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    This study introduces an automated 3D scanning system for complex objects. Its adaptive viewpoint planning ensures efficient, complete surface coverage for high-fidelity 3D reconstruction.

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    Area of Science:

    • Robotics and Automation
    • Computer Vision
    • Metrology

    Background:

    • Achieving comprehensive 3D digitization of complex objects in real-world settings presents challenges.
    • Selecting minimal, effective viewpoints for complete surface coverage while minimizing redundancy is crucial for efficient 3D scanning.

    Purpose of the Study:

    • To develop a practical, automatic 3D scanning pipeline for comprehensive digitization of unknown objects.
    • To introduce an adaptive global viewpoint planning algorithm for maximizing coverage efficiency and reducing redundancy.
    • To present a high-fidelity, autonomous scanning system for accurate 3D reconstruction.

    Main Methods:

    • An adaptive global viewpoint planning algorithm utilizing a viewpoint energy field and field-guided optimization.
    • Integration of multi-resolution optical sensors with a robotic system for autonomous scanning.
    • A low-cost, high-precision two-step calibration method for the sensor-actuator setup.

    Main Results:

    • Demonstrated capability for accurate, complete, and fully automatic 3D reconstruction.
    • Successful experimental results on both public and real-world models.
    • Validation of the system's practical potential for optical metrology and real-scene digitization.

    Conclusions:

    • The developed system offers a practical solution for automated, high-fidelity 3D digitization.
    • The adaptive viewpoint planning algorithm enhances coverage efficiency and reduces redundancy in 3D scanning.
    • The system holds significant potential for applications in optical metrology and real-world digitization.