All-Automatic 3D BIM Modeling of Existing Buildings

  • D. BenarabEmail author
  • W. Derigent
  • D. Brie
  • V. Bombardier
  • A. Thomas
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


In order to ensure a reliable building life cycle management, it is essential to generate an accurate and up-to-date referential mock-up that will be used for renovation, extension and maintenance. Based on this statement, we carried out, in a previous work, a research study in the sake of reconstructing a 3D CAD model from a point cloud acquired using a Lidar. This point cloud is processed automatically to detect planes and contours and to generate the 3D CAD model. However, during the life cycle of the project, different actors from different fields intervene on the building, which creates several communication conflicts, and this means a loss of time, energy and money. In order to ensure a constructive collaboration and a simplified data exchange between the different contributors in the building, we continued our work to propose, in this paper, an automatic conversion of a point cloud to a 3D BIM file. This conversion induces the passage to the standard IFC format and the integration of a valuable knowledge in it. To do so, an automatic classification of contours into architectural elements is proposed. It consists in defining a hierarchical classification rule reproducing the human reasoning for classifying the architectural elements. Then, based on the classified set of polygons, an automatic generation of 3D IFC model is proposed.


Point cloud 3D reconstruction Mock-up CAD Entity classification IFC BIM PLM Calibration 


  1. 1.
    Andrey, D., Mani, G.: Segmentation of building point cloud models including detailed architectural/structural features and MEP systems. Autom. Constr. 51, 32–45 (2015). Scholar
  2. 2.
    Hélène, M.: From point cloud to building information model (BIM). 3D semi-automatic reconstruction of existing buildings. University of Strasbourg (2017)Google Scholar
  3. 3.
    Thomson, C., Boehm, J.: Automatic geometry generation from point clouds for BIM. Remote Sens. 7(9), 11753–11775 (2015). Scholar
  4. 4.
    Barnea, S., Filin, S.: Segmentation of terrestrial laser scanning data using geometry and image information. ISPRS J. Photogramm. Remote Sens. 76, 33–48 (2013). Scholar
  5. 5.
    Budroni, A., Boehm, J.: Automated 3D reconstruction of interiors from point clouds. Int. J. Archit. Comput. 8, 55–73 (2010). Scholar
  6. 6.
    Adan, A., Huber, D.: 3D reconstruction of interior wall surfaces under occlusion and clutter. In: Proceedings of the IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), China, pp. 275–281 (2011).
  7. 7.
    Brie, D., Bombardier, V., Baeteman, G., Bennis, A.: Local surface sampling step estimation for extracting boundaries of planar point clouds. ISPRS J. Photogramm. Remote Sens. 119, 309–319 (2016). Scholar
  8. 8.
    Benis, A.: Contribution to the 3D reconstruction of buildings using point cloud of terrestrial laser scanning. University of Lorraine (2015)Google Scholar
  9. 9.
    Frueh, C., Jain, S., Zakhor, A.: Data processing algorithms for generating textured 3D building facade meshes from laser scans and camera images. Int. J. Comput. Vis. 61(2), 159–184 (2005). Scholar
  10. 10.
    Pu, S., Vosselman, G.: Building facade reconstruction by fusing terrestrial laser points and images. ISPRS J. Photogram. Remote Sens. 64, 2525–4542 (2009). Scholar
  11. 11.
    Böhm, J., Becker, S., Haala, N.: Model refinement by integrated processing of laser scanning and photogrammetry. In: Proceedings of the 3D Virtual Reconstruction and Visualization of Complex Architectures (3D-Arch) (2007)Google Scholar
  12. 12.
    Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3D building models from laser scanner data. Autom. Constr. 31, 325–337 (2013). Scholar
  13. 13.
    Jung, J., et al.: Productive modeling for development of as-built BIM of existing indoor structures. Autom. Constr. 42, 68–77 (2014). Scholar
  14. 14.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981). Scholar
  15. 15.
    Edelsbrunner, H., Kirkpatrick, D.G., Seidel, R.: On the shape of a set of points in the plane. IEEE Trans. Inf. Theory 29, 551–559 (1983). Scholar
  16. 16.
    Edelsbrunner, H., Mucke, E.P.: Three-dimensional alpha shapes. ACM Trans. Graph. 13(1), 43–72 (1994). Scholar
  17. 17.
    ISO.: Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries (2013)Google Scholar
  18. 18.
    Pratt, M.J.: ISO 10303, the STEP standard for product data exchange, and its PLM capabilities. Int. J. Prod. Lifecycle Manag. 1(1), 86 (2005). Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • D. Benarab
    • 1
    • 2
    Email author
  • W. Derigent
    • 1
    • 2
  • D. Brie
    • 1
    • 2
  • V. Bombardier
    • 1
    • 2
  • A. Thomas
    • 1
    • 2
  1. 1.Université de Lorraine, CRAN, UMR 7039Vandœuvre-lès-NancyFrance
  2. 2.CNRS, CRAN, UMR 7039Vandœuvre-lès-NancyFrance

Personalised recommendations