Point Cloud Simplification Method Based on k-Means Clustering

Yibo He; Ranli Chen; Kan Wu; Zhixin Duan

doi:10.3788/LOP56.091002

Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 9 >Page 091002 > Article

Laser & Optoelectronics Progress
Vol. 56, Issue 9, 091002 (2019)

Point Cloud Simplification Method Based on k-Means Clustering

Yibo He^1、*, Ranli Chen², Kan Wu³, and Zhixin Duan³

Author Affiliations

¹ Department of Architecture and Civil Engineering, Datong Vocational and Technical College of Coal, Datong, Shanxi 0 37003, China

² Department of Surveying Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, Hebei 0 50041, China

³ School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

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DOI: 10.3788/LOP56.091002 Cite this Article Set citation alerts

Yibo He, Ranli Chen, Kan Wu, Zhixin Duan. Point Cloud Simplification Method Based on k-Means Clustering[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091002 Copy Citation Text

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Abstract

A point cloud simplification method is proposed based on k-means clustering. Compared with the bounding box method with a similar compression rate, the k-means clustering method can preserve the details better, and the result is more consistent with the dense and sparse distribution of the original data. Moreover, the surface of the constructed model is smoother.

Keywords

compression rate image processing k-means clustering point cloud simplification root mean square curvature surface fitting

Yibo He, Ranli Chen, Kan Wu, Zhixin Duan. Point Cloud Simplification Method Based on k-Means Clustering[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091002

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