  A new unsupervised learning method of depth and ego-motion using multiple
masks from monocular video is proposed in this paper. The depth estimation
network and the ego-motion estimation network are trained according to the
constraints of depth and ego-motion without truth values. The main contribution
of our method is to carefully consider the occlusion of the pixels generated
when the adjacent frames are projected to each other, and the blank problem
generated in the projection target imaging plane. Two fine masks are designed
to solve most of the image pixel mismatch caused by the movement of the camera.
In addition, some relatively rare circumstances are considered, and repeated
masking is proposed. To some extent, the method is to use a geometric
relationship to filter the mismatched pixels for training, making unsupervised
learning more efficient and accurate. The experiments on KITTI dataset show our
method achieves good performance in terms of depth and ego-motion. The
generalization capability of our method is demonstrated by training on the
low-quality uncalibrated bike video dataset and evaluating on KITTI dataset,
and the results are still good.
