提出混合校准方法，解决通用模型姿态歧义问题
Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline
- 设计线性求解器与非线性优化，消除通用校准中的姿态歧义
- 融合通用与参数化模型，提升外参精度并减少过拟合
- 适用于复杂场景，对不同镜头和噪声均表现稳定
传统离线相机标定通常采用参数化或通用相机模型。参数化模型选择依赖用户经验，不当模型会显著影响标定精度；而通用标定方法流程复杂，无法提供传统内参。本文揭示了通用标定中姿态解存在的不可逆歧义，影响后续姿态估计。为此，提出线性求解器与非线性优化方法以解决该问题，并引入全局优化的混合标定框架，将通用与参数化模型结合，提升通用标定的外参精度，同时缓解参数化标定中的过拟合与数值不稳定性。仿真与真实实验表明，该混合标定方法在多种镜头类型和噪声条件下均持续优于基准方法，有望成为复杂场景下可靠准确的相机标定方案。
原文摘要 · Abstract (English)
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.
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