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Robust Single-shot Fringe Projection Profilometry Based on Morphological Component Analysis.

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    This study introduces a novel single-shot fringe projection profilometry (FPP) algorithm. It effectively extracts fringe patterns from textured objects by enhancing morphological component analysis (MCA) for improved 3D surface reconstruction.

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    Area of Science:

    • Optical Metrology
    • 3D Imaging
    • Computer Vision

    Background:

    • Fringe projection profilometry (FPP) captures 3D surface data using projected fringe patterns.
    • Object texture in fringe images complicates fringe pattern extraction in single-shot FPP.
    • Traditional methods struggle with highly textured surfaces, limiting FPP applications.

    Purpose of the Study:

    • To develop a robust single-shot FPP algorithm for accurate fringe pattern extraction from textured objects.
    • To simultaneously estimate object texture and fringe patterns.
    • To improve the performance of single-shot FPP on challenging surfaces.

    Main Methods:

    • Proposed a novel single-shot FPP algorithm based on enhanced morphological component analysis (MCA).
    • Utilized the low-rank structure of fringe sparse representation for adaptive thresholding.
    • Integrated simultaneous estimation of fringe patterns and object texture.

    Main Results:

    • Successfully extracted fringe patterns from highly textured surfaces, overcoming limitations of conventional methods.
    • Achieved simultaneous estimation of object texture alongside fringe patterns.
    • Demonstrated superior performance compared to traditional single-shot FPP approaches in experiments.

    Conclusions:

    • The enhanced MCA-based FPP algorithm provides a robust solution for single-shot 3D surface reconstruction.
    • Simultaneous texture and fringe extraction broadens FPP applicability.
    • The proposed method significantly improves accuracy and reliability on complex object surfaces.