  Monocular depth estimation plays a crucial role in 3D recognition and
understanding. One key limitation of existing approaches lies in their lack of
structural information exploitation, which leads to inaccurate spatial layout,
discontinuous surface, and ambiguous boundaries. In this paper, we tackle this
problem in three aspects. First, to exploit the spatial relationship of visual
features, we propose a structure-aware neural network with spatial attention
blocks. These blocks guide the network attention to global structures or local
details across different feature layers. Second, we introduce a global focal
relative loss for uniform point pairs to enhance spatial constraint in the
prediction, and explicitly increase the penalty on errors in depth-wise
discontinuous regions, which helps preserve the sharpness of estimation
results. Finally, based on analysis of failure cases for prior methods, we
collect a new Hard Case (HC) Depth dataset of challenging scenes, such as
special lighting conditions, dynamic objects, and tilted camera angles. The new
dataset is leveraged by an informed learning curriculum that mixes training
examples incrementally to handle diverse data distributions. Experimental
results show that our method outperforms state-of-the-art approaches by a large
margin in terms of both prediction accuracy on NYUDv2 dataset and
generalization performance on unseen datasets.
