Unsupervised monocular depth estimation has made great progress after deep
 learning is involved. Training with binocular stereo images is considered as a
 good option as the data can be easily obtained. However, the depth or disparity
 prediction results show poor performance for the object boundaries. The main
 reason is related to the handling of occlusion areas during the training. In this paper,
 we propose a novel method to overcome this issue. Exploiting disparity maps
 property, we generate an occlusion mask to block the back-propagation of the occlusion
 areas during image warping. We also design new networks with flipped
 stereo images to induce the networks to learn occluded boundaries. It shows that
 our method achieves clearer boundaries and better evaluation results on KITTI
 driving dataset and Virtual KITTI dataset.
This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion