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13 April 2018 Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961A (2018) https://doi.org/10.1117/12.2309639
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
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.
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Christoph Heindl, Thomas Pönitz, Gernot Stübl, Andreas Pichler, and Josef Scharinger "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961A (13 April 2018); https://doi.org/10.1117/12.2309639
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