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Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

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Zhan, Huangying, Garg, Ravi, Weerasekera, Chamara Saroj, Li, Kejie, Agarwal, Harsh, Reid, Ian M.·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

DOI 10.1109/cvpr.2018.00043

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Abstract

Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recent approaches to single view depth estimation explore the possibility of learning without full supervision via minimizing photometric error. In this paper, we explore the use of stereo sequences for learning depth and visual odometry. The use of stereo sequences enables the use of both spatial (between left-right pairs) and temporal (forward backward) photometric warp error, and constrains the scene depth and camera motion to be in a common, real-world scale. At test time our framework is able to estimate single view depth and two-view odometry from a monocular sequence. We also show how we can improve on a standard photometric warp loss by considering a warp of deep features. We show through extensive experiments that: (i) jointly training for single view depth and visual odometry improves depth prediction because of the additional constraint imposed on depths and achieves competitive results for visual odometry; (ii) deep feature-based warping loss improves upon simple photometric warp loss for both single view depth estimation and visual odometry. Our method outperforms existing learning based methods on the KITTI driving dataset in both tasks. The source code is available at https://github.com/Huangying-Zhan/Depth-VO-Feat.

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Authors: Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. M.
Affiliations: The University of Adelaide (AU)
Indian Institute of Technology BHU (IN)
ORCID: Zhan, Huangying
Garg, Ravi
Li, Kejie
Agarwal, Harsh
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher: IEEE
Published: 2018-06
Pages: 340-349
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr.2018.00043
OpenAlex: W2962816904
Cited by: 705
References: 49
Citations by year: 2026: 21 · 2025: 42 · 2024: 72 · 2023: 88 · 2022: 85 · 2021: 153 · 2020: 115 · 2019: 115 · 2018: 14
FWCI: 29.45
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
arXiv (Cornell University)
Funders: Australian Centre for Robotic Vision
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
Keywords: monocular depth estimation, visual odometry, unsupervised learning

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Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

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