  Monocular depth estimation has been extensively explored based on deep
learning, yet its accuracy and generalization ability still lag far behind the
stereo-based methods. To tackle this, a few recent studies have proposed to
supervise the monocular depth estimation network by distilling disparity maps
as proxy ground-truths. However, these studies naively distill the stereo
knowledge without considering the comparative advantages of stereo-based and
monocular depth estimation methods. In this paper, we propose to selectively
distill the disparity maps for more reliable proxy supervision. Specifically,
we first design a decoder (MaskDecoder) that learns two binary masks which are
trained to choose optimally between the proxy disparity maps and the estimated
depth maps for each pixel. The learned masks are then fed to another decoder
(DepthDecoder) to enforce the estimated depths to learn from only the masked
area in the proxy disparity maps. Additionally, a Teacher-Student module is
designed to transfer the geometric knowledge of the StereoNet to the MonoNet.
Extensive experiments validate our methods achieve state-of-the-art performance
for self- and proxy-supervised monocular depth estimation on the KITTI dataset,
even surpassing some of the semi-supervised methods.
