  There has been tremendous research progress in estimating the depth of a
scene from a monocular camera image. Existing methods for single-image depth
prediction are exclusively based on deep neural networks, and their training
can be unsupervised using stereo image pairs, supervised using LiDAR point
clouds, or semi-supervised using both stereo and LiDAR. In general,
semi-supervised training is preferred as it does not suffer from the weaknesses
of either supervised training, resulting from the difference in the cameras and
the LiDARs field of view, or unsupervised training, resulting from the poor
depth accuracy that can be recovered from a stereo pair. In this paper, we
present our research in single image depth prediction using semi-supervised
training that outperforms the state-of-the-art. We achieve this through a loss
function that explicitly exploits left-right consistency in a stereo
reconstruction, which has not been adopted in previous semi-supervised
training. In addition, we describe the correct use of ground truth depth
derived from LiDAR that can significantly reduce prediction error. The
performance of our depth prediction model is evaluated on popular datasets, and
the importance of each aspect of our semi-supervised training approach is
demonstrated through experimental results. Our deep neural network model has
been made publicly available.
