Unsupervised Depth Estimation is a computational framework within the field of computer vision that enables the prediction of depth information from images without requiring ground-truth depth labels during training by leveraging inherent geometric or photometric consistency across multi-view imagery.
145
Publications
14K
Citations
372
Authors
164
Institutions
Publications per year
2017–2026
140
Parents
Publications
145
Learning Depth from Monocular Videos Using Direct Methods
Chaoyang Wang, José M. Buenaposada, Rui Zhu et al. · 2018 · 686 citations
Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney et al. · 2019 · 525 citations
1–5 of 145
Authors
372
- AA
7
h-index
- ZY
6
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- PW
6
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- SS
6
h-index
- RM
5
h-index
1–5 of 372
Institutions
164
University of Oxford
Oxford, United Kingdom
7 h-index
16 publications
1.1K citations
Tsinghua University
Beijing, China
6 h-index
13 publications
510 citations
Google (United States)
Mountain View, United States
5 h-index
13 publications
1.3K citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
10 publications
2.4K citations
University of California, Los Angeles
Los Angeles, United States
5 h-index
12 publications
1.2K citations
Australian National University
Canberra, Australia
5 h-index
10 publications
1.5K citations
University of Bologna
Bologna, Italy
5 h-index
15 publications
830 citations
Johns Hopkins University
Baltimore, United States
5 h-index
7 publications
954 citations
1–8 of 164
Venues
- arXiv (Cornell University)
Repository · 26 publications · 1.5K citations
17
h-index
- IEEE Transactions on Circuits and Systems for Video Technology
3 publications · 174 citations
5
h-index
- IEEE Transactions on Image Processing
5 publications · 197 citations
5
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 5 publications · 413 citations
5
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- Neurocomputing
4 publications · 69 citations
4
h-index
1–5