  Accurate depth estimation from images is a fundamental task in many
applications including scene understanding and reconstruction. Existing
solutions for depth estimation often produce blurry approximations of low
resolution. This paper presents a convolutional neural network for computing a
high-resolution depth map given a single RGB image with the help of transfer
learning. Following a standard encoder-decoder architecture, we leverage
features extracted using high performing pre-trained networks when initializing
our encoder along with augmentation and training strategies that lead to more
accurate results. We show how, even for a very simple decoder, our method is
able to achieve detailed high-resolution depth maps. Our network, with fewer
parameters and training iterations, outperforms state-of-the-art on two
datasets and also produces qualitatively better results that capture object
boundaries more faithfully. Code and corresponding pre-trained weights are made
publicly available.
