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Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time
Author(s): Christoph Heindl; Thomas Pönitz; Gernot Stübl; Andreas Pichler; Josef Scharinger
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Paper Abstract

Commodity RGB-D sensors capture color images along with dense pixel-wise depth information in real-time. Typical RGB-D sensors are provided with a factory calibration and exhibit erratic depth readings due to coarse calibration values, ageing and thermal influence effects. This limits their applicability in computer vision and robotics. We propose a novel method to accurately calibrate depth considering spatial and thermal influences jointly. Our work is based on Gaussian Process Regression in a four dimensional Cartesian and thermal domain. We propose to leverage modern GPUs for dense depth map correction in real-time. For reproducibility we make our dataset and source code publicly available.

Paper Details

Date Published: 13 April 2018
PDF: 8 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961A (13 April 2018); doi: 10.1117/12.2309639
Show Author Affiliations
Christoph Heindl, Profactor GmbH (Austria)
Thomas Pönitz, Profactor GmbH (Austria)
Gernot Stübl, Profactor GmbH (Austria)
Andreas Pichler, Profactor GmbH (Austria)
Josef Scharinger, JKU (Austria)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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