bd78bb60f27e89f7a6b
Database: OpenAIRE
Description
Abstract:The classification of three-dimensional point clouds is a complex task because of its disorder and uneven density. This paper proposes that in the point-cloud preprocessing stage, the Density-Based Spatial Clustering of Algorithm with Noise (DBSCAN) is added to cluster the three-dimensional point cloud, then the clustering results are extracted through the PointNet deep learning network to extract the characteristics of the local area, thus outputting the classification results of the point cloud. This method can not only reflect the feature distribution of point cloud in three-dimensional space, but also can be divided into several classes according to the different shape features of point cloud. Verified in the ModelNet10 and ModelNet40 point cloud dataset, the classification accuracy of this method on both ModelNet10 and ModelNet40 can reach more than 92.5%.
ISSN:1555130X
DOI:10.1166/jno.2023.3469