# Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline

arXiv: https://arxiv.org/abs/2508.07217 · HTML: https://mlsanity.com/paper/2508.07217/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [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)
- [BabelCalib: A Universal Approach to Calibrating Central Cameras](https://mlsanity.com/paper/2109.09704/) (2021)
- [Efficient Pose Selection for Interactive Camera Calibration](https://mlsanity.com/paper/1907.04096/) (2019)
- [Infrastructure-based Multi-Camera Calibration using Radial Projections](https://mlsanity.com/paper/2007.15330/) (2020)
- [Camera Calibration with Pose Guidance](https://mlsanity.com/paper/2102.10202/) (2021)
- [Inferring bias and uncertainty in camera calibration](https://mlsanity.com/paper/2107.13484/) (2021)

## Abstract (arXiv)

Offline camera calibration techniques typically employ parametric or generic camera models. Selecting parametric models relies heavily on user experience, and an inappropriate camera model can significantly affect calibration accuracy. Meanwhile, generic calibration methods involve complex procedures and cannot provide traditional intrinsic parameters. This paper reveals a pose ambiguity in the pose solutions of generic calibration methods that irreversibly impacts subsequent pose estimation. A linear solver and a nonlinear optimization are proposed to address this ambiguity issue. Then a global optimization hybrid calibration method is introduced to integrate generic and parametric models together, which improves extrinsic parameter accuracy of generic calibration and mitigates overfitting and numerical instability in parametric calibration. Simulation and real-world experimental results demonstrate that the generic-parametric hybrid calibration method consistently excels across various lens types and noise contamination, hopefully serving as a reliable and accurate solution for camera calibration in complex scenarios.