@inproceedings{
moran2024consensus,
title={Consensus Learning with Deep Sets for Essential Matrix Estimation},
author={Dror Moran and Yuval Margalit and Guy Trostianetsky and Fadi Khatib and Meirav Galun and Ronen Basri},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=6sIOBDwr6d}
}
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.
Consensus Learning with Deep Sets for Essential Matrix Estimation
Dror Moran Yuval Margalit Guy Trostianetsky Fadi Khatib Meirav Galun Ronen Basri