Machine Learning Segmentation and Classification Algorithm to Support Simulated Point Cloud As-Built Model Applications
Publication: Computing in Civil Engineering 2021
ABSTRACT
Management and maintenance of existing buildings remains a major problem due to the lack of existing three-dimensional (3D) models and accurate as-built representation. In this paper the authors propose to use 3D scanner technology to capture the as-built and existing conditions of the buildings combined with building information modeling (BIM) as the underlying technology, which is a 3D semantic representation of all the life cycle phases of a building. This paper presents the results from creating as-built BIM models of existing buildings, using point cloud (a set of points in 3D space) and machine learning as an intermediate medium. Machine learning methodologies are used to speed up the computation of segmentation and classification of point clouds from a 3D virtual indoor environment using procedural modeling, which focused on two attributes, point density and the level of random errors. In this paper we will present findings on the evaluation of the performance of machine learning segmentation and classification algorithm based on the comparison of ten different point cloud data sets. Different sets of segmentation and classification models with comparison between models and within themselves were provided, which included the mean loss and accuracy between models with different point density.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Bueno, M., Bosché, F., González-Jorge, H., Martínez-Sánchez, J., and Arias, P. (2018). 4-Plane congruent sets for automatic registration of as-is 3D point clouds with 3D BIM models. Automation in Construction, 89(January), 120–134.
Bechtold, S., and Höfle, B. (2016). HELIOS: A multi-purpose LiDAR simulation framework for research, planning and training with airborne, ground-based mobile and stationary platforms. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(June), 161–168. https://doi.org/10.5194/isprs-annals-III-3-161-2016.
Qi, C. R., Su, H., Mo, K., and Guibas, L. J. (2017). Stanford University. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
Eastman, C. M. (2011). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors, Wiley.
Eastman, C., Teicholz, P., Sacks, R., and Liston, K. (2011). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Architects, Engineers, Contractors, and Fabricators, Wiley, Hoboken, NJ.
Dimitrov, A., and Golparvar-Fard, M. (2015). Segmentation of building point cloud models including detailed architectural/structural features and MEP systems. Automation in Construction, 51(C), 32–45. https://doi.org/10.1016/j.autcon.2014.12.015.
Grilli, E., Menna, F., and Remondino, F. (2017). A review of point clouds segmentation and classification algorithms. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2W3), 339–344. https://doi.org/10.5194/isprs-archives-XLII-2-W3-339-2017.
Jagannathan, A., and Miller, E. L. (2007). Three-dimensional surface mesh segmentation using curvedness-based region growing approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29(12), pp. 2195–2204.
Jung, J., Hong, S., Jeong, S., Kim, S., Cho, H., Hong, S., and Heo, J. (2014). Productive modeling for development of as-built BIM of existing indoor structures. Automation in Construction, 42, 68–77.
Kukko, A., and Hyypp¨a, J. (2009). Small-footprint Laser Scanning Simulator for System Validation, Error Assessment, and Algorithm Development. Photogrammetric Engineering and Remote Sensing 75(10), pp. 1177–1189.
Lari, Z. (2014). Adaptive Processing of Laser Scanning Data and Texturing of the Segmentation Outcome. https://doi.org/http://hdl.handle.net/11023/1672.
Li, D., Liu, J., Feng, L., Zhou, Y., Liu, P., and Chen, Y. F. (2020). Terrestrial laser scanning assisted flatness quality assessment for two different types of concrete surfaces. Measurement, 154, 107436. https://doi.org/10.1016/j.measurement.2019.107436.
Lohani, B., and Mishra, R. K. (2007). Generating lidar data in laboratory: Lidar simulator. In: International Archive of Photogrammetry and Remote Sensing XXXVI(3)W52 of Laser Scanning 2007 and SilviLaser 2007, p. 6.
Rabbani, T., Van Den Heuvel, F., and Vosselmann, G. (2006). Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36(5), pp. 248–253.
Rottensteiner, F., Sohn, G., Gerke, M., Wegner, J. D., Breitkopf, U., and Jung, J. (2014). Results of the ISPRS benchmark on urban object detection and 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 256–271. https://doi.org/10.1016/J.ISPRSJPRS.2013.10.004.
Pătrăucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., and Haas, C. (2015). State of research in automatic as-built modelling, Adv. Eng. Inf. 29: 162–171.
Valero, E., Bosché, F., and Forster, A. (2018). Automatic segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance. Automation in Construction, 96(September), 29–39.
Wang, Y. B., Xie, D. B., Yan, G. B., Zhang, W. B., and Mu, X. B. (2013). Analysis on the inversion accuracy of LAI based on simulated point clouds of terrestrial LiDAR of tree by ray tracing algorithm. International Geoscience and Remote Sensing Symposium (IGARSS) pp. 532–535.
Information & Authors
Information
Published In
Computing in Civil Engineering 2021
Pages: 942 - 949
History
Published online: May 24, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.