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Proceedings Paper

Improving automated 3D reconstruction methods via vision metrology
Author(s): Isabella Toschi; Erica Nocerino; Mona Hess; Fabio Menna; Ben Sargeant; Lindsay MacDonald; Fabio Remondino; Stuart Robson
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Paper Abstract

This paper aims to provide a procedure for improving automated 3D reconstruction methods via vision metrology. The 3D reconstruction problem is generally addressed using two different approaches. On the one hand, vision metrology (VM) systems try to accurately derive 3D coordinates of few sparse object points for industrial measurement and inspection applications; on the other, recent dense image matching (DIM) algorithms are designed to produce dense point clouds for surface representations and analyses. This paper strives to demonstrate a step towards narrowing the gap between traditional VM and DIM approaches. Efforts are therefore intended to (i) test the metric performance of the automated photogrammetric 3D reconstruction procedure, (ii) enhance the accuracy of the final results and (iii) obtain statistical indicators of the quality achieved in the orientation step. VM tools are exploited to integrate their main functionalities (centroid measurement, photogrammetric network adjustment, precision assessment, etc.) into the pipeline of 3D dense reconstruction. Finally, geometric analyses and accuracy evaluations are performed on the raw output of the matching (i.e. the point clouds) by adopting a metrological approach. The latter is based on the use of known geometric shapes and quality parameters derived from VDI/VDE guidelines. Tests are carried out by imaging the calibrated Portable Metric Test Object, designed and built at University College London (UCL), UK. It allows assessment of the performance of the image orientation and matching procedures within a typical industrial scenario, characterised by poor texture and known 3D/2D shapes.

Paper Details

Date Published: 21 June 2015
PDF: 15 pages
Proc. SPIE 9528, Videometrics, Range Imaging, and Applications XIII, 95280H (21 June 2015); doi: 10.1117/12.2184974
Show Author Affiliations
Isabella Toschi, Fondazione Bruno Kessler (Italy)
Erica Nocerino, Fondazione Bruno Kessler (Italy)
Mona Hess, Univ. College London (United Kingdom)
Fabio Menna, Fondazione Bruno Kessler (Italy)
Ben Sargeant, Univ. College London (United Kingdom)
Lindsay MacDonald, Univ. College London (United Kingdom)
Fabio Remondino, Fondazione Bruno Kessler (Italy)
Stuart Robson, Univ. College London (United Kingdom)

Published in SPIE Proceedings Vol. 9528:
Videometrics, Range Imaging, and Applications XIII
Fabio Remondino; Mark R. Shortis, Editor(s)

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