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Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
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“Garg et al. [90] have recently shown how a deep network for single-view depth estimation can be trained simply by observing a large corpus of stereo pairs, without the need to observe or calculate depth explicitly.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [90]
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Metadata
- Authors
- Garg, R., B.G., V. K., Carneiro, G., & Reid, I.
- Affiliations
- The University of Adelaide (AU)
- Corresponding author
- Garg, Ravi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 740-756
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2300779272
- Cited by
- 1,632
- References
- 40
- Citations by year
- 2026: 48 · 2025: 107 · 2024: 164 · 2023: 173 · 2022: 207 · 2021: 253 · 2020: 227 · 2019: 236 · 2018: 153 · 2017: 57 · 2016: 7
- FWCI
- 52.49
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- arXiv (Cornell University)
- 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
- single-view depth estimation, unsupervised learning, convolutional neural networks, autoencoder, stereoscopic images, camera movement, KITTI dataset
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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