Consensus Learning with Deep Sets for Essential Matrix Estimation
<|reference_start|>Consensus Learning with Deep Sets for Essential Matrix Estimation: Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate estimation by using complex network architectures that involve graphs, attention layers, and hard pruning steps. Here, we propose a simpler network architecture based on Deep Sets. Given a collection of point matches extracted from two images, our method identifies outlier point matches and models the displacement noise in inlier matches. A weighted DLT module uses these predictions to regress the essential matrix. Our network achieves accurate recovery that is superior to existing networks with significantly more complex architectures.<|reference_end|>
@article{moran2024consensus,
    title={Consensus Learning with Deep Sets for Essential Matrix Estimation},
    author={Dror Moran, Yuval Margalit, Guy Trostianetsky, Fadi Khatib, Meirav
  Galun, Ronen Basri},
    journal={arXiv preprint arXiv:2406.17414},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17414},
    primaryClass={cs.CV}
}