Toward Intelligent Agents to Detect Work Pieces and Processes in Modular Construction: An Approach to Generate Synthetic Training Data
Publication: Construction Research Congress 2022
ABSTRACT
Modular construction has been an alternative to traditional construction processes to reduce environmental impact and construction waste as well as to deal with space constraints in highly dense urban construction sites. Furthermore, since modules are pre-fabricated in a controlled environment, modular construction has the advantage to achieve automation and optimization as compared to traditional construction. However, due to the one-of-a-type nature of construction projects, automation in construction is still in its infancy as compared to other manufacturing industries. Meanwhile, recently, advancements in technologies such as computer vision and deep learning provide opportunities to train machine intelligence to solve problems that were not possible before. In this study, we propose an approach to automatically generate high-resolution synthetic training data for scene understanding in the modular construction context. Evaluation of the approach in testbed factory settings shows that we can systematically capture and label AEC components such as walls and doors on RGB-D images as synthetic datasets for applications of supervised learning in relation to modular construction. The proposed method can provide a mechanism to feed the necessary but missing large-scale datasets to train scene understanding models in modular construction factories as modular projects and corresponding workpieces change.
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REFERENCES
Lawson, R. M., Ogden, R. G., and Bergin, R. (2012). Application of modular construction in high-rise buildings. Journal of architectural engineering, 18(2), 148–154.
Bock, T. (2015). The future of construction automation: Technological disruption and the upcoming ubiquity of robotics. Automation in Construction, 59, 113–121.
Ng, A. (2021). “Issue 84”. Retrieved from https://www.deeplearning.ai/the-batch/issue-84/, access date: March 24, 2021.
Smith, L. B. (2005). Cognition as a dynamic system: Principles from embodiment. Developmental Review, 25(3-4), 278–298.
Armeni, I., He, Z. Y., Gwak, J., Zamir, A. R., Fischer, M., Malik, J., and Savarese, S. (2019). 3d scene graph: A structure for unified semantics, 3d space, and camera. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5664–5673).
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Gorden, D., Zhu, Y., Gupta, A., and Farhadi, A. (2017). Ai2-thor: An interactive 3d environment for visual ai.
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., and Zhang, Y. (2017). Matterport3d: Learning from rgb-d data in indoor environments.
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., and Savarese, S. (2018). Gibson env: Real-world perception for embodied agents. In Proceedings of the IEEE on CVPR (pp. 9068–9079).
Liu, C., Wu, J., and Furukawa, Y. (2018). Floornet: A unified framework for floorplan reconstruction from 3d scans. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 201–217).
Dai, A., Siddiqui, Y., Thies, J., Valentin, J., and Nießner, M. (2020). Spsg: Self-supervised photometric scene generation from rgb-d scans.
Dai, A., Ritchie, D., Bokeloh, M., Reed, S., Sturm, J., and Nießner, M. (2018). Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4578–4587).
Liu, S., Hu, Y., Zeng, Y., Tang, Q., Jin, B., Han, Y., and Li, X. (2018, December). See and think: Disentangling semantic scene completion. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (pp. 261–272).
Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., Parikh, D., and Batra, D. (2019). Habitat: A platform for embodied ai research. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9339–9347).
Koo, B., Jung, R., and Yu, Y. (2021). Automatic classification of wall and door BIM element subtypes using 3D geometric deep neural networks. Advanced Engineering Informatics, 47, 101200.
Li, Z., Wang, H., and Li, J. (2020). Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition.
Hoiem, D., Hays, J., Xiao, J., and Khosla, A. (2015). Guest editorial: Scene understanding. International Journal of Computer Vision, 112(2), 131–132.
Park, K., and Ergan, S. (2021). Towards Intelligent Agents to Assist in Modular Construction: Evaluation of Datasets Generated in Virtual Environments for AI training. 38th International Symposium on Automation and Robotics in Construction (submitted).
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Published online: Mar 7, 2022
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Cited by
- Roshan Panahi, Joseph Louis, Ankur Podder, Colby Swanson, Shanti Pless, Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation, Computing in Civil Engineering 2023, 10.1061/9780784485224.036, (290-297), (2024).