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    A novel cross-structured light sensor architecture improves 3D reconstruction. An enhanced center of mass method offers fast and robust laser stripe extraction for detailed 3D imaging.

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    Area of Science:

    • Computer Vision
    • Optical Engineering
    • Metrology

    Background:

    • Accurate 3D reconstruction is crucial for various applications.
    • Traditional structured light sensors face limitations in capturing complex geometries.
    • Efficient laser stripe extraction is key to precise 3D data acquisition.

    Purpose of the Study:

    • To develop an advanced cross-structured light sensor architecture for enhanced 3D reconstruction.
    • To introduce an improved center of mass method for robust and rapid laser stripe extraction.
    • To evaluate the accuracy and information richness of the proposed 3D reconstruction system.

    Main Methods:

    • Established a cross-structured light sensor setup.
    • Implemented an improved center of mass algorithm utilizing Hessian matrix-derived normal directions for laser stripe extraction.
    • Categorized laser stripe center points based on calculated normal directions.
    • Performed 3D reconstruction of a cylinder and a shoe last for accuracy and data completeness analysis.

    Main Results:

    • The proposed laser stripe extraction method demonstrated high speed and robustness.
    • Achieved a relative accuracy of less than 0.15 mm in cylinder 3D reconstruction.
    • Demonstrated that cross-structured light sensors capture more comprehensive data compared to single-structured light sensors, as evidenced by shoe last reconstruction.

    Conclusions:

    • The developed cross-structured light sensor architecture and improved center of mass method provide a fast, robust, and accurate solution for 3D reconstruction.
    • The system's ability to capture richer information makes it suitable for complex object modeling.
    • This approach advances the field of optical metrology and 3D imaging.