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

## TL;DR

This paper introduces a real-time method using Gaussian Processes to correct RGB-D sensor depth inaccuracies caused by spatial and thermal effects, enhancing their reliability for computer vision and robotics applications.

## Contribution

It presents a novel Gaussian Process-based calibration method that jointly models spatial and thermal influences for RGB-D sensors, enabling real-time dense depth correction.

## Key findings

- Achieves accurate depth correction considering thermal effects
- Operates in real-time leveraging GPU acceleration
- Provides publicly available dataset and source code

## 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.