In IEEE transactions on pattern analysis and machine intelligence
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 stereo depth estimation. This network is based on a multi-scale refinement strategy that combines the information provided by both images. 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 outperforms previous unsupervised deep learning methods for depth prediction.
Pilzer Andrea, Lathuiliere Stephane, Xu Dan, Puscas Mihai Marian, Ricci Elisa, Sebe Nicu