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    This study introduces a robust structured light (SL) 3D reconstruction method using redundant patterns to overcome ambient light interference. This technique enhances geometric accuracy in challenging environments.

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    Area of Science:

    • Computer Vision
    • 3D Reconstruction
    • Optical Metrology

    Background:

    • Structured Light (SL) systems are crucial for high-fidelity 3D geometry acquisition using active illumination.
    • Conventional SL systems struggle with performance degradation in environments with high ambient illumination.
    • Robustness against environmental noise is a key challenge in practical SL applications.

    Purpose of the Study:

    • To develop a general-purpose solution for improving the robustness of structured light systems in strong ambient illumination.
    • To systematically analyze redundant SL code design for high accuracy with minimal redundancy.
    • To enhance 3D reconstruction performance by integrating spatial context-aware disparity estimators and iterative refinement.

    Main Methods:

    • Projecting a redundant number of patterns to make signals more distinguishable from errors.
    • Systematic analysis of redundant SL code design rules for optimal accuracy and efficiency.
    • Integration of spatial context-aware disparity estimators and natural image priors.
    • Application of iterative error detection and refinement techniques.

    Main Results:

    • Redundant pattern projection enhances signal distinguishability, improving geometric recovery despite a lower signal-noise-ratio per frame.
    • Systematic analysis yielded design rules for efficient redundant codes.
    • Spatial context-aware disparity estimation and iterative refinement significantly boosted performance.
    • Demonstrated significant performance improvements in both simulations and challenging real-world scenes.

    Conclusions:

    • The proposed efficient redundant code structured light system offers improved robustness and accuracy in challenging lighting conditions.
    • The integration of advanced estimation and refinement techniques further enhances 3D reconstruction quality.
    • This approach provides a reliable solution for 3D geometry acquisition in diverse and difficult environments.