  This paper aims at understanding the role of multi-scale information in the
estimation of depth from monocular images. More precisely, the paper
investigates four different deep CNN architectures, designed to explicitly make
use of multi-scale features along the network, and compare them to a
state-of-the-art single-scale approach. The paper also shows that involving
multi-scale features in depth estimation not only improves the performance in
terms of accuracy, but also gives qualitatively better depth maps. Experiments
are done on the widely used NYU Depth dataset, on which the proposed method
achieves state-of-the-art performance.
