Abstract
This paper aims to propose an approach to automated classifying building element instance in BIM using deep learning-based 3D object classification algorithm. Recently, studies related to checking or validating engine of BIM object for ensuring data integrity of BIM instances are getting attention. As a part of this research, this paper train recognition models that are targeted at basic building element and interior element using 3D object recognition technique that uses images of objects as inputs. Object recognition is executed in two stages; (1) class of object (e.g. wall, window, seating furniture, toilet fixture and etc.), (2) sub-type of specific classes (e.g. Toilet or Urinal). Using the trained models, BIM plug-in prototype is developed and the performance of this AI-based approach with test BIM model is checked. We expect this recognition approach to help ensure the integrity of BIM data and contribute to the practical use of BIM.
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
Eastman, C., et al.: Automatic rule-based checking of building designs. Autom. Constr. 18(8), 1011–1033 (2009)
Lee, Y.-C., et al.: Validations for ensuring the interoperability of data exchange of a building information model. Autom. Constr. 58, 176–195 (2015)
Koo, B., Shin, B.: Applying novelty detection to identify model element to IFC class misclassifications on architectural and infrastructure building information models. J. Comput. Design Eng. 5(4), 391–400 (2018)
IFC Solutions Factory: Model View Definitions Site. http://www.blis-project.org/IAI-MVD. Accessed 29 Mar 2019
Weise, M., et al.: Integrating use case definitions for IFC developments, eWork and eBusiness in architecture and construction, pp. 637–645 (2009)
SMC: Solibri Model Checker. http://www.solibri.com. Accessed 29 Mar 2019
Sacks, R., et al.: Semantic enrichment for building information modeling: procedure for compiling inference rules and operators for complex geometry. J. Comput. Civ. Eng. 31(6), 256–272 (2017)
Bloch, T., Sacks, R.: Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Autom. Constr. 91, 256–272 (2018)
Koo, B., et al.: Using support vector machines to classify building elements for checking the semantic integrity of building information models. Autom. Constr. 98, 183–194 (2019)
The Princeton ModelNet. http://modelnet.cs.princeton.edu/. Accessed 29 Mar 2019
Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ International Conference Proceedings of Intelligent Robots and Systems (IROS), pp. 922–928 (2015)
Su, H., et al.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 945–953 (2015)
Krizhevsky A., et al.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)
Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: Proceedings of Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:1603.04467 (2016)
KBIMS Library. http://www.kbims.or.kr/lms. Accessed 29 Mar 2019
BIMObject: BIM Libarary. https://www.bimobject.com/. Accessed 29 Mar 2019
3Dwarehouse. https://3dwarehouse.sketchup.com/. Accessed 29 Mar 2019
Revit and Revit API. https://www.autodesk.co.kr/products/revit/overview. Accessed 29 Mar 2019
Omniclass: a classification system for the construction industry. http://www.omniclass.orgAccessed 29 Mar 2019
Acknowledgement
This research was supported by a grant (19AUDP-B127891-03) from the Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kim, J., Song, J., Lee, JK. (2019). Recognizing and Classifying Unknown Object in BIM Using 2D CNN. In: Lee, JH. (eds) Computer-Aided Architectural Design. "Hello, Culture". CAAD Futures 2019. Communications in Computer and Information Science, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-13-8410-3_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-8410-3_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8409-7
Online ISBN: 978-981-13-8410-3
eBook Packages: Computer ScienceComputer Science (R0)