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    Area of Science:

    • Optics
    • Computer Vision
    • Machine Learning

    Background:

    • Chromatic aberration causes focal length to vary with wavelength, focusing different colors at different distances.
    • Existing depth estimation methods lack specialized datasets for spectral information.

    Purpose of the Study:

    • To propose and validate a novel depth estimation network leveraging spectral chromatic aberration.
    • To address the absence of public datasets for spectral depth estimation.

    Main Methods:

    • Constructed a large-aperture spectral imaging system to capture hyperspectral images utilizing inherent chromatic aberration.
    • Developed an encoder-decoder network with consecutive dilated convolutions (cdc) and local-global features interaction (LGFI) blocks.
    • Integrated skip connections for improved depth prediction accuracy by concatenating intermediate depth maps with encoder features.

    Main Results:

    • Achieved relative errors below 10% in depth estimation.
    • Demonstrated accuracy exceeding 85% on the custom-collected dataset.
    • Validated the effectiveness of the spectral chromatic aberration-based depth estimation network.

    Conclusions:

    • The proposed spectral chromatic aberration-based depth estimation network effectively utilizes spectral information for accurate depth mapping.
    • The developed hyperspectral imaging system and dataset enable further research in spectral depth estimation.
    • This approach offers a promising solution for depth estimation in scenarios where spectral data is available.