ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 395–402, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-395-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 395–402, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-395-2020

  03 Aug 2020

03 Aug 2020

DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS

J. Sanchez1, F. Denis1, F. Dupont1, L. Trassoudaine2, and P. Checchin2 J. Sanchez et al.
  • 1Univ Lyon, LIRIS, UMR 5205 CNRS, Université Claude Bernard Lyon 1, 43, bd du 11 novembre 1918, 69622 Villeurbanne CEDEX, France
  • 2Institut Pascal, UMR 6602, Université Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France

Keywords: point cloud, reconstruction, modeling, building interiors

Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.