  In this paper, we address monocular depth estimation with deep neural
networks. To enable training of deep monocular estimation models with various
sources of datasets, state-of-the-art methods adopt image-level normalization
strategies to generate affine-invariant depth representations. However,
learning with image-level normalization mainly emphasizes the relations of
pixel representations with the global statistic in the images, such as the
structure of the scene, while the fine-grained depth difference may be
overlooked. In this paper, we propose a novel multi-scale depth normalization
method that hierarchically normalizes the depth representations based on
spatial information and depth distributions. Compared with previous
normalization strategies applied only at the holistic image level, the proposed
hierarchical normalization can effectively preserve the fine-grained details
and improve accuracy. We present two strategies that define the hierarchical
normalization contexts in the depth domain and the spatial domain,
respectively. Our extensive experiments show that the proposed normalization
strategy remarkably outperforms previous normalization methods, and we set new
state-of-the-art on five zero-shot transfer benchmark datasets.
