  Nowadays, the majority of state of the art monocular depth estimation
techniques are based on supervised deep learning models. However, collecting
RGB images with associated depth maps is a very time consuming procedure.
Therefore, recent works have proposed deep architectures for addressing the
monocular depth prediction task as a reconstruction problem, thus avoiding the
need of collecting ground-truth depth. Following these works, we propose a
novel self-supervised deep model for estimating depth maps. Our framework
exploits two main strategies: refinement via cycle-inconsistency and
distillation. Specifically, first a \emph{student} network is trained to
predict a disparity map such as to recover from a frame in a camera view the
associated image in the opposite view. Then, a backward cycle network is
applied to the generated image to re-synthesize back the input image,
estimating the opposite disparity. A third network exploits the inconsistency
between the original and the reconstructed input frame in order to output a
refined depth map. Finally, knowledge distillation is exploited, such as to
transfer information from the refinement network to the student. Our extensive
experimental evaluation demonstrate the effectiveness of the proposed framework
which outperforms state of the art unsupervised methods on the KITTI benchmark.
