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

TL;DR

This paper introduces a hybrid camera calibration method that combines generic and parametric models to improve accuracy and robustness, addressing pose ambiguity issues in generic calibration.

Contribution

It proposes a linear solver and nonlinear optimization to resolve pose ambiguity and develops a hybrid calibration pipeline integrating both models.

Findings

Enhanced extrinsic parameter accuracy in generic calibration.

Robust performance across various lens types and noise levels.

Mitigation of overfitting and numerical instability in parametric calibration.

Abstract

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.