  Monocular visual odometry approaches that purely rely on geometric cues are
prone to scale drift and require sufficient motion parallax in successive
frames for motion estimation and 3D reconstruction. In this paper, we propose
to leverage deep monocular depth prediction to overcome limitations of
geometry-based monocular visual odometry. To this end, we incorporate deep
depth predictions into Direct Sparse Odometry (DSO) as direct virtual stereo
measurements. For depth prediction, we design a novel deep network that refines
predicted depth from a single image in a two-stage process. We train our
network in a semi-supervised way on photoconsistency in stereo images and on
consistency with accurate sparse depth reconstructions from Stereo DSO. Our
deep predictions excel state-of-the-art approaches for monocular depth on the
KITTI benchmark. Moreover, our Deep Virtual Stereo Odometry clearly exceeds
previous monocular and deep learning based methods in accuracy. It even
achieves comparable performance to the state-of-the-art stereo methods, while
only relying on a single camera.
