ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 79–86, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-79-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 79–86, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-79-2020

  03 Aug 2020

03 Aug 2020

VOXEL-BASED INDOOR RECONSTRUCTION FROM HOLOLENS TRIANGLE MESHES

P. Hübner, M. Weinmann, and S. Wursthorn P. Hübner et al.
  • Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Keywords: Indoor Reconstruction, HoloLens, Triangle Meshes, Voxels, 3D, Classification

Abstract. Current mobile augmented reality devices are often equipped with range sensors. The Microsoft HoloLens for instance is equipped with a Time-of-Flight (ToF) range camera providing coarse triangle meshes that can be used in custom applications. We suggest to use these triangle meshes for the automatic generation of indoor models that can serve as basis for augmenting their physical counterpart with location-dependent information. In this paper, we present a novel voxel-based approach for automated indoor reconstruction from unstructured three-dimensional geometries like triangle meshes. After an initial voxelisation of the input data, rooms are detected in the resulting voxel grid by segmenting connected voxel components of ceiling candidates and extruding them downwards to find floor candidates. Semantic class labels like ’Wall’, ’Wall Opening’, ’Interior Object’ and ’Empty Interior’ are then assigned to the room voxels in-between ceiling and floor by a rule-based voxel sweep algorithm. Finally, the geometry of the detected walls and their openings is refined in voxel representation. The proposed approach is not restricted to Manhattan World scenarios and does not rely on room surfaces being planar.