The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 279–284, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-279-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 279–284, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-279-2021

  28 Jun 2021

28 Jun 2021

TLS AND SHORT-RANGE PHOTOGRAMMETRIC DATA FUSION FOR BUILDINGS 3D MODELING

P. R. S. Ruiz1, C. M. Almeida1, M. B. Schimalski2, V. Liesenberg2, and E. A. Mitishita3 P. R. S. Ruiz et al.
  • 1Division for Earth Observation and Geoinformatics, National Institute for Space Research - INPE, Brazil
  • 2Department of Forest Engineering, Santa Catarina State University - UDESC, Brazil
  • 3Department of Geomatics, Federal University of Parana - UFPR, Brazil

Keywords: Photogrammetry, RPA, LiDAR, SfM, ICP

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.