  Convolutional Neural Networks have demonstrated superior performance on
single image depth estimation in recent years. These works usually use stacked
spatial pooling or strided convolution to get high-level information which are
common practices in classification task. However, depth estimation is a dense
prediction problem and low-resolution feature maps usually generate blurred
depth map which is undesirable in application. In order to produce high quality
depth map, say clean and accurate, we propose a network consists of a Dense
Feature Extractor (DFE) and a Depth Map Generator (DMG). The DFE combines
ResNet and dilated convolutions. It extracts multi-scale information from input
image while keeping the feature maps dense. As for DMG, we use attention
mechanism to fuse multi-scale features produced in DFE. Our Network is trained
end-to-end and does not need any post-processing. Hence, it runs fast and can
predict depth map in about 15 fps. Experiment results show that our method is
competitive with the state-of-the-art in quantitative evaluation, but can
preserve better structural details of the scene depth.
