Abstract. 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