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Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Open accessAbstract
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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“More surprisingly, they show that the welltrained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Zhan et al. 2018
In the citing paper
“One solution to solve both scale problems is using stereo sequences during training (Godard et al. 2019; Li et al. 2017; Zhan et al. 2018), the deep predictions are aligned with real-world scale and scale-consistent because of the constraint introduced by the known stereo baseline.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Zhan et al. 2018
In the citing paper
“Like [1], we use these instead of the reprojected LIDAR scans to compare our method against several existing baseline algorithms, still showing superior performance.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [1]
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Metadata
- 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)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 340-349
- ISSN
- 1063-6919 (ISSN-L)
- 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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