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• W3204718685 abstract "With the gaining popularity and proliferation of building information modeling (BIM) techniques, a growing demand emerges for accurate, up-to-date and semantically-enriched digital representations of built environments. In this regard, current mobile indoor mapping systems like the Microsoft HoloLens or Matterport allow to efficiently acquire triangle meshes of indoor building environments. However, manually reconstructing digital models of building interiors on the basis of these triangle meshes is a cumbersome and time-consuming task. Consequently, in this work, we propose a fully automatic, voxel-based indoor reconstruction approach to derive semantically-enriched and geometrically completed indoor models in voxel representation from unstructured triangle meshes. The presented approach does not require room surfaces such as walls, ceilings or floors to be planar or aligned with the coordinate axes. Furthermore, it does not rely on a clear vertical subdivision in distinct floor levels and even allows for slanted floors such as ramps or stair flights. It thus can also be applied to challenging indoor environments featuring curved room surfaces and complex vertical room layouts. The proposed approach labels voxels as ‘Ceiling’, ‘Floor’, ‘Wall’, ‘Wall Opening’, ‘Interior Object’ and ‘Empty Interior’. Room surfaces are geometrically completed in case of holes in the input triangle meshes caused by occlusion or incomplete mapping. Furthermore, the derived interior space is partitioned into rooms and connecting transition spaces. To demonstrate the performance of our approach, we conduct a thorough quantitative evaluation on four labeled benchmark datasets. To this aim, we present a novel and adequate, automatic evaluation method. The four datasets have been acquired with the Microsoft HoloLens and are available along with the manually modeled ground truth. We also release the code of our implementation of the voxel-based indoor reconstruction approach presented in this paper as well as the code for the automated evaluation against the ground truth data at https://github.com/huepat/voxir." @default.
• W3204718685 created "2021-10-11" @default.
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• W3204718685 date "2021-11-01" @default.
• W3204718685 modified "2023-10-16" @default.
• W3204718685 title "Automatic voxel-based 3D indoor reconstruction and room partitioning from triangle meshes" @default.
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• W3204718685 doi "https://doi.org/10.1016/j.isprsjprs.2021.07.002" @default.
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