A Convex Hull-Based Automatic Registration Method for Drone Point Clouds and BIM in Construction Sites

Hyeon-Su Lim; You-Kyung Kim; Min-Seul Kwak; Seung-Beom Ha; Seok-Heon Yun

doi:10.13161/kibim.2026.16.3.023

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2026 Vol.16, Issue 3 Preview Page Next Page

A Convex Hull-Based Automatic Registration Method for Drone Point Clouds and BIM in Construction Sites 건설현장 드론 포인트클라우드와 BIM 간의 Convex Hull 기반 자동 정합 방안

30 September 2026. pp. 23-32

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Abstract

This study proposes a Convex Hull-based automatic registration method between BIM and drone-based point clouds for construction status analysis. Drone-based point clouds acquired from actual construction sites often contain temporary structures and non-target objects, while interior data may be incomplete due to the limitations of exterior drone imaging. These characteristics create geometric differences between BIM and point cloud data and may reduce the applicability of conventional registration methods that rely on entire shapes or local feature correspondences. To address these limitations, this study uses the lowest floor boundary, which can be commonly identified in both datasets, as the geometric reference for registration. The proposed method sequentially performs Z-axis alignment, Convex Hull-based floor boundary extraction, XY translational alignment based on the hull centroid, and rotational alignment based on the major-axis direction of the minimum bounding rectangle. Registration accuracy was evaluated using corresponding upper slab planes extracted from BIM and drone-based point clouds. The proposed method achieved a Grid IoU of 84.0%, with a mean distance error of 151 mm, an RMSE of 299 mm, and a median error of 74 mm. In comparison, ICP produced a mean error of 259 mm and an RMSE of 436 mm, while FPFH+ICP produced a mean error of 985 mm and an RMSE of 2,194 mm. These results indicate that the proposed method can provide stable spatial alignment between BIM and drone-based point clouds even under construction-site conditions involving temporary objects and incomplete interior data.

Keywords

BIM

Point Cloud

Drone

Convex Hull

Automatic Registration

키워드

빌딩정보모델

포인트클라우드

드론

컨벡스 헐

자동 정합

References

Attia, M., Slama, Y., Peyrodie, L., Cao, H., Haddad, F. (2018). 3D Point Cloud Coarse Registration Based on Convex Hull Refined by ICP and NDT, In 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1-6.

10.1109/M2VIP.2018.8600843

Barber, C. B., Dobkin, D. P., Huhdanpaa, H. (1996). The Quickhull Algorithm for Convex hulls, ACM Transactions on Mathematical Software (TOMS), 22(4), pp. 469-483.

10.1145/235815.235821

Braun, A., Tuttas, S., Borrmann, A., Stilla, U. (2015). A Concept for Automated Construction Progress Monitoring using Bim-based Geometric Constraints and Photogrammetric Point Clouds, Electronic Journal of Information Technology in Construction, 20(5), pp. 68-79.

Fan, J., Yang, J., Zhao, Y., Ai, D., Liu, Y., Wang, G., Wang, Y. (2017). Convex Hull Aided Registration Method (CHARM). IEEE Transactions on Visualization and Computer Graphics, 23(9), pp. 2042-2055.

10.1109/TVCG.2016.2602858

Kong, L., Qian, H., Wu, Y., Niu, X., Wang, D., Huang, Z. (2023). Simplification and Regularization Algorithm for Right-angled Polygon Building Outlines with Jagged Edges, ISPRS International Journal of Geo-Information, 12(12), Article 469.

10.3390/ijgi12120469

Lee, D. R., Kim, D. P. (2025). Accuracy Analysis of 3D Structural Frame Data Based on UAV-Terrestrial LiDAR Convertgence, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 43(6), pp. 815-823.

10.7848/ksgpc.2025.43.6.815

Lee, D. S., Kim, G. H. (2020). Smart Quality Inspection Scheme for Construction Project Using 3D Scanning Technology, Journal of the Korea Institute of Building Construction, 20(2), pp. 191-198.

10.5345/JKIBC.2020.20.2.191

Lim, H. S., Lee, Y. S., Yun, S. H. (2025). Automatic Registration Method between BIM and Point Cloud Data for Construction Accuracy Analysis. KIBIM Magazine, 15(4), pp. 84-92.

Park, J. K., Um, D. Y. (2020). Accuracy Evaluation by Point Cloud Data Registration Method, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 38(1), pp. 35-41.

10.7848/KSGPC.2020.38.1.35

Wen, Y., Luo, P., Wang, M., Muchla, A., Xiao, L., Brilakis, I. (2026). Vision Language Model-driven Multimodal Occlusion Analysis (VLM-MOA) Method for Construction Progress Monitoring, In Computing in Civil Engineering 2025: Computational and Intelligent Technologies, pp. 488-497.

10.1061/9780784486436.052

Zhang, Y., Liu, W., Zuo, H., Fu, C. (2024). Global BIM-Point Registration by Primitive-Level Structure Matching. In 2024 International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 278-283.

10.1109/ICARM62033.2024.10715880

Information

Publisher :Journal of KIBIM
Publisher(Ko) :한국BIM학회
Journal Title :Journal of KIBIM
Journal Title(Ko) :한국BIM학회논문집
Volume : 16
No :3
Pages :23-32
Received Date : 2026-09-07; 2026-09-19
Accepted Date : 2026-09-23
DOI :https://doi.org/10.13161/kibim.2026.16.3.023

Journal of KIBIM ISSN:2288-1697(Print) 2288-1697(Online) 한국BIM학회논문집

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