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Related Experiment Videos

Robust Relative Rotation Averaging.

Avishek Chatterjee, Venu Madhav Govindu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 20, 2017
    PubMed
    Summary
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    This study introduces a robust and efficient method for averaging camera rotations in large-scale Structure from Motion (SfM) projects. The new approach improves both speed and accuracy compared to existing techniques.

    Area of Science:

    • Computer Vision
    • Robotics
    • 3D Reconstruction

    Background:

    • Structure from Motion (SfM) requires accurate camera pose estimation.
    • Relative rotation averaging is crucial for determining global camera orientations from pairwise measurements.
    • Existing methods can be computationally expensive and sensitive to outliers.

    Purpose of the Study:

    • To develop a generalized framework for robust and efficient relative rotation averaging in large-scale SfM.
    • To improve the accuracy and speed of determining absolute camera rotations.
    • To handle noisy relative rotation measurements effectively.

    Main Methods:

    • A generalized framework for relative rotation averaging is proposed.
    • The method employs robust loss functions and joint optimization of all camera rotations.

    Related Experiment Videos

  • A quasi-Newton optimization leads to an efficient iteratively reweighted least squares (IRLS) formulation in the Lie algebra of 3D rotations.

    • Main Results:

    • The proposed method demonstrates superior performance on large-scale datasets.
    • Outperforms existing methods in terms of both computational speed and accuracy.
    • Robustness to outliers is enhanced through the use of generalized loss functions.

    Conclusions:

    • The developed framework offers a significant advancement in relative rotation averaging for SfM.
    • The method provides a more efficient and accurate solution for large-scale 3D reconstruction.
    • Future work could explore extensions to include translation estimation.