The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W17
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W17, 217–224, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W17-217-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W17, 217–224, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W17-217-2019

  29 Nov 2019

29 Nov 2019

DENOISING OF 3D POINT CLOUDS

E. Mugner1 and N. Seube2 E. Mugner and N. Seube
  • 113, Av de l’Europe, 31520 Ramonville Ste-Agne, France
  • 23051 rue du Plateau, J7V 8P2 Vaudreuil-Dorion, Québec, Canada

Keywords: Denoising, Airborne LiDAR, 3D point clouds

Abstract. 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.