  Monocular depth estimation, which plays a crucial role in understanding 3D
scene geometry, is an ill-posed problem. Recent methods have gained significant
improvement by exploring image-level information and hierarchical features from
deep convolutional neural networks (DCNNs). These methods model depth
estimation as a regression problem and train the regression networks by
minimizing mean squared error, which suffers from slow convergence and
unsatisfactory local solutions. Besides, existing depth estimation networks
employ repeated spatial pooling operations, resulting in undesirable
low-resolution feature maps. To obtain high-resolution depth maps,
skip-connections or multi-layer deconvolution networks are required, which
complicates network training and consumes much more computations. To eliminate
or at least largely reduce these problems, we introduce a spacing-increasing
discretization (SID) strategy to discretize depth and recast depth network
learning as an ordinal regression problem. By training the network using an
ordinary regression loss, our method achieves much higher accuracy and
\dd{faster convergence in synch}. Furthermore, we adopt a multi-scale network
structure which avoids unnecessary spatial pooling and captures multi-scale
information in parallel.
  The method described in this paper achieves state-of-the-art results on four
challenging benchmarks, i.e., KITTI [17], ScanNet [9], Make3D [50], and NYU
Depth v2 [42], and win the 1st prize in Robust Vision Challenge 2018. Code has
been made available at: https://github.com/hufu6371/DORN.
