ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W2, 195-202, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W2-195-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
16 Aug 2017
AUTOMATED RECONSTRUCTION OF HISTORIC ROOF STRUCTURES FROM POINT CLOUDS – DEVELOPMENT AND EXAMPLES
M. Pöchtrager1,2, G. Styhler-Aydın1, M. Döring-Williams1, and N. Pfeifer2 1Institute of History of Art, Building Archaeology and Restoration, TU Wien, Austria
2Department of Geodesy and Geoinformation, TU Wien, Austria
Keywords: historical timber structures, LiDAR, point clouds, digital reconstruction Abstract. The analysis of historic roof constructions is an important task for planning the adaptive reuse of buildings or for maintenance and restoration issues. Current approaches to modeling roof constructions consist of several consecutive operations that need to be done manually or using semi-automatic routines. To increase efficiency and allow the focus to be on analysis rather than on data processing, a set of methods was developed for the fully automated analysis of the roof constructions, including integration of architectural and structural modeling. Terrestrial laser scanning permits high-detail surveying of large-scale structures within a short time. Whereas 3-D laser scan data consist of millions of single points on the object surface, we need a geometric description of structural elements in order to obtain a structural model consisting of beam axis and connections. Preliminary results showed that the developed methods work well for beams in flawless condition with a quadratic cross section and no bending. Deformations or damages such as cracks and cuts on the wooden beams can lead to incomplete representations in the model. Overall, a high degree of automation was achieved.
Conference paper (PDF, 2274 KB)


Citation: Pöchtrager, M., Styhler-Aydın, G., Döring-Williams, M., and Pfeifer, N.: AUTOMATED RECONSTRUCTION OF HISTORIC ROOF STRUCTURES FROM POINT CLOUDS – DEVELOPMENT AND EXAMPLES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W2, 195-202, https://doi.org/10.5194/isprs-annals-IV-2-W2-195-2017, 2017.

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