# Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras

arXiv: https://arxiv.org/abs/2012.14292 · HTML: https://mlsanity.com/paper/2012.14292/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels](https://mlsanity.com/paper/2301.12689/) (2023)
- [Fieldscale: Locality-Aware Field-based Adaptive Rescaling for Thermal Infrared Image](https://mlsanity.com/paper/2405.15395/) (2024)
- [Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry](https://mlsanity.com/paper/2209.06588/) (2022)
- [Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images](https://mlsanity.com/paper/2410.18340/) (2024)
- [Photometric Correction for Infrared Sensors](https://mlsanity.com/paper/2304.03930/) (2023)
- [Supervised Image Translation from Visible to Infrared Domain for Object Detection](https://mlsanity.com/paper/2408.01843/) (2024)

## Abstract (arXiv)

Thermal infrared cameras are increasingly being used in various applications such as robot vision, industrial inspection and medical imaging, thanks to their improved resolution and portability. However, the performance of traditional computer vision techniques developed for electro-optical imagery does not directly translate to the thermal domain due to two major reasons: these algorithms require photometric assumptions to hold, and methods for photometric calibration of RGB cameras cannot be applied to thermal-infrared cameras due to difference in data acquisition and sensor phenomenology. In this paper, we take a step in this direction, and introduce a novel algorithm for online photometric calibration of thermal-infrared cameras. Our proposed method does not require any specific driver/hardware support and hence can be applied to any commercial off-the-shelf thermal IR camera. We present this in the context of visual odometry and SLAM algorithms, and demonstrate the efficacy of our proposed system through extensive experiments for both standard benchmark datasets, and real-world field tests with a thermal-infrared camera in natural outdoor environments.