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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Conventional 3D imaging systems often require bulky optical components.
    • Metasurfaces offer flexible wavefront modulation and integrate well with computer vision algorithms.
    • There is a need for compact, high-performance planar 3D optical imaging devices.

    Purpose of the Study:

    • To propose a large field-of-view (FOV) structured light dot array projection device based on a metasurface.
    • To develop a novel local bright spot gray scale matching algorithm for depth extraction in 3D imaging.
    • To enable compact and efficient 3D optical imaging for advanced applications.

    Main Methods:

    • Designed a metasurface-based structured light dot array projector with a 2π FOV.
    • Projected coded point clouds in Fourier space.
    • Developed and applied a local bright spot gray scale matching algorithm for depth extraction, optimizing feature matching and reducing computational complexity.

    Main Results:

    • Achieved a large 2π FOV for 3D imaging.
    • The gray scale matching algorithm simplified data processing and improved depth extraction accuracy.
    • Demonstrated enhanced tolerance to image quality fluctuations and reduced computational load.

    Conclusions:

    • The proposed metasurface-based 3D imaging system offers a compact and high-performance solution.
    • The novel algorithm enhances depth extraction efficiency and robustness.
    • This advancement paves the way for next-generation planar 3D optical imaging devices in computer vision and AI.