The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 535–539, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-535-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 535–539, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-535-2018

  19 Sep 2018

19 Sep 2018

INDOOR MESH CLASSIFICATION FOR BIM

L. S. Runceanu and N. Haala L. S. Runceanu and N. Haala
  • Institute for Photogrammetry, University of Stuttgart, Germany

Keywords: Mesh classification, BIM, Random Forest

Abstract. This work addresses the automatic reconstruction of objects useful for BIM, like walls, floors and ceilings, from meshed and textured mapped 3D point clouds of indoor scenes. For this reason, we focus on the semantic segmentation of 3D indoor meshes as the initial step for the automatic generation of BIM models. Our investigations are based on the benchmark dataset ScanNet, which aims at the interpretation of 3D indoor scenes. For this purpose it provides 3D meshed representations as collected from low cost range cameras. In our opinion such RGB-D data has a great potential for the automated reconstruction of BIM objects.