DENOISING OF 3D POINT CLOUDS

A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system may supplement a geometric approach. Our method...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. remote sensing and spatial information sciences., 2019, Vol.XLII-2/W17, p.217-224
Main Authors: Mugner, E., Seube, N.
Format: Article
Language:English
Subjects:
Algorithms
Biological & chemical terrorism
Lidar
Mapping
Noise reduction
Photogrammetry
Random errors
Three dimensional models
Weights & measures
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Summary:A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system may supplement a geometric approach. Our method can be applied to any point cloud, acquired by a fixed, a mobile or an airborne LiDAR system. We present the principle of the method and some results from various LiDAR system mounted on UAVs. A comparison of a low-cost LiDAR system and a high-grade LiDAR system is performed on the same area, showing the benefits of applying our denoising algorithm to UAV LiDAR data. We also present the impact of denoising as a pre-processing tool for ground classification applications. Finaly, we also show some application of our denoising algorithm to dense point clouds produced by a photogrammetry software.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-2-W17-217-2019