  Monocular depth estimation, which plays a key role in understanding 3D scene
geometry, is fundamentally an ill-posed problem. Existing methods based on deep
convolutional neural networks (DCNNs) have examined this problem by learning
convolutional networks to estimate continuous depth maps from monocular images.
However, we find that training a network to predict a high spatial resolution
continuous depth map often suffers from poor local solutions. In this paper, we
hypothesize that achieving a compromise between spatial and depth resolutions
can improve network training. Based on this "compromise principle", we propose
a regression-classification cascaded network (RCCN), which consists of a
regression branch predicting a low spatial resolution continuous depth map and
a classification branch predicting a high spatial resolution discrete depth
map. The two branches form a cascaded structure allowing the classification and
regression branches to benefit from each other. By leveraging large-scale raw
training datasets and some data augmentation strategies, our network achieves
top or state-of-the-art results on the NYU Depth V2, KITTI, and Make3D
benchmarks.
