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Advancing 3D surface imaging: single-axis structured light illumination plenoptic camera with machine learning

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    We developed a novel single-axis 3D structured light illumination (SLI) plenoptic camera. This 3D imaging system overcomes surface texture limitations and enhances resolution and range using machine learning algorithms.

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    Area of Science:

    • Optics and Photonics
    • 3D Imaging Technologies
    • Machine Learning Applications

    Background:

    • Structured light illumination (SLI) is a 3D surface imaging technique.
    • Traditional SLI often requires off-axis projection for depth measurement.
    • Plenoptic imaging (PI) offers single-axis 3D imaging but relies on surface texture.

    Purpose of the Study:

    • To present a single-axis 3D SLI plenoptic camera.
    • To combine single-axis imaging benefits with surface-texture independence.
    • To explore machine learning for enhanced 3D SLI performance.

    Main Methods:

    • Developed a coaxial SLI 3D plenoptic camera.
    • Implemented traditional feature tracking algorithms.
    • Integrated machine learning (ML) algorithms for data processing.

    Main Results:

    • Achieved depth sensitivity down to 0.2% dz/z0.
    • Demonstrated independence from surface texture.
    • Showcased enhanced resolution and measurement range via ML.

    Conclusions:

    • The single-axis SLI 3D plenoptic camera is suitable for in-situ topographical measurements.
    • This technology is effective in challenging environments like high temperatures or constrained geometries.
    • The system performs well on objects lacking trackable surface texture.