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3DFacilities: Annotated 3D Reconstructions of Building Facilities

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

Scan-to-BIM is the process of converting 3D reconstructions into building information models (BIM). Currently, it involves manual tracing of point clouds by human users in BIM authoring tools, with some automation functionality available for walls, floors, windows, doors, and piping. Emerging semantic segmentation methods demonstrate a level of versatility that could extend the capabilities of automated Scan-to-BIM well past the limited existing object categories. The accuracy of supervised deep learning methods in the context of 3D scene segmentation has experienced rapid improvement over the past year due to the recent availability of large, annotated datasets of indoor spaces. Unfortunately, the semantic object categories in the available datasets do not cover many essential BIM object categories, such as heating, ventilation and air-conditioning (HVAC), and plumbing systems. In an effort to leverage the success of deep learning for Scan-to-BIM, we present 3DFacilities, an annotated dataset of 3D reconstructions of building facilities. The dataset contains over 11,000 individual RGB-D frames comprising 50 scene reconstructions annotated with 3D camera poses and per-vertex and per-pixel annotations. Our dataset is available at https://thomasczerniawski.com/3dfacilities/.

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Notes

  1. 1.

    Model Uses List, http://bimexcellence.com/model-uses/, last accessed 2018/01/15.

  2. 2.

    Large Scale Visual Recognition Challenge 2012: All results, http://www.image-net.org/challenges/LSVRC/2012/results.html, last accessed 2018/01/15.

  3. 3.

    Structure sensor home page, https://structure.io/, last accessed 2018/01/15.

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Acknowledgments

This research was supported, in part, by the National Science Foundation (NSF) under award number 1562438. Their support is gratefully acknowledged. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Mention of trade names in this article does not imply endorsement by the University of Texas at Austin or NSF.

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Authors and Affiliations

  1. University of Texas at Austin, Austin, TX, 78712, USA

    Thomas Czerniawski & Fernanda Leite

Authors
  1. Thomas Czerniawski
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  2. Fernanda Leite
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Correspondence to Thomas Czerniawski .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Czerniawski, T., Leite, F. (2018). 3DFacilities: Annotated 3D Reconstructions of Building Facilities. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_10

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  • Online ISBN: 978-3-319-91635-4

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