  Depth prediction plays a key role in understanding a 3D scene. Several
techniques have been developed throughout the years, among which Convolutional
Neural Network has recently achieved state-of-the-art performance on estimating
depth from a single image. However, traditional CNNs suffer from the lower
resolution and information loss caused by the pooling layers. And oversized
parameters generated from fully connected layers often lead to a exploded
memory usage problem. In this paper, we present an advanced Dilated Fully
Convolutional Neural Network to address the deficiencies. Taking advantages of
the exponential expansion of the receptive field in dilated convolutions, our
model can minimize the loss of resolution. It also reduces the amount of
parameters significantly by replacing the fully connected layers with the fully
convolutional layers. We show experimentally on NYU Depth V2 datasets that the
depth prediction obtained from our model is considerably closer to ground truth
than that from traditional CNNs techniques.
