  It is a classical compute vision problem to obtain real scene depth maps by
using a monocular camera, which has been widely concerned in recent years.
However, training this model usually requires a large number of artificially
labeled samples. To solve this problem, some researchers use a self-supervised
learning model to overcome this problem and reduce the dependence on manually
labeled data. Nevertheless, the accuracy and reliability of these methods have
not reached the expected standard. In this paper, we propose a new
self-supervised learning method based on cascade networks. Compared with the
previous self-supervised methods, our method has improved accuracy and
reliability, and we have proved this by experiments. We show a cascaded neural
network that divides the target scene into parts of different sight distances
and trains them separately to generate a better depth map. Our approach is
divided into the following four steps. In the first step, we use the
self-supervised model to estimate the depth of the scene roughly. In the second
step, the depth of the scene generated in the first step is used as a label to
divide the scene into different depth parts. The third step is to use models
with different parameters to generate depth maps of different depth parts in
the target scene, and the fourth step is to fuse the depth map. Through the
ablation study, we demonstrated the effectiveness of each component
individually and showed high-quality, state-of-the-art results in the KITTI
benchmark.
