Abstract

The adoption of 3D survey techniques is essential to promote efficient and timely information acquisition on constructed buildings. This article addresses terrestrial LiDAR (TLS) and close-range photogrammetric data fusion for the 3D modeling of a building in Level of Detail (LoD) 3. The selected building presents challenging elements for modeling, such as extended curved slabs, external glass walls, recessed facades and diverse roof pitches. It is located on the campus of the Federal University of Paraná (UFPR) in Curitiba, Brazil. The accuracy of the data integration was obtained through the analysis of deviations between the clouds of primary points. The accuracy of the point cloud model was verified by comparing its dimensions with the real dimensions of the building, obtained by means of a handheld laser distance meter (EDM). The results demonstrate that there was a correspondence between the EDM measures and the model, with a satisfactory statistical agreement between the estimated and reference values and a general maximum absolute error of 4.5 cm. The article focuses on the accuracy of point cloud models for the cadastral updating of buildings, providing information for decision making in projects documentation and interventions.

Details

Title
TLS AND SHORT-RANGE PHOTOGRAMMETRIC DATA FUSION FOR BUILDINGS 3D MODELING
Author
Ruiz, P R S 1 ; Almeida, C M 1 ; Schimalski, M B 2 ; Liesenberg, V 2 ; Mitishita, E A 3 

 Division for Earth Observation and Geoinformatics, National Institute for Space Research - INPE, Brazil; Division for Earth Observation and Geoinformatics, National Institute for Space Research - INPE, Brazil 
 Department of Forest Engineering, Santa Catarina State University - UDESC, Brazil; Department of Forest Engineering, Santa Catarina State University - UDESC, Brazil 
 Department of Geomatics, Federal University of Parana - UFPR, Brazil; Department of Geomatics, Federal University of Parana - UFPR, Brazil 
Pages
279-284
Publication year
2021
Publication date
2021
Publisher
Copernicus GmbH
ISSN
16821750
e-ISSN
21949034
Source type
Conference Paper
Language of publication
English
ProQuest document ID
2585322944
Copyright
© 2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.