  Monocular depth estimation aims at estimating a pixelwise depth map for a
single image, which has wide applications in scene understanding and autonomous
driving. Existing supervised and unsupervised methods face great challenges.
Supervised methods require large amounts of depth measurement data, which are
generally difficult to obtain, while unsupervised methods are usually limited
in estimation accuracy. Synthetic data generated by graphics engines provide a
possible solution for collecting large amounts of depth data. However, the
large domain gaps between synthetic and realistic data make directly training
with them challenging. In this paper, we propose to use the stereo matching
network as a proxy to learn depth from synthetic data and use predicted stereo
disparity maps for supervising the monocular depth estimation network.
Cross-domain synthetic data could be fully utilized in this novel framework.
Different strategies are proposed to ensure learned depth perception capability
well transferred across different domains. Our extensive experiments show
state-of-the-art results of monocular depth estimation on KITTI dataset.
