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    This study introduces efficient solvers for two-view relative pose estimation using affine transformations. Minimal solutions are achieved with single affine correspondences under planar motion or known vertical direction, improving accuracy and reducing RANSAC iterations.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Geometric Deep Learning

    Background:

    • Relative pose estimation is crucial for 3D reconstruction and navigation.
    • Existing methods often require numerous feature correspondences or complex computations.
    • Exploiting affine transformations offers a more efficient approach.

    Purpose of the Study:

    • To develop minimal and efficient solvers for two-view relative pose estimation.
    • To leverage affine transformations for simplified camera pose recovery.
    • To improve accuracy and reduce computational cost in motion estimation.

    Main Methods:

    • Utilizing affine transformations between feature points for pose estimation.
    • Developing closed-form and least-squares solutions for calibrated and uncalibrated cameras.
    • Considering planar motion and known vertical direction constraints.
    • Implementing solvers for efficient outlier detection within RANSAC (Random Sample Consensus).

    Main Results:

    • Four minimal cases for relative pose estimation are presented.
    • Single affine correspondences are sufficient under specific motion assumptions.
    • Proposed methods demonstrate superior accuracy compared to state-of-the-art.
    • Reduced number of RANSAC iterations required for robust estimation.

    Conclusions:

    • The developed algorithms provide efficient and accurate solutions for two-view relative pose estimation.
    • Affine transformation-based methods offer significant advantages in computational efficiency and accuracy.
    • These methods are valuable for real-time applications in computer vision and robotics.