A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion
<|reference_start|>A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion: We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers. STE is a fusion of the Tyler's M-estimator (TME) and a variant of the fast median subspace. Our theoretical analysis suggests that, under a common inlier-outlier model, STE can effectively recover the underlying subspace, even when it contains a smaller fraction of inliers relative to other methods in the field of robust subspace recovery. We apply STE in the context of Structure from Motion (SfM) in two ways: for robust estimation of the fundamental matrix and for the removal of outlying cameras, enhancing the robustness of the SfM pipeline. Numerical experiments confirm the state-of-the-art performance of our method in these applications. This research makes significant contributions to the field of robust subspace recovery, particularly in the context of computer vision and 3D reconstruction.<|reference_end|>
@article{yu2024a,
    title={A Subspace-Constrained Tyler's Estimator and its Applications to
  Structure from Motion},
    author={Feng Yu, Teng Zhang, Gilad Lerman},
    journal={arXiv preprint arXiv:2404.11590},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11590},
    primaryClass={cs.CV}
}