  Two-view structure from motion (SfM) is the cornerstone of 3D reconstruction
and visual SLAM (vSLAM). Many existing end-to-end learning-based methods
usually formulate it as a brute regression problem. However, the inadequate
utilization of traditional geometry model makes the model not robust in unseen
environments. To improve the generalization capability and robustness of
end-to-end two-view SfM network, we formulate the two-view SfM problem as a
maximum likelihood estimation (MLE) and solve it with the proposed framework,
denoted as DeepMLE. First, we propose to take the deep multi-scale correlation
maps to depict the visual similarities of 2D image matches decided by
ego-motion. In addition, in order to increase the robustness of our framework,
we formulate the likelihood function of the correlations of 2D image matches as
a Gaussian and Uniform mixture distribution which takes the uncertainty caused
by illumination changes, image noise and moving objects into account.
Meanwhile, an uncertainty prediction module is presented to predict the
pixel-wise distribution parameters. Finally, we iteratively refine the depth
and relative camera pose using the gradient-like information to maximize the
likelihood function of the correlations. Extensive experimental results on
several datasets prove that our method significantly outperforms the
state-of-the-art end-to-end two-view SfM approaches in accuracy and
generalization capability.
