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

Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time