The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-4/W1-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W1-2020, 49–55, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-49-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W1-2020, 49–55, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-49-2020

  03 Sep 2020

03 Sep 2020

POINT CLOUD ROOM SEGMENTATION BASED ON INDOOR SPACES AND 3D MATHEMATICAL MORPHOLOGY

E. Frías1, J. Balado1,2, L. Díaz-Vilariño1, and H. Lorenzo1 E. Frías et al.
  • 1Universidade de Vigo, CINTECX, Applied Geotechnologies Research Group, Campus universitario de Vigo, As Lagoas, Marcosende 36310 Vigo, Spain
  • 2Delft University of Technology, Faculty of Architecture and the Built Environment, GIS Technology Section, 2628 BL Delft, the Netherlands

Keywords: room segmentation, indoor spaces, point cloud segmentation, 3D morphology, indoor navigation, reconstruction

Abstract. Room segmentation is a matter of ongoing interesting for indoor navigation and reconstruction in robotics and AEC. While in robotics field, the problem room segmentation has been typically addressed on 2D floorplan, interest in enrichment 3D models providing more detailed representation of indoors has been growing in the AEC. Point clouds make available more realistic and update but room segmentation from point clouds is still a challenging topic. This work presents a method to carried out point cloud segmentation into rooms based on 3D mathematical morphological operations. First, the input point cloud is voxelized and indoor empty voxels are extracted by CropHull algorithm. Then, a morphological erosion is performed on the 3D image of indoor empty voxels in order to break connectivity between voxels belonging to adjacent rooms. Remaining voxels after erosion are clustered by a 3D connected components algorithm so that each room is individualized. Room morphology is retrieved by individual 3D morphological dilation on clustered voxels. Finally, unlabelled occupied voxels are classified according proximity to labelled empty voxels after dilation operation. The method was tested in two real cases and segmentation performance was evaluated with encouraging results.