  Unsupervised learning based depth estimation methods have received more and
more attention as they do not need vast quantities of densely labeled data for
training which are touch to acquire. In this paper, we propose a novel
unsupervised monocular video depth estimation method in natural scenes by
taking advantage of the state-of-the-art method of Zhou et al. which jointly
estimates depth and camera motion. Our method advances beyond the baseline
method by three aspects: 1) we add an additional signal as supervision to the
baseline method by incorporating left-right binocular images reconstruction
loss based on the estimated disparities, thus the left frame can be
reconstructed by the temporal frames and right frames of stereo vision; 2) the
network is trained by jointly using two kinds of view syntheses loss and
left-right disparity consistency regularization to estimate depth and pose
simultaneously; 3) we use the edge aware smooth L2 regularization to smooth the
depth map while preserving the contour of the target. Extensive experiments on
the KITTI autonomous driving dataset and Make3D dataset indicate the
superiority of our algorithm in training efficiency. We can achieve competitive
results with the baseline by only 3/5 times training data. The experimental
results also show that our method even outperforms the classical supervised
methods that using either ground truth depth or given pose for training.
