  Self-supervised monocular depth estimation, aiming to learn scene depths from
single images in a self-supervised manner, has received much attention
recently. In spite of recent efforts in this field, how to learn accurate scene
depths and alleviate the negative influence of occlusions for self-supervised
depth estimation, still remains an open problem. Addressing this problem, we
firstly empirically analyze the effects of both the continuous and discrete
depth constraints which are widely used in the training process of many
existing works. Then inspired by the above empirical analysis, we propose a
novel network to learn an Occlusion-aware Coarse-to-Fine Depth map for
self-supervised monocular depth estimation, called OCFD-Net. Given an arbitrary
training set of stereo image pairs, the proposed OCFD-Net does not only employ
a discrete depth constraint for learning a coarse-level depth map, but also
employ a continuous depth constraint for learning a scene depth residual,
resulting in a fine-level depth map. In addition, an occlusion-aware module is
designed under the proposed OCFD-Net, which is able to improve the capability
of the learnt fine-level depth map for handling occlusions. Experimental
results on KITTI demonstrate that the proposed method outperforms the
comparative state-of-the-art methods under seven commonly used metrics in most
cases. In addition, experimental results on Make3D demonstrate the
effectiveness of the proposed method in terms of the cross-dataset
generalization ability under four commonly used metrics. The code is available
at https://github.com/ZM-Zhou/OCFD-Net_pytorch.
