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.
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Volk, R., Stengel, J., Schultmann, F.: Building information modeling (BIM) for existing buildings—literature review and future needs. Autom. Constr. 38, 109–127 (2014)
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)
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
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
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)
Bassier, M., Van Genechten, B., Vergauwen, M.: Classification of sensor independent point cloud data of building objects using random forests. J. Build. Eng. (2018)
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)
Rabbani, T.: Automatic reconstruction of industrial installations using point clouds and images. Publications on Geodesy, vol. 62 (2006)
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)
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)
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)
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)
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)
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)
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
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)
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)
Autodesk App Store Homepage. https://apps.autodesk.com/it Accessed 17 May 2018
Food for Rhino Homepage. http://www.food4rhino.com/. Accessed 17 May 2018
Lumion® LiveSync® by Act-3D download webpage in Autodesk App Store
BIMobject® by BIMobject download webpage in Autodesk App Store
Import/Export Excel by Virtual construction and technology BIM One Inc, download webpage in Autodesk App Store
IFC 2018 by Autodesk, Inc. download webpage in Autodesk App Store
Advance Steel 2018 Extension Autodesk, Inc. download webpage, in Autodesk App Store
Kangaroo Physics by Daniel Piker download webpage, in Food for Rhino
Lunchbox by Nathan Miller download webpage, in Food for Rhino
Grasshopper Home page. http://www.grasshopper3d.com/. Accessed 17 May 2018
Dynamo Home page. http://dynamobim.org/. Accessed 17 May 2018
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)
Previtali, M., et al.: Automatic façade segmentation for thermal retrofit. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40, 197–204 (2013)
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”.
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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
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