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    Area of Science:

    • Computer Vision
    • Geometric Computer Vision
    • Robotics

    Background:

    • Affine correspondences (ACs) are increasingly used in computer vision, offering advantages over point correspondences (PCs).
    • A single AC typically provides three constraints for relative pose estimation.
    • Multi-perspective camera systems are vital for autonomous vehicles and augmented reality.

    Purpose of the Study:

    • To develop the first comprehensive suite of minimal solvers for 6DoF relative pose estimation using only two ACs in multi-camera systems.
    • To address the need for robust model fitting in scenarios with limited correspondences.
    • To enhance computational efficiency and solution compactness in relative pose estimation.

    Main Methods:

    • Analysis of all configurations for two ACs across two views.
    • Development of minimal solvers for all identified minimal cases.
    • Utilizing the hidden variable technique to eliminate translation parameters.
    • Representing rotation using Cayley parameters or quaternions.
    • Introducing novel constraints for the generalized relative pose problem.

    Main Results:

    • The proposed methods provide a comprehensive suite of minimal solvers for 6DoF relative pose estimation using two ACs.
    • Solvers are derived for all minimal configurations of two ACs.
    • Novel constraints lead to more compact solvers with fewer solutions.
    • Experimental validation on synthetic and real-world data confirms high effectiveness and computational efficiency.

    Conclusions:

    • Affine correspondence-based solvers offer a powerful and efficient solution for relative pose estimation in multi-camera systems.
    • The developed minimal solvers are valuable for robust geometric computations in computer vision.
    • This work advances the state-of-the-art in relative pose estimation for applications like autonomous driving and augmented reality.