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.
@article{arxiv.1907.00549,
title = {Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time},
author = {Christoph Heindl and Thomas Pönitz and Gernot Stübl and Andreas Pichler and Josef Scharinger},
journal= {arXiv preprint arXiv:1907.00549},
year = {2019}
}