Abstract. Signiicant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs). While absolute features, such as edges and textures, could be efectively extracted, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent CNN-based methods. To overcome this limitation, we explicitly model the relationships of diferent image locations with an ainity layer and combine absolute and relative features in an end-to-end network. In addition, we consider prior knowledge that major depth changes lie in the vertical direction, and thus, it is beneicial to capture long-range vertical features for reined depth estimation. In the proposed algorithm we introduce vertical pooling to aggregate image features vertically to improve the depth accuracy. Furthermore, since the Lidar depth ground truth is quite sparse, we enhance the depth labels by generating high-quality dense depth maps with of-the-shelf stereo matching method taking left-right image pairs as input. We also integrate multi-scale structure in our network to obtain global understanding of the image depth and exploit residual learning to help depth reinement. We demonstrate that the proposed algorithm performs favorably against state-of-the-art methods both qualitatively and quantitatively on the KITTI driving dataset.