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    Area of Science:

    • Computer Vision
    • Geometric Optics
    • 3D Reconstruction

    Background:

    • Accurate 3D surface reconstruction is crucial for various applications.
    • Existing methods often struggle with complex surfaces and steep inclinations.

    Purpose of the Study:

    • To develop a novel 3D freeform surface reconstruction method.
    • To address limitations of current techniques in handling complex geometries and steep slopes.

    Main Methods:

    • A one-shot color mapping imaging system captures reflectance direction fields.
    • A stripe multicolor filter with orthogonal orientations is employed.
    • A deep neural network (DNN) solves a derived equation without training data.

    Main Results:

    • The method accurately reconstructs 3D surfaces, including complex shapes.
    • Effectiveness is demonstrated on surfaces with concave, convex areas, and steep inclinations.
    • Numerical validations confirm the method's robustness.

    Conclusions:

    • The proposed method offers a robust solution for 3D freeform surface reconstruction.
    • It overcomes challenges posed by complex geometries and steep inclinations.
    • The DNN-driven approach eliminates the need for training data.