Skip to main content

Towards the Definition of Workflows for Automation in HBIM Generation

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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)

    Article  Google Scholar 

  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. 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_10

    Chapter  Google Scholar 

  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_8

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  8. Rabbani, T.: Automatic reconstruction of industrial installations using point clouds and images. Publications on Geodesy, vol. 62 (2006)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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_13

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Autodesk App Store Homepage. https://apps.autodesk.com/it Accessed 17 May 2018

  19. Food for Rhino Homepage. http://www.food4rhino.com/. Accessed 17 May 2018

  20. Lumion® LiveSync® by Act-3D download webpage in Autodesk App Store

    Google Scholar 

  21. BIMobject® by BIMobject download webpage in Autodesk App Store

    Google Scholar 

  22. Import/Export Excel by Virtual construction and technology BIM One Inc, download webpage in Autodesk App Store

    Google Scholar 

  23. IFC 2018 by Autodesk, Inc. download webpage in Autodesk App Store

    Google Scholar 

  24. Advance Steel 2018 Extension Autodesk, Inc. download webpage, in Autodesk App Store

    Google Scholar 

  25. Kangaroo Physics by Daniel Piker download webpage, in Food for Rhino

    Google Scholar 

  26. Lunchbox by Nathan Miller download webpage, in Food for Rhino

    Google Scholar 

  27. Grasshopper Home page. http://www.grasshopper3d.com/. Accessed 17 May 2018

  28. Dynamo Home page. http://dynamobim.org/. Accessed 17 May 2018

  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)

    Article  Google Scholar 

  30. Previtali, M., et al.: Automatic façade segmentation for thermal retrofit. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40, 197–204 (2013)

    Article  Google Scholar 

Download references

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”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mattia Previtali .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Previtali, M., Banfi, F. (2018). Towards the Definition of Workflows for Automation in HBIM Generation. In: Ioannides, M., et al. Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2018. Lecture Notes in Computer Science(), vol 11196. Springer, Cham. https://doi.org/10.1007/978-3-030-01762-0_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01762-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01761-3

  • Online ISBN: 978-3-030-01762-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics