  This paper proposes a new residual convolutional neural network (CNN)
architecture for single image depth estimation. Compared with existing deep CNN
based methods, our method achieves much better results with fewer training
examples and model parameters. The advantages of our method come from the usage
of dilated convolution, skip connection architecture and soft-weight-sum
inference. Experimental evaluation on the NYU Depth V2 dataset shows that our
method outperforms other state-of-the-art methods by a margin.
