  Estimating fundamental matrices is a classic problem in computer vision.
Traditional methods rely heavily on the correctness of estimated key-point
correspondences, which can be noisy and unreliable. As a result, it is
difficult for these methods to handle image pairs with large occlusion or
significantly different camera poses. In this paper, we propose novel neural
network architectures to estimate fundamental matrices in an end-to-end manner
without relying on point correspondences. New modules and layers are introduced
in order to preserve mathematical properties of the fundamental matrix as a
homogeneous rank-2 matrix with seven degrees of freedom. We analyze performance
of the proposed models using various metrics on the KITTI dataset, and show
that they achieve competitive performance with traditional methods without the
need for extracting correspondences.
