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Homography-Based Minimal-Case Relative Pose Estimation With Known Gravity Direction.

Yaqing Ding, Jian Yang, Jean Ponce

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    This summary is machine-generated.

    This study introduces a new method for determining camera relative pose using gravity direction, crucial for mobile devices. The approach efficiently solves problems with unknown focal lengths, improving computer vision accuracy.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Sensor Fusion

    Background:

    • Relative pose estimation is fundamental in computer vision for tasks like 3D reconstruction and navigation.
    • Camera-IMU systems, common in mobile devices, provide gravity vector information aiding pose estimation.
    • Existing methods often require known focal lengths or more points, limiting applicability.

    Purpose of the Study:

    • To develop a novel, efficient approach for two-view relative pose estimation using homography and known gravity direction.
    • To address minimal-case problems relevant to smartphone and tablet camera systems.
    • To solve for calibrated, semi-calibrated (unknown focal length), and fully uncalibrated (unknown focal lengths) scenarios.

    Main Methods:

    • Exploitation of the rank-1 constraint between the Euclidean homography matrix and its corresponding rotation.
    • Development of an efficient two-step solution.
    • Application of the hidden variable technique to transform problems into polynomial eigenvalue problems.

    Main Results:

    • Derivation of new 3.5-point, 3.5-point, and 4-point solvers for scenarios with unknown and potentially different focal lengths.
    • Demonstration of an efficient two-step solution applicable to both calibrated and semi-calibrated cases.
    • Validation through detailed analyses and comparisons with existing 6- and 7-point solvers, including real-world smartphone image results.

    Conclusions:

    • The proposed homography-based method provides an efficient and robust solution for two-view relative pose problems leveraging known gravity.
    • The derived solvers offer improved flexibility for scenarios with unknown focal lengths, expanding applicability in mobile robotics and augmented reality.
    • The approach demonstrates practical effectiveness, validated by experiments on smartphone imagery.