International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-1/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W2, 53–60, 2019
https://doi.org/10.5194/isprs-archives-XLII-1-W2-53-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W2, 53–60, 2019
https://doi.org/10.5194/isprs-archives-XLII-1-W2-53-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2019

12 Sep 2019

A MULTI-PURPOSE BENCHMARK FOR PHOTOGRAMMETRIC URBAN 3D RECONSTRUCTION IN A CONTROLLED ENVIRONMENT

E. Özdemir1,2, I. Toschi2, and F. Remondino2 E. Özdemir et al.
  • 1Space Centre, Skolkovo Institute of Technology (SKOLTECH), Moscow, Russia
  • 23D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: benchmark, photogrammetry, orientation, matching, classification, 3D building reconstruction, evaluation, accuracy

Abstract. This paper presents a novel multi-purpose benchmark for assessing the performance of the entire image-based pipeline for 3D urban reconstruction and 3D data classification. The proposed benchmark is called “3DOMcity” photogrammetric contest and is publicly available at https://3dom.fbk.eu/3domcity-benchmark. Compared to previous benchmark initiatives, the innovative aspects introduced by 3DOMcity are threefold. First, it covers the entire 3D reconstruction pipeline, including image orientation, dense image matching, point cloud classification and 3D building reconstruction. Second, the performance assessment is performed within a metrological context. Finally, the provided datasets offer 2D and 3D data collected at a very high spatial resolution. Furthermore, the benchmark is multi-purpose since it involves a multiplicity of tasks, that can be either performed independently from each other’s, or grouped together. The contributions of this paper are (i) the presentation of the novel benchmark dataset and corresponding reference data, (ii) the description of the multi-purpose benchmark tasks and evaluation procedures, and (iii) the discussion of first results of image orientation, dense image matching and classification.