Skip to main content

2023 | OriginalPaper | Buchkapitel

Applications of 4D Point Clouds (4DPC) in Digital Twin Construction: A SWOT Analysis

verfasst von : Dong Liang, Fan Xue

Erschienen in: Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Digital twin construction (DTC), the virtual replica of physical construction, is an essential enabler for the promised Construction 4.0. Thanks to its real-time virtual-physical synchronicity, many uncertainty-tolerant functions, and applications, such as system integration, testing, monitoring, and maintenance, could be dynamically simulated and optimized through the DTC. The data foundation for creating or updating a DTC is real-time reality capture on construction sites. Despite a lot of efforts such as the Internet of Things (IoT) and AI cameras, so far, it is very challenging to capture the spatial-temporal information of the whole construction site. Time-dynamic 4D point cloud (4DPC) is an emerging sensing technology consisting of three-dimensional point cloud scans at a frequency, e.g., two frames per second. 4DPC has been successfully used in several industries, such as autonomous driving and motion analysis in sports science, due to its ability to capture dynamic objects and environments. This paper presents a strengths, weaknesses, opportunities, and threats (SWOT) analysis of the possible applications of 4DPC in DTC. In summary, 4DPC can effectively capture the dynamic feature of construction progress, with strengths in real-time information updating, covering more precise geometry dynamics, and providing localization and mapping information, etc., which are in line with the development of DTC. Meanwhile, the disadvantages include errors or noise in recognizing lign-permeable materials and water, enormous data volume, challenges in modeling spatial-temporal structure, and computation load in processing. The 4DPC receives several opportunities, such as establishing a dynamic data foundation for construction automation and modeling of workspace and integrating with 4D BIM for creating DTC; However, threats such as LiDAR-hostile weather and emerging competitors are also identified. Overall, 4DPC is a promising research direction that may interest DTC researchers and practitioners for various application situations in the construction industry.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M.: Digital twin application in the construction industry: a literature review. J. Build. Eng. 40, 102726 (2021)CrossRef Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M.: Digital twin application in the construction industry: a literature review. J. Build. Eng. 40, 102726 (2021)CrossRef
2.
Zurück zum Zitat Boje, C., Guerriero, A., Kubicki, S., Rezgui, Y.: Towards a semantic construction digital twin: directions for future research. Autom. Constr. 114, 103179 (2020)CrossRef Boje, C., Guerriero, A., Kubicki, S., Rezgui, Y.: Towards a semantic construction digital twin: directions for future research. Autom. Constr. 114, 103179 (2020)CrossRef
3.
Zurück zum Zitat Salami, D., Palipana, S., Kodali, M., Sigg, S.: Motion pattern recognition in 4D point clouds. In: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP, pp. 1–6. IEEE (2020) Salami, D., Palipana, S., Kodali, M., Sigg, S.: Motion pattern recognition in 4D point clouds. In: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP, pp. 1–6. IEEE (2020)
4.
Zurück zum Zitat Xue, F., Lu, W., Chen, K., Webster, C.J.: BIM reconstruction from 3D point clouds: a semantic registration approach based on multimodal optimization and architectural design knowledge. Adv. Eng. Inform. 42, 100965 (2019)CrossRef Xue, F., Lu, W., Chen, K., Webster, C.J.: BIM reconstruction from 3D point clouds: a semantic registration approach based on multimodal optimization and architectural design knowledge. Adv. Eng. Inform. 42, 100965 (2019)CrossRef
5.
Zurück zum Zitat Bhople, A.R., Shrivastava, A.M., Prakash, S.: Point cloud based deep convolutional neural network for 3D face recognition. Multimedia Tools Appl. 80(20), 30237–30259 (2021)CrossRef Bhople, A.R., Shrivastava, A.M., Prakash, S.: Point cloud based deep convolutional neural network for 3D face recognition. Multimedia Tools Appl. 80(20), 30237–30259 (2021)CrossRef
6.
Zurück zum Zitat Zhang, Y., et al.:. PolarNet: an improved grid representation for online LiDAR point clouds semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9601–9610 (2020) Zhang, Y., et al.:. PolarNet: an improved grid representation for online LiDAR point clouds semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9601–9610 (2020)
7.
Zurück zum Zitat Xue, F., Lu, W., Webster, C.J., Chen, K.: A derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds. ISPRS J. Photogramm. Remote. Sens. 148, 32–40 (2019)CrossRef Xue, F., Lu, W., Webster, C.J., Chen, K.: A derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds. ISPRS J. Photogramm. Remote. Sens. 148, 32–40 (2019)CrossRef
8.
Zurück zum Zitat Wu, Y., Shang, J., Xue, F.: Regard: symmetry-based coarse registration of smartphone’s colorful point clouds with cad drawings for low-cost digital twin buildings. Remote Sens. 13(10), 1882 (2021)CrossRef Wu, Y., Shang, J., Xue, F.: Regard: symmetry-based coarse registration of smartphone’s colorful point clouds with cad drawings for low-cost digital twin buildings. Remote Sens. 13(10), 1882 (2021)CrossRef
9.
Zurück zum Zitat Yuan, L., Guo, J., Wang, Q.: Automatic classification of common building materials from 3D terrestrial laser scan data. Autom. Constr. 110, 103017 (2020)CrossRef Yuan, L., Guo, J., Wang, Q.: Automatic classification of common building materials from 3D terrestrial laser scan data. Autom. Constr. 110, 103017 (2020)CrossRef
10.
Zurück zum Zitat Wen, H., Liu, Y., Huang, J., Duan, B., Yi, L.: Point primitive transformer for long-term 4D point cloud video understanding (2022). arXiv preprint arXiv:2208.00281 Wen, H., Liu, Y., Huang, J., Duan, B., Yi, L.: Point primitive transformer for long-term 4D point cloud video understanding (2022). arXiv preprint arXiv:​2208.​00281
11.
Zurück zum Zitat Fan, H., Yang, Y., Kankanhalli, M.: Point 4D transformer networks for spatio-temporal modeling in point cloud videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14204–14213 (2021) Fan, H., Yang, Y., Kankanhalli, M.: Point 4D transformer networks for spatio-temporal modeling in point cloud videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14204–14213 (2021)
12.
Zurück zum Zitat Fan, H., Yu, X., Ding, Y., Yang, Y., Kankanhalli, M.: PSTNet: point spatio-temporal convolution on point cloud sequences (2022). arXiv preprint arXiv:2205.13713 Fan, H., Yu, X., Ding, Y., Yang, Y., Kankanhalli, M.: PSTNet: point spatio-temporal convolution on point cloud sequences (2022). arXiv preprint arXiv:​2205.​13713
13.
Zurück zum Zitat Shi, H., Lin, G., Wang, H., Hung, T.Y., Wang, Z.: SpSequenceNet: semantic segmentation network on 4D point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4574–4583 (2020) Shi, H., Lin, G., Wang, H., Hung, T.Y., Wang, Z.: SpSequenceNet: semantic segmentation network on 4D point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4574–4583 (2020)
14.
Zurück zum Zitat Wang, Z., Li, W., Shen, Y., Cai, B.: 4-D SLAM: an efficient dynamic Bayes network-based approach for dynamic scene understanding. IEEE Access 8, 219996–220014 (2020)CrossRef Wang, Z., Li, W., Shen, Y., Cai, B.: 4-D SLAM: an efficient dynamic Bayes network-based approach for dynamic scene understanding. IEEE Access 8, 219996–220014 (2020)CrossRef
15.
Zurück zum Zitat Gao, R., Li, M., Yang, S.J., Cho, K.: Reflective noise filtering of large-scale point cloud using transformer. Remote Sens. 14(3), 577 (2022)CrossRef Gao, R., Li, M., Yang, S.J., Cho, K.: Reflective noise filtering of large-scale point cloud using transformer. Remote Sens. 14(3), 577 (2022)CrossRef
16.
Zurück zum Zitat Chen, L., Yang, J., Kong, H.: Lidar-histogram for fast road and obstacle detection. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1343–1348. IEEE (2017) Chen, L., Yang, J., Kong, H.: Lidar-histogram for fast road and obstacle detection. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1343–1348. IEEE (2017)
17.
Zurück zum Zitat Li, L., Li, Z., Zakharchenko, V., Chen, J., Li, H.: Advanced 3D motion prediction for video-based dynamic point cloud compression. IEEE Trans. Image Process. 29, 289–302 (2019)CrossRef Li, L., Li, Z., Zakharchenko, V., Chen, J., Li, H.: Advanced 3D motion prediction for video-based dynamic point cloud compression. IEEE Trans. Image Process. 29, 289–302 (2019)CrossRef
18.
Zurück zum Zitat Liu, Z., et al.: Point cloud video streaming: challenges and solutions. IEEE Netw. 35(5), 202–209 (2021)CrossRef Liu, Z., et al.: Point cloud video streaming: challenges and solutions. IEEE Netw. 35(5), 202–209 (2021)CrossRef
19.
Zurück zum Zitat Walker, R., Smith, S., Bosché, F.: Enabling operational autonomy in earth-moving with real-time 3D environment modelling. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 38, pp. 145–152. IAARC Publications (2021) Walker, R., Smith, S., Bosché, F.: Enabling operational autonomy in earth-moving with real-time 3D environment modelling. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 38, pp. 145–152. IAARC Publications (2021)
20.
Zurück zum Zitat Fang, Y., Cho, Y.K., Chen, J.: A framework for real-time pro-active safety assistance for mobile crane lifting operations. Autom. Constr. 72, 367–379 (2016)CrossRef Fang, Y., Cho, Y.K., Chen, J.: A framework for real-time pro-active safety assistance for mobile crane lifting operations. Autom. Constr. 72, 367–379 (2016)CrossRef
21.
Zurück zum Zitat Heinzler, R., Piewak, F., Schindler, P., Stork, W.: Cnn-based lidar point cloud de-noising in adverse weather. IEEE Robot. Autom. Lett. 5(2), 2514–2521 (2020)CrossRef Heinzler, R., Piewak, F., Schindler, P., Stork, W.: Cnn-based lidar point cloud de-noising in adverse weather. IEEE Robot. Autom. Lett. 5(2), 2514–2521 (2020)CrossRef
22.
Zurück zum Zitat Chidsin, W., Gu, Y., Goncharenko, I.: AR-based navigation using RGB-D camera and hybrid map. Sustainability 13(10), 5585 (2021)CrossRef Chidsin, W., Gu, Y., Goncharenko, I.: AR-based navigation using RGB-D camera and hybrid map. Sustainability 13(10), 5585 (2021)CrossRef
23.
Zurück zum Zitat Düking, P., Holmberg, H.C., Sperlich, B.: The potential usefulness of virtual reality systems for athletes: a short SWOT analysis. Front. Physiol. 9, 128 (2018)CrossRef Düking, P., Holmberg, H.C., Sperlich, B.: The potential usefulness of virtual reality systems for athletes: a short SWOT analysis. Front. Physiol. 9, 128 (2018)CrossRef
Metadaten
Titel
Applications of 4D Point Clouds (4DPC) in Digital Twin Construction: A SWOT Analysis
verfasst von
Dong Liang
Fan Xue
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-3626-7_95