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    This study introduces a novel multiscale approach for 3D single-photon LiDAR imaging, significantly reducing data volume and improving target detection. The method enables background-free 3D surface reconstruction and spectral classification, even in challenging conditions.

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    Area of Science:

    • Photonics and Imaging
    • Computational Imaging
    • Remote Sensing

    Background:

    • 3D single-photon LiDAR imaging is crucial but faces challenges with low signal-to-noise ratio and high data volumes.
    • Imaging through obscurants or high ambient light degrades data quality and complicates analysis.

    Purpose of the Study:

    • To develop a multiscale approach for efficient 3D surface detection from photon timing histograms.
    • To enable background-free 3D reconstruction and spectral classification of multispectral single-photon LiDAR data.

    Main Methods:

    • A multiscale processing approach for photon timing histograms to reduce data volume.
    • A hierarchical Bayesian model for 3D reconstruction and spectral classification.
    • Coordinate gradient descent algorithm for parameter estimation, promoting spatial correlation.

    Main Results:

    • Significant reduction in data volume for 3D single-photon LiDAR.
    • Generation of background-free surfaces for depth and reflectivity inference.
    • Demonstrated benefits on simulated and real data compared to existing methods.

    Conclusions:

    • The proposed multiscale approach enhances 3D single-photon LiDAR data analysis.
    • Effective target detection and reconstruction are achieved, even in noisy or complex environments.
    • The method provides a robust solution for multispectral single-photon LiDAR data processing.