Deep Ordinal Regression Network for Monocular Depth Estimation
Open accessAbstract
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.
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- Authors
- Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D.
- Affiliations
- The University of Sydney (AU)
- University of Pittsburgh (US)
- Centre National de la Recherche Scientifique (FR)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Volume
- 2018
- Pages
- 2002-2011
- ISSN
- 1063-6919 (ISSN-L)
- PMC
- PMC6607900
- OpenAlex
- W2963488291
- Cited by
- 1,991
- References
- 71
- Citations by year
- 2026: 93 · 2025: 220 · 2024: 294 · 2023: 306 · 2022: 306 · 2021: 347 · 2020: 253 · 2019: 159 · 2018: 13
- FWCI
- 57.28
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Minerva Access (University of Melbourne) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Pfizer
- Nvidia
- Centre National de la Recherche Scientifique (INS2I-JCJC-INVISANA, INS2I)
- Australian Research Council (FL170100117, DP180103424)
- Institut des sciences de l'information et de leurs interactions
- National Heart, Lung, and Blood Institute (R01HL141813)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- monocular depth estimation, ordinal regression, multi-scale network, spatial pooling, high-resolution depth maps, regression loss, convergence rate
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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