Planar point cloud denoising using the methodology of fusion of principal component analysis and local neighborhood

doi:10.13474/j.cnki.11-2246.2020.0143

Abstract

Abstract: Large hydraulic structures are very important infrastructures of the nation. It has become a research hotspot to realize the deformation monitoring using laser scanning technique. Buildings are with many planar structure characteristics, and in order to remove the noise efficiently from the point cloud of planar structures, the methodology combining principal component analysis and local neighborhood is proposed. Firstly, the global noise is eliminated using principal component analysis, and the local noise is detected and removed by considering its local neighborhood. The methodology is verified by the measured data and the results demonstrate that the proposed methodology has the advantages of high-efficiency and feasibility, which is of high application value.

Key words: deformation monitoring, principal component analysis, local neighborhood, denoising, planar features

CLC Number:

P234

CHEN Jianhua. Planar point cloud denoising using the methodology of fusion of principal component analysis and local neighborhood[J]. Bulletin of Surveying and Mapping, 2020, 0(5): 47-50,68.

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