# Learning Camera Miscalibration Detection

arXiv: https://arxiv.org/abs/2005.11711 · HTML: https://mlsanity.com/paper/2005.11711/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [Inferring bias and uncertainty in camera calibration](https://mlsanity.com/paper/2107.13484/) (2021)
- [AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion](https://mlsanity.com/paper/2503.21581/) (2025)
- [Neural Real-Time Recalibration for Infrared Multi-Camera Systems](https://mlsanity.com/paper/2410.14505/) (2024)

## Abstract (arXiv)

Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications. One of the issues that can occur to a robot is miscalibration of its sensors due to aging, environmental transients, or external disturbances. Precise calibration lies at the core of a variety of applications, due to the need to accurately perceive the world. However, while a lot of work has focused on calibrating the sensors, not much has been done towards identifying when a sensor needs to be recalibrated. This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras. Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric. Additionally, by training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not. The code is available at http://github.com/ethz-asl/camera_miscalib_detection.