  Current self-supervised methods for monocular depth estimation are largely
based on deeply nested convolutional networks that leverage stereo image pairs
or monocular sequences during a training phase. However, they often exhibit
inaccurate results around occluded regions and depth boundaries. In this paper,
we present a simple yet effective approach for monocular depth estimation using
stereo image pairs. The study aims to propose a student-teacher strategy in
which a shallow student network is trained with the auxiliary information
obtained from a deeper and more accurate teacher network. Specifically, we
first train the stereo teacher network by fully utilizing the binocular
perception of 3-D geometry and then use the depth predictions of the teacher
network to train the student network for monocular depth inference. This
enables us to exploit all available depth data from massive unlabeled stereo
pairs. We propose a strategy that involves the use of a data ensemble to merge
the multiple depth predictions of the teacher network to improve the training
samples by collecting non-trivial knowledge beyond a single prediction. To
refine the inaccurate depth estimation that is used when training the student
network, we further propose stereo confidence-guided regression loss that
handles the unreliable pseudo depth values in occlusion, texture-less region,
and repetitive pattern. To complement the existing dataset comprising outdoor
driving scenes, we built a novel large-scale dataset consisting of one million
outdoor stereo images taken using hand-held stereo cameras. Finally, we
demonstrate that the monocular depth estimation network provides feature
representations that are suitable for high-level vision tasks. The experimental
results for various outdoor scenarios demonstrate the effectiveness and
flexibility of our approach, which outperforms state-of-the-art approaches.
