Volume XLII-2/W17
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W17, 331–338, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W17-331-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W17, 331–338, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W17-331-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Nov 2019

29 Nov 2019

OPEN-SOURCE IMAGE-BASED 3D RECONSTRUCTION PIPELINES: REVIEW, COMPARISON AND EVALUATION

E.-K. Stathopoulou1,2, M. Welponer1, and F. Remondino1 E.-K. Stathopoulou et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2Laboratory of Photogrammetry, National Technical University of Athens (NTUA), Greece

Keywords: photogrammetry, image orientation, SfM, dense image matching, MVS, validation

Abstract. State-of-the-art automated image orientation (Structure from Motion) and dense image matching (Multiple View Stereo) methods commonly used to produce 3D information from 2D images can generate 3D results – such as point cloud or meshes – of varying geometric and visual quality. Pipelines are generally robust and reliable enough, mostly capable to process even large sets of unordered images, yet the final results often lack completeness and accuracy, especially while dealing with real-world cases where objects are typically characterized by complex geometries and textureless surfaces and obstacles or occluded areas may also occur. In this study we investigate three of the available commonly used open-source solutions, namely COLMAP, OpenMVG+OpenMVS and AliceVision, evaluating their results under diverse large scale scenarios. Comparisons and critical evaluation on the image orientation and dense point cloud generation algorithms is performed with respect to the corresponding ground truth data. The presented FBK-3DOM datasets are available for research purposes.