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    This study introduces a new heterogeneous computing architecture for 3D shape measurement using phase measuring profilometry (PMP). The system achieves real-time, high-accuracy results with significantly reduced computation time and power consumption.

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    Area of Science:

    • Computer Vision
    • Embedded Systems Engineering
    • Metrology

    Background:

    • Phase Measuring Profilometry (PMP) is crucial for 3D shape measurement.
    • Existing implementations often struggle with real-time processing demands and power efficiency in embedded applications.

    Purpose of the Study:

    • To develop a novel high-performance heterogeneous computation architecture for stereo structured light 3D measurement.
    • To achieve real-time and high-accuracy 3D shape measurement using the PMP algorithm on a Zynq UltraScale+ System on Chip (SoC).

    Main Methods:

    • Implementation of a heterogeneous computation architecture on a Zynq UltraScale+ SoC.
    • Utilizing the phase measuring profilometry (PMP) algorithm for 3D reconstruction.
    • Performance evaluation against software implementations on Raspberry Pi and high-end PCs.

    Main Results:

    • The proposed architecture achieved a calculation time of 14.11 ms for a 1280x1024 resolution PMP algorithm.
    • Demonstrated a 51x speed improvement over optimized Raspberry Pi software and 3x over a high-end PC.
    • Achieved 15x lower power consumption compared to existing solutions.

    Conclusions:

    • The novel architecture enables real-time, high-accuracy 3D shape measurement.
    • The system offers significant performance and power efficiency advantages for embedded applications.
    • The Zynq UltraScale+ SoC is well-suited for demanding real-time 3D metrology tasks.