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Multichannel autostereoscopic measurement system for micro-structured surfaces based on multi-scale depth fusion.

Yongqiang Yang, Chi Fai Cheung, Da Li

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    |September 23, 2025
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    A new multichannel autostereoscopic measurement (MAM) system enhances 3D reconstruction accuracy. By fusing data from 3D and 2D channels, it overcomes limitations of single-modality systems for precise micro-structure metrology.

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    Area of Science:

    • Optics
    • Computer Vision
    • Metrology

    Background:

    • Autostereoscopic technology enables precise metrology of micro-structured surfaces.
    • Single light field modality in existing systems limits performance.
    • Richer data is needed for improved depth estimation and 3D reconstruction.

    Purpose of the Study:

    • To develop a multichannel autostereoscopic measurement (MAM) system.
    • To enhance the accuracy and robustness of 3D reconstruction for micro-structured surfaces.
    • To address the limitations of single-modality autostereoscopic systems.

    Main Methods:

    • A 3D optical channel captures elemental images (EIs) from multiple viewpoints.
    • A 2D channel acquires high-resolution (HR) images.
    • Data fusion techniques and a deep learning network (UniDepth) are employed for depth estimation and 3D geometry.
    • Pyramid representation combines multi-scale depth information for precise reconstruction.

    Main Results:

    • The proposed MAM system integrates data from 3D and 2D channels.
    • Deep learning (UniDepth) estimates 3D geometry from HR images.
    • Data fusion compensates for deficiencies and enhances accuracy.
    • Multi-scale depth information improves 3D reconstruction precision.

    Conclusions:

    • The multichannel autostereoscopic measurement system provides richer data for depth estimation.
    • The system demonstrates improved quality and robustness in 3D reconstruction.
    • This approach overcomes the constraints of single light field modalities in autostereoscopic metrology.