Abstract
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 multilayer 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 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 proposed deep ordinal regression network (DORN) achieves state-of-the-art
results on three challenging benchmarks, i.e., KITTI [16],
Make3D [49], and NYU Depth v2 [41], and outperforms
existing methods by a large margin