Terrestrial Scanning / Architecture, Infrastructure & Construction

Taskın Özkan et al.: Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds 13.01.2022

In this paper, the team of the Institute of History of Art, Building Archaeology and Restoration of the Vienna University of Technology presents a set of methods to improve the automation of the parametric 3D modeling of historic roof structures using terrestrial laser scanning (TLS) point clouds.
The final product of the TLS point clouds consist of 3D representation of all objects, which were visible during the scanning, including structural elements, wooden walking ways and rails, roof cover and the ground; thus, a new method was applied to detect and exclude the roof cover points. On the interior roof points, a region-growing segmentation-based beam side face searching approach was extended with an additional method that splits complex segments into linear sub-segments.
The presented workflow was conducted on an entire historic roof structure. The main target is to increase the automation of the modeling in the context of completeness. The number of manually counted beams served as reference to define a completeness ratio for results of automatically modeling beams.
The analysis shows that this approach could increase the quantitative completeness of the full automatically generated 3D model of the roof structure from 29% to 63%.

To evalute the suggested methods and the workflow from scratch to end, the roof structure of St. Michael, a medieval church in the city center of Vienna, was chosen as a case study. Scanning of the roof structure was made with a RIEGL VZ-2000i Terrestrial Laser Scanner.
Over 600 million points from inside the roof structure were collected at the end of one day of work. While the coarse registration of the scans was already performed internally by the scanner for spatially consecutive scan positions, coarse registration of non-registered scan positions, refinement of the registration and multi-station adjustment applications were performed afterwards using the RIEGL software package RiSCAN PRO. While the registration after scanning was completed within two working days, the multi-station adjustment process resulted in 0.004 m root mean square error (RMSE) in approximately 40 min.

The full article was published in the Journal of Imaging 2022, 10 and can be found here.