Cited by 1,991Advanced Vision and ImagingAdvanced Neural Network ApplicationsRobotics and Sensor-Based Localization

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Deep Ordinal Regression Network for Monocular Depth Estimation

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Fu, Huan, Gong, Mingming, Wang, Chaohui, Batmanghelich, Kayhan, Tao, Dacheng·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

DOI 10.1109/cvpr.2018.00214PMC PMC6607900

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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.

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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)
ORCID: Fu, Huan
Gong, Mingming
Wang, Chaohui
Batmanghelich, Kayhan
Tao, Dacheng
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)
DOI: 10.1109/cvpr.2018.00214
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 › Computer Science › Computer Vision and Pattern Recognition › 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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Deep Ordinal Regression Network for Monocular Depth Estimation

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