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Proceedings Paper

Characterizing the influence of surface roughness and inclination on 3D vision sensor performance
Author(s): John R. Hodgson; Peter Kinnell; Laura Justham; Michael R. Jackson
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Paper Abstract

This paper reports a methodology to evaluate the performance of 3D scanners, focusing on the influence of surface roughness and inclination on the number of acquired data points and measurement noise. Point clouds were captured of samples mounted on a robotic pan-tilt stage using an Ensenso active stereo 3D scanner. The samples have isotropic texture and range in surface roughness (Ra) from 0.09 to 0.46 μm. By extracting the point cloud quality indicators, point density and standard deviation, at a multitude of inclinations, maps of scanner performance are created. These maps highlight the performance envelopes of the sensor, the aim being to predict and compare scanner performance on real-world surfaces, rather than idealistic artifacts. The results highlight the need to characterize 3D vision sensors by their measurement limits as well as best-case performance, determined either by theoretical calculation or measurements in ideal circumstances.

Paper Details

Date Published: 8 December 2015
PDF: 7 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751L (8 December 2015); doi: 10.1117/12.2228826
Show Author Affiliations
John R. Hodgson, Loughborough Univ. (United Kingdom)
Peter Kinnell, Loughborough Univ. (United Kingdom)
Laura Justham, Loughborough Univ. (United Kingdom)
Michael R. Jackson, Loughborough Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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