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    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • Image Processing

    Background:

    • Challenges in projector-camera system calibration include low precision and noise sensitivity at angular points.
    • Accurate perception of depth anisotropic surfaces is crucial for autonomous systems.
    • Existing methods struggle with adaptive perceptual projection under varying conditions.

    Purpose of the Study:

    • To propose an improved projection correction method for depth anisotropic surfaces.
    • To enhance the adaptive perceptual projection capabilities of projection equipment.
    • To achieve subpixel-level geometric distortion correction accuracy.

    Main Methods:

    • Developed an angular-point subpixel detection algorithm using color information guidance.
    • Proposed a projection geometry correction algorithm based on topology analysis.
    • Utilized homography matrix computation for different regions of anisotropic surfaces.

    Main Results:

    • Achieved identification accuracy of 0.25 pixels for angular points.
    • Demonstrated high planar parallelism and linear perpendicularity.
    • Confirmed subpixel-level geometric distortion correction precision for depth anisotropic surfaces.

    Conclusions:

    • The proposed method significantly improves the precision of angular-point detection and geometric distortion correction.
    • The technique enhances the adaptive perceptual projection of depth anisotropic surfaces.
    • The results confirm the method's effectiveness in achieving high imaging consistency.