  Recent works have shown the benefit of integrating Conditional Random Fields
(CRFs) models into deep architectures for improving pixel-level prediction
tasks. Following this line of research, in this paper we introduce a novel
approach for monocular depth estimation. Similarly to previous works, our
method employs a continuous CRF to fuse multi-scale information derived from
different layers of a front-end Convolutional Neural Network (CNN). Differently
from past works, our approach benefits from a structured attention model which
automatically regulates the amount of information transferred between
corresponding features at different scales. Importantly, the proposed attention
model is seamlessly integrated into the CRF, allowing end-to-end training of
the entire architecture. Our extensive experimental evaluation demonstrates the
effectiveness of the proposed method which is competitive with previous methods
on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2
dataset.
