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    Area of Science:

    • Computer Vision
    • Geometric Computer Vision
    • Robotics

    Background:

    • Lens distortion and scene rectification are crucial in computer vision.
    • Existing methods often require complex computations or multiple feature correspondences.
    • Man-made environments frequently exhibit coplanar translated and reflected scene textures.

    Purpose of the Study:

    • To develop minimal solvers for joint radial lens undistortion and affine rectification.
    • To utilize local features from coplanar textures for efficient geometric correction.
    • To improve robustness and speed compared to state-of-the-art methods.

    Main Methods:

    • Employing algebraic geometry techniques for solver formulation.
    • Developing solvers that accommodate various local features and sampling strategies.
    • Proposing variants requiring only a single feature correspondence.

    Main Results:

    • Solvers are computationally efficient, stable, and small.
    • Demonstrated superior robustness to noise in synthetic and real-world experiments.
    • Achieved accurate rectification of imaged scene planes from challenging imagery.

    Conclusions:

    • The proposed minimal solvers offer a significant advancement in lens undistortion and affine rectification.
    • The solvers are effective for rectifying textured planes, even with challenging wide-field-of-view lenses.
    • Integration into an automated system highlights practical applicability in computer vision tasks.