Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline
This work addresses camera calibration challenges for computer vision applications, offering a more reliable solution in complex scenarios, though it is incremental as it builds on existing parametric and generic models.
The paper tackled the problem of pose ambiguity in generic camera calibration methods, which negatively affects pose estimation, by proposing a linear solver and nonlinear optimization to resolve it, and introduced a hybrid calibration method that improves extrinsic parameter accuracy and reduces overfitting, achieving consistent performance across various lens types and noise levels.
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.