  Supervised deep learning often suffers from the lack of sufficient training
data. Specifically in the context of monocular depth map prediction, it is
barely possible to determine dense ground truth depth images in realistic
dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
present in the distance measurements, the calibration between sensors cannot be
perfect, and the measurements are typically much sparser than the camera
images. In this paper, we propose a novel approach to depth map prediction from
monocular images that learns in a semi-supervised way. While we use sparse
ground-truth depth for supervised learning, we also enforce our deep network to
produce photoconsistent dense depth maps in a stereo setup using a direct image
alignment loss. In experiments we demonstrate superior performance in depth map
prediction from single images compared to the state-of-the-art methods.
