  Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction
and visual SLAM. Existing deep learning-based approaches formulate the problem
by either recovering absolute pose scales from two consecutive frames or
predicting a depth map from a single image, both of which are ill-posed
problems. In contrast, we propose to revisit the problem of deep two-view SfM
by leveraging the well-posedness of the classic pipeline. Our method consists
of 1) an optical flow estimation network that predicts dense correspondences
between two frames; 2) a normalized pose estimation module that computes
relative camera poses from the 2D optical flow correspondences, and 3) a
scale-invariant depth estimation network that leverages epipolar geometry to
reduce the search space, refine the dense correspondences, and estimate
relative depth maps. Extensive experiments show that our method outperforms all
state-of-the-art two-view SfM methods by a clear margin on KITTI depth, KITTI
VO, MVS, Scenes11, and SUN3D datasets in both relative pose and depth
estimation.
