DRAWING GENERATION FOR DEFORMED ARCHITECTURAL OBJECTS BASED ON POINT CLOUDS

Authors

  • Robert Toots Simon Kuznets Kharkiv National University of Economics
  • Olena Shapovalova Simon Kuznets Kharkiv National University of Economics
  • Tetiana Nalyvaiko Simon Kuznets Kharkiv National University of Economics

DOI:

https://doi.org/10.34132/mspc2025.01.06.31

Keywords:

point cloud, architectural objects, deformations, technical drawings, design automation, 3D scanning.

Abstract

The thesis presents an analysis of the current state of scientific research and literature on the automation of drawing generation for architectural objects using point clouds, with particular attention to deformed structures. Emphasis is placed on the need to develop accurate and efficient methods for the automatic creation of technical drawings from real scanned data that capture the complex geometry of architectural elements and their deformations.

References

Kim, H., Son, H., & Kim, C. (2021). Automated extraction of building elements from 3D point cloud data. Automation in Construction, 124, 103573.

Xu, Y., & Stilla, U. (2021). Least squares fitting techniques for point cloud-based building modeling. Remote Sensing, 13(9), 1789.

Yadav, M., & Singh, A. (2022). Machine learning approaches for accurate modeling of point cloud data. Journal of Engineering, Design and Technology, 20(2), 544-560.

Zhu, J., Huang, J., & Feng, H. (2023). Deep learning for structural deformation recognition in point clouds. Engineering Structures, 281, 115804.

Tang, P., Huber, D., & Akinci, B. (2022). Integration of laser scanning and building information modeling (BIM) in construction engineering. Journal of Computing in Civil Engineering, 36(1), 04021056.

Published

2025-05-20