Volume XLII-2/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 755-761, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-755-2019
© 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/W9, 755-761, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-755-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Jan 2019

31 Jan 2019

FOVEON VS BAYER: COMPARISON OF 3D RECONSTRUCTION PERFORMANCES

M. Vlachos1, D. Skarlatos1, and P. Bodin2 M. Vlachos et al.
  • 1Cyprus University of Technology, Dep. Of Civil Engineering and Geomatics, P.O. Box 50329, Limassol 3603, Cyprus
  • 2National School of Geographic Sciences (ENSG), France

Keywords: Photogrammetry, Bayer pattern, accuracy, de-mosaicking, point cloud, structure light scanner

Abstract. The main idea of this particular study was to validate if the new FOVEON technology implemented by sigma cameras can provide better overall results and outperform the traditional Bayer pattern sensor cameras regarding the radiometric information that records as well as the photogrammetric point cloud quality that can provide. Based on that, the scope of this paper is separated into two evaluations. First task is to evaluate the quality of information reconstructed during de-mosaicking step for Bayer pattern cameras by detecting potential additional colour distortion added during the de-mosaicking step, and second task is the geometric comparisons of point clouds generated by the photos by Bayer and FOVEON sensors against a reference point cloud. The first phase of the study is done using various de-mosaicking algorithms to process various artificial Bayern pattern images and then compare them with reference FOVEON images. The second phase of the study is carried on by reconstructing 3D point clouds of the same objects captured by a Bayer and a FOVEON sensor respectively and then comparing the various point clouds with a reference one, generated by a structured light hand-held scanner. The comparison is separated into two parts, where initially we evaluate five separate point clouds (RGB, Gray, Red, Green, Blue) for each camera sensor per site and then a second comparison is evaluated on colour classified RGB point cloud segments.