  Recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance. However, they require costly
ground truth annotations during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps. We introduce a new network architecture, named Progressive Fusion
Network (PFN), that is specifically designed for binocular stereo depth
estimation. This network is based on a multi-scale refinement strategy that
combines the information provided by both stereo views. In addition, we propose
to stack twice this network in order to form a cycle. This cycle approach can
be interpreted as a form of data-augmentation since, at training time, the
network learns both from the training set images (in the forward half-cycle)
but also from the synthesized images (in the backward half-cycle). The
architecture is jointly trained with adversarial learning. Extensive
experiments on the publicly available datasets KITTI, Cityscapes and
ApolloScape demonstrate the effectiveness of the proposed model which is
competitive with other unsupervised deep learning methods for depth prediction.
