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    A new method precisely calibrates rotation axis parameters for 3D reconstruction systems. This technique improves 3D stitching accuracy by considering orientation and position, outperforming traditional methods.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Metrology

    Background:

    • Accurate rotation axis calibration is vital for 3D reconstruction and stitching.
    • Traditional circle fitting methods have limitations in precision and noise sensitivity.

    Purpose of the Study:

    • To propose a novel parameters estimation-based method for calibrating rotation axis parameters.
    • To enhance the precision and robustness of rotation axis calibration in 3D reconstruction systems.

    Main Methods:

    • A parameters estimation-based approach using 2D planar targets.
    • Incorporation of both orientation and position information for calibration.
    • Leveraging spatial pose relationships to mitigate noise impact.

    Main Results:

    • The proposed method demonstrates superior precision performance compared to traditional circle fitting.
    • Effective mitigation of noise impact leads to more accurate calibration.
    • Error validation and 3D reconstruction experiments confirm the method's effectiveness.

    Conclusions:

    • The novel parameters estimation-based method significantly enhances rotation axis parameter calibration.
    • The approach is effective and applicable for improving 3D reconstruction systems.
    • This method offers a more precise and robust solution for critical calibration tasks.