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Towards the Definition of Workflows for Automation in HBIM Generation

  • Mattia PrevitaliEmail author
  • Fabrizio Banfi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11196)

Abstract

In the last years creation of as-built Building Information Modelling (BIM), and Historic Building Information Modelling (HBIM) in particular, has become a widely researched topic. In particular, the so-called “Scan.-to-BIM” procedure has received a lot of attention. This is mainly given by the fact that nowadays, terrestrial laser scanning (TLS), either static and mobile, and 3D photogrammetry are quite popular techniques to acquire building geometry raw data. However, turning a set of scans into a BIM model is still a labor-intensive and manual work. This paper presents two workflows for increasing the automation in HBIM generation. The presented approaches differ in the level of automation achieved and in the level of maturity. Indeed, while the first one presents a higher level of automation it is designed only to work in the case straight geometrical features are dominant in the scene (i.e., Manhattan world assumption holds). In addition, it is currently implemented in Matlab. On the other hand, the second one is closer to semi-automated modelling since some manual operations are still needed. However, it is implemented as a Revit Plug-in and for this reason it is more user-friendly.

Keywords

HBIM Automation Segmentation Point cloud Add-in 

Notes

Acknowledgements

Research leading to this results is partially funded by Regione Lombardia - Bando “Smart Living: integrazione fra produzione servizi e tecnologia nella filiera costruzioni-legno-arredo-casa” approvato con d.d.u.o. n.11672 dell’15 novembre 2016 nell’ambito del progetto “HOMeBIM liveAPP: Sviluppo di una Live APP multi-utente della realtà virtuale abitativa 4D per il miglioramento di comfort-efficienza-costi, da una piattaforma cloud che controlla nel tempo il flusso BIM-sensori – ID 379270”.

References

  1. 1.
    Volk, R., Stengel, J., Schultmann, F.: Building information modeling (BIM) for existing buildings—literature review and future needs. Autom. Constr. 38, 109–127 (2014)CrossRefGoogle Scholar
  2. 2.
    Meschini, S., Iturralde, K., Linner, T., Bock, T.: Novel applications offered by integration of robotic tools in BIM-based design workflow for automation in construction processes. In: Advanced Construction and Building Technology for Society, p. 59 (2016)Google Scholar
  3. 3.
    Banfi, F.: Building information modelling – a novel parametric modeling approach based on 3D surveys of historic architecture. In: Ioannides, M., et al. (eds.) EuroMed 2016. LNCS, vol. 10058, pp. 116–127. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48496-9_10CrossRefGoogle Scholar
  4. 4.
    Chiabrando, F., Donato, V., Lo Turco, M., Santagati, C.: Cultural heritage documentation, analysis and management using building information modelling: state of the art and perspectives. In: Ottaviano, E., Pelliccio, A., Gattulli, V. (eds.) Mechatronics for Cultural Heritage and Civil Engineering. ISCASE, vol. 92, pp. 181–202. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-68646-2_8CrossRefGoogle Scholar
  5. 5.
    Macher, H., Landes, T., Grussenmeyer, P.: From point clouds to building information models: 3D semi-automatic reconstruction of indoors of existing buildings. Appl. Sci. 7(10), 1030 (2017)CrossRefGoogle Scholar
  6. 6.
    Bassier, M., Van Genechten, B., Vergauwen, M.: Classification of sensor independent point cloud data of building objects using random forests. J. Build. Eng. (2018)Google Scholar
  7. 7.
    Filin, S., Pfeifer, N.: Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS J. Photogramm. Remote. Sens. 60(2), 71–80 (2006)CrossRefGoogle Scholar
  8. 8.
    Rabbani, T.: Automatic reconstruction of industrial installations using point clouds and images. Publications on Geodesy, vol. 62 (2006)Google Scholar
  9. 9.
    Grilli, E., Menna, F., Remondino, F.: A review of point clouds segmentation and classification algorithms. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 42(2), W3 (2017)Google Scholar
  10. 10.
    Xiao, J., Zhang, J., Adler, B., Zhang, H., Zhang, J.: Three-dimensional point cloud plane segmentation in both structured and unstructured environments. Robot. Auton. Syst. 61(12), 1641–1652 (2013)CrossRefGoogle Scholar
  11. 11.
    Vo, A.V., Truong-Hong, L., Laefer, D.F., Bertolotto, M.: Octree-based region growing for point cloud segmentation. ISPRS J. Photogramm. Remote Sens. 104, 88–100 (2015)CrossRefGoogle Scholar
  12. 12.
    Chen, D., Zhang, L., Mathiopoulos, P.T., Huang, X.: A methodology for automated segmentation and reconstruction of urban 3-D buildings from ALS point clouds. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(10), 4199–4217 (2014)CrossRefGoogle Scholar
  13. 13.
    Poux, F., Hallot, P., Neuville, R., Billen, R.: Smart point cloud: definition and remaining challenges. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4, 119 (2016)CrossRefGoogle Scholar
  14. 14.
    Rabbani, T., Van Den Heuvel, F., Vosselmann, G.: Segmentation of point clouds using smoothness constraint. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36(5), 248–253 (2006)Google Scholar
  15. 15.
    Castillo, E., Liang, J., Zhao, H.: Point cloud segmentation and denoising via constrained nonlinear least squares normal estimates. In: Breuß, M., Bruckstein, A., Maragos, P. (eds.) Innovations for Shape Analysis, pp. 283–299. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-34141-0_13CrossRefGoogle Scholar
  16. 16.
    Weinmann, M., Jutzi, B., Hinz, S., Mallet, C.: Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS J. Photogramm. Remote Sens. 105, 286–304 (2015)CrossRefGoogle Scholar
  17. 17.
    Wang, C., Cho, Y.K., Kim, C.: Automatic BIM component extraction from point clouds of existing buildings for sustainability applications. Autom. Constr. 56, 1–13 (2015)CrossRefGoogle Scholar
  18. 18.
    Autodesk App Store Homepage. https://apps.autodesk.com/it Accessed 17 May 2018
  19. 19.
    Food for Rhino Homepage. http://www.food4rhino.com/. Accessed 17 May 2018
  20. 20.
    Lumion® LiveSync® by Act-3D download webpage in Autodesk App StoreGoogle Scholar
  21. 21.
    BIMobject® by BIMobject download webpage in Autodesk App StoreGoogle Scholar
  22. 22.
    Import/Export Excel by Virtual construction and technology BIM One Inc, download webpage in Autodesk App StoreGoogle Scholar
  23. 23.
    IFC 2018 by Autodesk, Inc. download webpage in Autodesk App StoreGoogle Scholar
  24. 24.
    Advance Steel 2018 Extension Autodesk, Inc. download webpage, in Autodesk App StoreGoogle Scholar
  25. 25.
    Kangaroo Physics by Daniel Piker download webpage, in Food for RhinoGoogle Scholar
  26. 26.
    Lunchbox by Nathan Miller download webpage, in Food for RhinoGoogle Scholar
  27. 27.
    Grasshopper Home page. http://www.grasshopper3d.com/. Accessed 17 May 2018
  28. 28.
    Dynamo Home page. http://dynamobim.org/. Accessed 17 May 2018
  29. 29.
    Banfi, F.: BIM orientation: grades of generation and information for different type of analysis and management process. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 42(2/W5), 57–64 (2017)CrossRefGoogle Scholar
  30. 30.
    Previtali, M., et al.: Automatic façade segmentation for thermal retrofit. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40, 197–204 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Architecture, Built Environment and Construction EngineeringPolitecnico di MilanoMilanItaly

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