# Inferring bias and uncertainty in camera calibration

arXiv: https://arxiv.org/abs/2107.13484 · HTML: https://mlsanity.com/paper/2107.13484/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty](https://mlsanity.com/paper/1811.03264/) (2018)
- [Efficient Pose Selection for Interactive Camera Calibration](https://mlsanity.com/paper/1907.04096/) (2019)
- [Learning Camera Miscalibration Detection](https://mlsanity.com/paper/2005.11711/) (2020)

## Built on by (most cited first, 5 of 5)

- [Modeling dynamic target deformation in camera calibration](https://mlsanity.com/paper/2110.07322/) (2021)
- [Camera Calibration via Circular Patterns: A Comprehensive Framework with Detection Uncertainty and Unbiased Projection Model](https://mlsanity.com/paper/2506.16842/) (2025)
- [CameraVDP: Perceptual Display Assessment with Uncertainty Estimation via Camera and Visual Difference Prediction](https://mlsanity.com/paper/2509.08947/) (2025)
- [Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline](https://mlsanity.com/paper/2508.07217/) (2025)
- [Automatic Reproducible Camera Intrinsic Calibration](https://mlsanity.com/paper/2609.10082/) (2026)

## Abstract (arXiv)

Accurate camera calibration is a precondition for many computer vision applications. Calibration errors, such as wrong model assumptions or imprecise parameter estimation, can deteriorate a system's overall performance, making the reliable detection and quantification of these errors critical. In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance). The proposed bias detection method uncovers smallest systematic errors and thereby reveals imperfections of the calibration setup and provides the basis for camera model selection. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. Furthermore, we derive a simple uncertainty metric that is independent of the camera model. In combination, the proposed methods can be used to assess the accuracy of individual calibrations, but also to benchmark new calibration algorithms, camera models, or calibration setups. We evaluate the proposed methods with simulations and real cameras.