Unsupervised Learning of Monocular Depth and Ego-Motion with Optical Flow Features and Multiple Constraints Article Swipe
Baigan Zhao
,
Yingping Huang
,
Wenyan Ci
,
Xing Hu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22041383
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22041383
This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models. Unlike the existing unsupervised methods, our method extracts the features from the optical flow rather than from the raw RGB images, thereby enhancing unsupervised learning. In addition, we exploit the forward-backward consistency check of the optical flow to generate a mask of the invalid region in the image, and accordingly, eliminate the outlier regions such as occlusion regions and moving objects for the learning. Furthermore, in addition to using view synthesis as a supervised signal, we impose additional loss functions, including optical flow consistency loss and depth consistency loss, as additional supervision signals on the valid image region to further enhance the training of the models. Substantial experiments on multiple benchmark datasets demonstrate that our method outperforms other unsupervised methods.
Related Topics
Concepts
Optical flow
Artificial intelligence
Computer science
Monocular
Unsupervised learning
Consistency (knowledge bases)
Benchmark (surveying)
Computer vision
Exploit
Outlier
Supervised learning
Pattern recognition (psychology)
Image (mathematics)
Artificial neural network
Geography
Computer security
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22041383
- https://www.mdpi.com/1424-8220/22/4/1383/pdf?version=1644571110
- OA Status
- gold
- Cited By
- 7
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213213810
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213213810Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22041383Digital Object Identifier
- Title
-
Unsupervised Learning of Monocular Depth and Ego-Motion with Optical Flow Features and Multiple ConstraintsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-11Full publication date if available
- Authors
-
Baigan Zhao, Yingping Huang, Wenyan Ci, Xing HuList of authors in order
- Landing page
-
https://doi.org/10.3390/s22041383Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/4/1383/pdf?version=1644571110Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/4/1383/pdf?version=1644571110Direct OA link when available
- Concepts
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Optical flow, Artificial intelligence, Computer science, Monocular, Unsupervised learning, Consistency (knowledge bases), Benchmark (surveying), Computer vision, Exploit, Outlier, Supervised learning, Pattern recognition (psychology), Image (mathematics), Artificial neural network, Geography, Computer security, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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