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Object phase-valid region segmentation method for FPP-based three-dimensional measurement.

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    A new Dual-Wavelet Feature Interaction Network (DWFI-Net) enables precise object segmentation for 3D measurements in fringe projector profilometry (FPP). This method effectively removes background and shadows, crucial for real-world industrial applications.

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    Area of Science:

    • Computer Vision
    • 3D Metrology
    • Image Processing

    Background:

    • Existing fringe projector profilometry (FPP) methods often require ideal conditions or full-scene reconstruction.
    • Automated object detection and segmentation are critical for object-level measurements in industrial settings.
    • Challenges include easily segmenting objects from complex backgrounds and shadows.

    Purpose of the Study:

    • To develop an automated method for object phase-valid region segmentation in FPP.
    • To enable accurate object-level 3D measurements in real industrial scenarios.
    • To effectively remove background and shadow interferences.

    Main Methods:

    • Introduced a Dual-Wavelet Feature Interaction Network (DWFI-Net).
    • Utilized modulation and wrapped phase maps as innovative inputs.
    • Developed an Adaptive Wavelet Feature Interaction (AWFI) module using Discrete Wavelet Transformation (DWT).
    • Implemented an Edge-aware Discrete Cosine Transformation (EDCT) module as a decoder using Discrete Cosine Transformation (DCT).

    Main Results:

    • DWFI-Net successfully performs object phase-valid region segmentation.
    • The network effectively removes background and shadow regions.
    • Demonstrated superiority over existing methods through qualitative and quantitative experiments.
    • Validated effectiveness for object-level 3D measurement using FPP.

    Conclusions:

    • DWFI-Net offers a robust solution for object segmentation in FPP.
    • The proposed method enhances the applicability of FPP in industrial environments.
    • The innovative use of dual-wavelet features and adaptive fusion improves measurement accuracy.