Abstract
Generating as-is 3D Models is constantly explored for various construction management applications. The industry has been dependent on either manual or semi-automated workflows for the Scan-to-BIM process, which is laborious as well as time taking. Recently machine learning has opened avenues to recognize geometrical elements from point clouds but has not been much used because of the insufficient labeled dataset. This study aims to set up a semi-automated workflow to create labeled data sets which can be used to train ML algorithms for element identification purpose. The study proposes an interactive user interface using a gaming engine within a mixed reality environment. A workflow for fusing as-is spatial information with the AR/VR based information is presented in Unity 3D. A user-friendly UI is then developed and integrated with the VR environment to help the user to choose the category of the component by visualization. This results in the generation of an accurate as-is 3D Model, which does not require much computation or time. The intention is to propose a smooth workflow to generate datasets for learning-based methodologies in a streamlined Scan-to-BIM Process. However, this process requires user domain knowledge and input. The dataset can be continuously increased and improved to get automated results later.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings - 30th IEEE Conference on Computing Vision and Pattern Recognition, CVPR 2017, January 2017, pp. 2432–2443 (2017)
Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: Proceedings of IEEE Computer Society Conference on Computing Vision and Pattern Recognition, December 2016, pp. 1534–1543 (2016)
Ochmann, S., Vock, R., Klein, R.: Automatic reconstruction of fully volumetric 3D building models from oriented point clouds. ISPRS J. Photogramm. Remote Sens. 151, 251–262 (2019)
Son, H., Kim, C.: Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity. Adv. Eng. Informatics 34, 114–124 (2017)
Banfi, F., Fai, S., Brumana, R.: BIM automation: advanced modeling generative process for complex structures. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4(2W2), 9–16 (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances Neural Information Processing System, December 2017, pp. 5100–5109 (2017)
Liu, C., Wu, J., Furukawa, Y.: FloorNet: a unified framework for floorplan reconstruction from 3D Scans. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 203–219. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_13
Chen, J., Kira, Z., Cho, Y.K.: Deep learning approach to point cloud scene understanding for automated scan to 3D reconstruction. J. Comput. Civ. Eng. 33(4) (2019)
Lei, H., Akhtar, N., Mian, A.: SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel, pp. 11608–11617 (2020)
Braun, A., Tuttas, S., Borrmann, A., Stilla, U.: Improving progress monitoring by fusing point clouds, semantic data and computer vision. Autom. Constr. 116, 103210 (2020)
Yuan, L., Guo, J., Wang, Q.: Automatic classification of common building materials from 3D terrestrial laser scan data. Autom. Constr. 110, 103017 (2020)
Ma, J.W., Czerniawski, T., Leite, F.: Semantic segmentation of point clouds of building interiors with deep learning: augmenting training datasets with synthetic BIM-based point clouds. Autom. Constr. 113, 103144 (2020)
Zeng, S., Chen, J., Cho, Y.K.: User exemplar-based building element retrieval from raw point clouds using deep point-level features. Autom. Constr. 114, 103159 (2020)
Zollmann, S., Hoppe, C., Kluckner, S., Poglitsch, C., Bischof, H., Reitmayr, G.: Augmented reality for construction site monitoring and documentation. Proc. IEEE 102(2), 137–154 (2014)
Golparvar-Fard, M., Peña-Mora, F., Savarese, S.: Integrated sequential as-built and as-planned representation with D4AR tools in support of decision-making tasks in the AEC/FM industry. J. Constr. Eng. Manag. 137(12), 1099–1116 (2011)
Kopsida, M., Brilakis, I.: Real-time volume-to-plane comparison for mixed reality-based progress monitoring. J. Comput. Civ. Eng. 34(4), 1–5 (2020)
Ferdani, D., Fanini, B., Piccioli, M.C., Carboni, F., Vigliarolo, P.: 3D reconstruction and validation of historical background for immersive VR applications and games: the case study of the Forum of Augustus in Rome. J. Cult. Herit. 43, 129–143 (2020)
Pour Rahimian, F., Seyedzadeh, S., Oliver, S., Rodriguez, S., Dawood, N.: On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Autom. Constr. 110, 103012 (2020)
Christopoulou, E., Xinogalos, S.: Overview and comparative analysis of game engines for desktop and mobile devices. Int. J. Serious Games 4(4), 21–36 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bhadaniya, P., Reja, V.K., Varghese, K. (2021). Mixed Reality-Based Dataset Generation for Learning-Based Scan-to-BIM. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-68787-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68786-1
Online ISBN: 978-3-030-68787-8
eBook Packages: Computer ScienceComputer Science (R0)
