  Depth cues have been proved very useful in various computer vision and
robotic tasks. This paper addresses the problem of monocular depth estimation
from a single still image. Inspired by the effectiveness of recent works on
multi-scale convolutional neural networks (CNN), we propose a deep model which
fuses complementary information derived from multiple CNN side outputs.
Different from previous methods using concatenation or weighted average
schemes, the integration is obtained by means of continuous Conditional Random
Fields (CRFs). In particular, we propose two different variations, one based on
a cascade of multiple CRFs, the other on a unified graphical model. By
designing a novel CNN implementation of mean-field updates for continuous CRFs,
we show that both proposed models can be regarded as sequential deep networks
and that training can be performed end-to-end. Through an extensive
experimental evaluation, we demonstrate the effectiveness of the proposed
approach and establish new state of the art results for the monocular depth
estimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and
KITTI.
