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Depth Sensing by Near-Infrared Light Absorption in Water.

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    Area of Science:

    • Optics
    • Computer Vision
    • Fluid Dynamics

    Background:

    • Accurate 3D depth recovery is crucial for underwater robotics and imaging.
    • Existing methods struggle with varying light conditions and object surface properties in water.

    Purpose of the Study:

    • To introduce a novel bispectral depth recovery method for underwater objects.
    • To demonstrate the robustness and accuracy of the proposed technique across different scenarios.

    Main Methods:

    • Utilized the Beer-Lambert model and light absorption differences at two near-infrared wavelengths.
    • Developed algorithms to correct for practical implementation nonidealities.
    • Constructed a coaxial bispectral depth imaging system with off-the-shelf hardware.

    Main Results:

    • Achieved accurate depth recovery irrespective of surface texture and reflectance.
    • Successfully demonstrated shape recovery of complex and dynamic underwater objects.
    • A trispectral variant showed improved robustness for challenging surface reflectances.

    Conclusions:

    • The proposed "shape from water" method offers a viable and robust solution for underwater 3D depth recovery.
    • The system is practical, cost-effective, and validated by experimental results.