CLOI: An Automated Benchmark Framework for Generating Geometric Digital Twins of Industrial Facilities
Publication: Computing in Civil Engineering 2021
ABSTRACT
This paper devises, implements, and benchmarks a novel framework, named CLOI, that can accurately generate individual labeled point clusters of the most important shapes of existing industrial facilities with minimal manual effort in a generic point-level format. CLOI employs a combination of deep learning and geometric methods to segment the points into classes and individual instances. The current geometric digital twin generation from point cloud data in commercial software is a tedious, manual process. Experiments with our CLOI framework reveal that the method can reliably segment complex and incomplete point clouds of industrial facilities, yielding 82% class segmentation accuracy. Compared to the current state-of-practice, the proposed framework can realize estimated time-savings of 30% on average. CLOI is the first framework to have achieved geometric digital twinning for the most important objects of industrial factories and provides the foundation for the generation of semantically enriched industrial digital twins.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
NIST. (2018). The Costs and Benefits of Advanced Maintenance in Manufacturing,. US Department of Commerce.
West, T., and Blackburn, M. (2017). “Is Digital Thread/Digital Twin Affordable? A Systemic Assessment of the Cost of DoD’s Latest Manhattan Project.” Procedia Computer Science, 114, 47–56.
Bauer, F. L., and Wössner, H. (1972). “The “Plankalkül” of Konrad Zuse: A Forerunner of Today’s Programming Languages.” Communications of the ACM.
Borrmann, A., and Berkhahn, V. (2018). “Principles of geometric modeling.” Building Information Modeling, Springer, 27–41.
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.
Hullo, J.-F., Thibault, G., Boucheny, C., Dory, F., and Mas, A. (2015). “Multi-Sensor As-Built Models of Complex Industrial Architectures.” Remote Sensing, 7(12), 16339–16362.
Agapaki, E., Miatt, G., and Brilakis, I. (2018). “Prioritizing object types for modelling existing industrial facilities.” Automation in Construction.
Agapaki, E., and Brilakis, I. (2020a). “Cloi-net: Class segmentation of industrial facilities’ pointcloud datasets.”Advanced Engineering Informatics, 45, 101121.
Agapaki, E., and Brilakis, I. (2020b). “Instance segmentation of industrial point cloud data, https://arxiv.org/abs/2012.14253.
Agapaki, E., Glyn-Davies, A., Mandoki, S., and Brilakis, I. (2019). “CLOI: A Shape Classification Benchmark Dataset for Industrial Facilities.”2019 ASCE International Conference on Computing in Civil Engineering.
Agapaki, E., and Nahangi, M. (2020). “Chapter 3 - Scene understanding and model generation.”Infrastructure Computer Vision, I. Brilakis and C. Haas, eds., Elsevier.
Li, B., Shi, Y., Qi, Z., and Chen, Z. (2019). “A survey on semantic segmentation.” IEEE Interna-tional Conference on Data Mining Workshops, ICDMW.
Perez-Perez, Y., Golparvar-Fard, M., and El-Rayes, K. (2016). “Semantic and Geometric Labeling for Enhanced 3D Point Cloud Segmentation.” Construction Research Congress 2016, 2542–2552.
Rusu, R. B., Blodow, N., Marton, Z. C., and Beetz, M. (2009). “Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments.”2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, 1–6.
Qi, C. R., Yi, L., Su, H., and Guibas, L. J. (2017a). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Computer Vision and Pattern Recognition (CVPR).
Qi, R., Su, H., Mo, K., and Guibas, L. J. (2017b). PointNET: Deep Learning on Point Sets for 3DClassification and Segmentation. Computer Vision and Pattern Recognition (CVPR).
Wang, W., Yu, R., Huang, Q., and Neumann, U. (2018). SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation. Computer Vision and Pattern Recognition.
Wang, X., Shen, X., Shen, C., and Jia, J. (2019). Associatively Segmenting Instances and Semantics in Point Clouds. CVPR.
Zhang, J., Huang, Q., and Peng, X. (2015). “3D Reconstruction of Indoor Environment Using the Kinect Sensor.”2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 538–541 (9).
Huang, J., and You, S. (2013). “Detecting objects in scene point cloud: A combinational approach. ”Proceedings - 2013 International Conference on 3D Vision, 175–182.
Armeni, I., Sener, O., Jiang, H., Fischer, M., and Savarese, S. (2016). “3D Semantic Parsing of Large-Scale Indoor Spaces.” Proceedings of the IEEE CVPR, 1534–1543.
Information & Authors
Information
Published In
Computing in Civil Engineering 2021
Pages: 546 - 553
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.