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Authors: Vlachos, Marinos 
Bodin, P. 
Skarlatos, Dimitrios 
Keywords: Accuracy;Bayer pattern;De-mosaicking;Photogrammetry;Point cloud;Structure light scanner
Category: Civil Engineering
Field: Engineering and Technology
Issue Date: 31-Jan-2019
Source: 8th International Workshop on 3D Virtual Reconstruction and Visualization of Complex Architectures, 3D-ARCH 2019, Bergamo, Italy, 6 February 2019 through 8 February 2019
Conference: International Workshop on 3D Virtual Reconstruction and Visualization of Complex Architectures 
Abstract: © Authors 2019. 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.
Description: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Volume 42, Issue 2/W9, 31 January 2019, Pages 755-761
ISSN: 21949042
DOI: 10.5194/isprs-archives-XLII-2-W9-755-2019
Type: Conference Papers
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