  Recovering the scene depth from a single image is an ill-posed problem that
requires additional priors, often referred to as monocular depth cues, to
disambiguate different 3D interpretations. In recent works, those priors have
been learned in an end-to-end manner from large datasets by using deep neural
networks. In this paper, we propose guiding depth estimation to favor planar
structures that are ubiquitous especially in indoor environments. This is
achieved by incorporating a non-local coplanarity constraint to the network
with a novel attention mechanism called depth-attention volume (DAV).
Experiments on two popular indoor datasets, namely NYU-Depth-v2 and ScanNet,
show that our method achieves state-of-the-art depth estimation results while
using only a fraction of the number of parameters needed by the competing
methods.
