  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 the 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 voxelization 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.
