  This paper addresses the problem of estimating the depth map of a scene given
a single RGB image. We propose a fully convolutional architecture, encompassing
residual learning, to model the ambiguous mapping between monocular images and
depth maps. In order to improve the output resolution, we present a novel way
to efficiently learn feature map up-sampling within the network. For
optimization, we introduce the reverse Huber loss that is particularly suited
for the task at hand and driven by the value distributions commonly present in
depth maps. Our model is composed of a single architecture that is trained
end-to-end and does not rely on post-processing techniques, such as CRFs or
other additional refinement steps. As a result, it runs in real-time on images
or videos. In the evaluation, we show that the proposed model contains fewer
parameters and requires fewer training data than the current state of the art,
while outperforming all approaches on depth estimation. Code and models are
publicly available.
