BIM Voxelization Method Supporting Cell-Based Creation of a Path-Planning Environment

Wang, Qiankun; Zuo, Weiwei; Guo, Zeng; Li, Qianyao; Mei, Tingting; Qiao, Songbo

doi:10.1061/(asce)co.1943-7862.0001864

Cited by 14 publications

(2 citation statements)

References 36 publications

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“…Conversion of the results towards actual surface or even volume geometries in Euclidean space would be a valuable extension, providing the means for the automated generation of actual BIM models from indoor mapping data and enabling better comparability of the results with those of other reconstruction methods. Still, we believe that voxels hold great potential for building-related analysis tasks (Gorte et al, 2019;Song et al, 2019;Wang et al, 2020).…”

Section: Discussionmentioning

confidence: 98%

Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

Hübner

Wursthorn

Weinmann

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, we present an automated method for classification of binary voxel occupancy grids of discretized indoor mapping data such as point clouds or triangle meshes according to normal vector directions. Filled voxels get assigned normal class labels distinguishing between horizontal and vertical building structures. The horizontal building structures are further differentiated into those with normal directions pointing upwards or downwards with respect to the building interior. The derived normal grids can be deployed in the context of an existing voxel-based indoor reconstruction pipeline, which so far was only applicable to indoor mapping triangle meshes that already contain normal vectors consistently oriented with respect to the building interior. By means of quantitative evaluation against reference data, we demonstrate the performance of the proposed method and its applicability in the context of voxel-based indoor reconstruction from indoor mapping point clouds without normal vectors. The code of our implementation is made available to the public at https://github.com/huepat/voxir.

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Section: Discussionmentioning

confidence: 98%

Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

Hübner

Wursthorn

Weinmann

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…RichVoxel models [6] as a common representation to match the different 3D city/building models Voxels in the field of 3D city modelling and urban research are not a new concept. Voxels have been used in various BIM/GIS applications such as path planning as seen in [9], as volumetric modelling primitives [10], for space planning and semantic segmentation of 3D point clouds, [12], etc. Further analysis using voxels is also popular in many applications such as thermal analysis of buildings where voxelised IFC models have been used which is seen in [13] or in [12] where voxels have been used to semantically segment 3D point clouds.…”

Section: Digital Twin Data Consistency Across Multiple Representationsmentioning

confidence: 99%

Artificial Intelligence for the Automated Creation of Multi-scale Digital Twins of the Built World—AI4TWINNING

Borrmann,

Biswanath,

Braun

et al. 2024

Lecture Notes in Geoinformation and Cartography

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The AI4TWINNING project aims at the automated generation of a system of inter-related digital twins of the built environment spanning multiple resolution scales providing rich semantics and coherent geometry. To this end, an interdisciplinary group of researchers develops a multi-scale, multi-sensor, multi-method approach combining terrestrial, airborne, and spaceborne acquisition, different sensor types (visible, thermal, LiDAR, Radar) and different processing methods integrating top-down and bottom-up AI approaches. The key concept of the project lies in intelligently fusing the data from different sources by AI-based methods, thus closing information gaps and increasing completeness, accuracy and reliance of the resulting digital twins. To facilitate the process and improve the results, the project makes extensive use of informed machine learning by exploiting explicit knowledge on the design and construction of built facilities. The final goal of the project is not to create a single monolithic digital twin, but instead a system of interlinked twins across different scales, providing the opportunity to seamlessly blend city, district and building models while keeping them up-to-date and consistent. As testbed and demonstration scenario serves a urban zone around the city campus of TUM, for which large data sets from various sensors are available.

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Pathfinding method for an indoor drone based on a BIM-semantic model

Chen

Huang

2022

Advanced Engineering Informatics

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BIM Voxelization Method Supporting Cell-Based Creation of a Path-Planning Environment

Cited by 14 publications

References 36 publications

Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

Normal Classification of 3d Occupancy Grids for Voxel-Based Indoor Reconstruction From Point Clouds

Artificial Intelligence for the Automated Creation of Multi-scale Digital Twins of the Built World—AI4TWINNING

Pathfinding method for an indoor drone based on a BIM-semantic model

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