Deep Ordinal Regression Network for Monocular Depth Estimation

• Published in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. 2018; pp. 2002 - 2011
• Main Authors: Fu, Huan, Gong, Mingming, Wang, Chaohui, Batmanghelich, Kayhan, Tao, Dacheng
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.06.2018
• Subjects: Estimation; Feature extraction; Kernel; Spatial resolution; Three-dimensional displays; Training; Two dimensional displays
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2018.00214

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.