Automation in the construction has seen progress in using modern techniques, which has opened new perspectives for the verification of construction structures using point clouds. This paper discusses wall structure geometry verification, using point cloud data with geometry information extracted from building information modeling models as reference data. The research is focusing on automating the verification of wall structures using a software solution developed in Python. It involves processing and extracting geometric data from models in industry foundation classes' format, comparing the data and visualization of deviation. Results, conclusion, and future workplans are given for achieving better understanding.
T. Mandičák, P. Mésároš, and M. Tkáč, “Impact of management decisions based on managerial competencies and skills developed through BIM technology on performance of construction enterprises,” Pollack Period, vol. 13, no. 3, pp. 131–140, 2018.
T. Mandičák, P. Mésároš, and M. Tkáč, “Construction project management through BIM and knowledge technology,” Pollack Period, vol. 15, no. 1, pp. 177–186, 2020.
J. Peroš, R. Paar, V. Divić, and B. Kovačić, “Fusion of laser scans and image data -RGB+D for structural health monitoring of engineering structures,” Appl. Sci., vol. 12, no. 22, 2021, Art no. 11763.
Ľ. Kovanič, P. Blistan, R. Urban, M. Štroner, K. Pukanská, K Bartoš, and J. Palková, “Analytical determination of geometric parameters of the rotary Kiln by Novel approach of TLS point cloud segmentation,” Appl. Sci., vol. 10, no. 21, 2019, Art no. 7652.
A. Hideghéty and M. Fraštia, “Comparison of image operators for time-based photogrammetry,” Pollack Period, vol. 18, no. 3, pp. 119–124, 2023.
R. Honti, J. Erdélyi, and A. Kopáčik, “Automated sphere segmentation from Point clouds,” Pollack Period, vol. 15, no. 3, pp. 15–25, 2020.
buildingsSMART, 2018. [Online]. Available: http://www.buildingsmart-tech.org. Accessed: Dec. 29, 2023.
R. Honti, J. Erdélyi, and A. Kopáčik, “A. semi-automated segmentation of geometric shapes from point clouds,” Remote Sensing, vol. 14, no. 18, 2022, Art no. 4591.
V. Lehtola, S. Nikoohemat, and A. Nüchter, “Indoor 3D: Overview on scanning and reconstruction methods,” Handbook of Big Geospatial Data, Springer, pp. 55–97, 2021.
F. Poux and R. Billen, “Voxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs. deep learning methods,” ISPRS Int. J. Geo-Information, vol. 8, no. 5, 2019, Art no. 213.
M. Bassier, M. Vergauwen, and F. Poux, “Point cloud vs. mesh features for building interior classification,” Remote Sensing, vol. 12, no. 14, 2020, Art no. 2224.
Y. Cui, Q. Li, B. Yang, W. Xiao, and C. Chen, “Automatic 3-D reconstruction of indoor environment with mobile laser scanning point clouds,” IEEE J. Selected Top. Appl. Earth Observations Remote Sensing, vol. 12, no. 8, pp. 3117–3130, 2019.
D. Wolf, J. Prankl, and M. Vincze, “Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters,” in 2015 IEEE International Conference on Robotics and Automation. Seattle, WA, USA, May 26–30, 2015, pp. 4867–4873.