is likely to improve many tasks including depth estimation.

-   Experimental results on NYUD-V2 and KITTI illustrate that our method achieves state-of-the-art performance.

Related Work
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**Monocular Depth Prediction.** Depth prediction from images is a long-standing problem. Previous work can be divided into active methods and passive methods. The former ones use the assistant optical information for prediction, such as coded patterns [@yin2017high], while the latter ones completely focus on image contents. Monocular depth prediction [@cao2017estimating; @eigen2015predicting; @eigen2014depth; @liu2016learning; @zheng2018net] has been extensively studied recently. As limited geometric information can be directly extracted from the monocular image, it is essentially an ill-posed problem. Recently, owing to the structural features from very deep convolution neural network, such as ResNet [@he2016deep], various DCNN-based methods learn to predict depth with deep CNN features. Fu  [@fu2018deep] proposed an encoder-decoder network, which extracts multi-scale features from the encoder and is trained in an end-to-end manner without iterative refinement. They achieved state-of-the-art performance on several datasets. Jiao [@jiao2018look] proposed an attention-driven loss, which merges the semantic priors to improve the prediction precision on unbalanced distribution datasets. Most previous methods only adopted the pixel-wise depth supervision to train a network. By contrast, Liu [@liu2016learning] combined DCNN with 