Next Article in Journal
Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks
Next Article in Special Issue
Use of Low-Cost Spherical Cameras for the Digitisation of Cultural Heritage Structures into 3D Point Clouds
Previous Article in Journal
Fast and Accurate Background Reconstruction Using Background Bootstrapping
Previous Article in Special Issue
HTR for Greek Historical Handwritten Documents
Article

Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds

1
Institute of History of Art, Building Archaeoloy and Restoration, Vienna University of Technology, Karlsplatz 13/E251, 1040 Vienna, Austria
2
Research Unit of Photogrammetry, Department of Geodesy and Geoinformation, Vienna University of Technology, Wiedner Hauptstraße 8/E120, 1040 Vienna, Austria
3
Institute for Mechanics of Materials and Structures (IMWS), Vienna University of Technology, Karlsplatz 13/202, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Guillaume Caron, Olga Regina Pereira Bellon and Ilan Shimshoni
J. Imaging 2022, 8(1), 10; https://doi.org/10.3390/jimaging8010010
Received: 30 November 2021 / Revised: 7 January 2022 / Accepted: 10 January 2022 / Published: 13 January 2022
We present 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%. View Full-Text
Keywords: architectural modeling; historic building structures; terrestrial laser scanning; point cloud processing; 3D modeling architectural modeling; historic building structures; terrestrial laser scanning; point cloud processing; 3D modeling
Show Figures

Figure 1

MDPI and ACS Style

Özkan, T.; Pfeifer, N.; Styhler-Aydın, G.; Hochreiner, G.; Herbig, U.; Döring-Williams, M. Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds. J. Imaging 2022, 8, 10. https://doi.org/10.3390/jimaging8010010

AMA Style

Özkan T, Pfeifer N, Styhler-Aydın G, Hochreiner G, Herbig U, Döring-Williams M. Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds. Journal of Imaging. 2022; 8(1):10. https://doi.org/10.3390/jimaging8010010

Chicago/Turabian Style

Özkan, Taşkın, Norbert Pfeifer, Gudrun Styhler-Aydın, Georg Hochreiner, Ulrike Herbig, and Marina Döring-Williams. 2022. "Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds" Journal of Imaging 8, no. 1: 10. https://doi.org/10.3390/jimaging8010010

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop