HARA: A Hierarchical Approach for Robust Rotation Averaging

Paper, Video, Supplementary material

In this repository, we provide the implementation of HARA. If you use our code, please cite it as follows:

@InProceedings{Lee_2022_CVPR,
    author    = {Lee, Seong Hun and Civera, Javier},
    title     = {{HARA}: A Hierarchical Approach for Robust Rotation Averaging},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {15777--15786}
}

Update (January 8th 2023)

This recent change in the code leads to a significant speedup of the local optimization step compared to the version used for the CVPR paper. We thank Chitturi Sidhartha, the first author of a 3DV paper titled 'It Is All In The Weights: Robust Rotation Averaging Revisited', for the discussion that led to this finding.

Quick start

Run Test_HARA.m to try it on a synthetic data WITHOUT using the number of inlier matches.

Main functions:

1. CreateSyntheticData.m: Generate a synthetic dataset, as described in the main paper.
2. RunHARA.m: Run HARA without using the number of inlier matches.
3. RunHARA_usingNumberOfInlierMatches.m: Run HARA using the number of inlier matches. Note that we only provide the function, without the test script or a sample dataset. This function is quite similar to RunHARA.m, so it shouldn't be too difficult to use on your own dataset.