Breaking of brightness consistency in optical flow with a lightweight CNN network Article Swipe
Yi-Cheng Lin
,
Shuo Wang
,
Yunlong Jiang
,
Bin Han
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.15655
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.15655
Sparse optical flow is widely used in various computer vision tasks, however assuming brightness consistency limits its performance in High Dynamic Range (HDR) environments. In this work, a lightweight network is used to extract illumination robust convolutional features and corners with strong invariance. Modifying the typical brightness consistency of the optical flow method to the convolutional feature consistency yields the light-robust hybrid optical flow method. The proposed network runs at 190 FPS on a commercial CPU because it uses only four convolutional layers to extract feature maps and score maps simultaneously. Since the shallow network is difficult to train directly, a deep network is designed to compute the reliability map that helps it. An end-to-end unsupervised training mode is used for both networks. To validate the proposed method, we compare corner repeatability and matching performance with origin optical flow under dynamic illumination. In addition, a more accurate visual inertial system is constructed by replacing the optical flow method in VINS-Mono. In a public HDR dataset, it reduces translation errors by 93\%. The code is publicly available at https://github.com/linyicheng1/LET-NET.
Related Topics
Concepts
Optical flow
Computer science
Consistency (knowledge bases)
Feature (linguistics)
Artificial intelligence
Brightness
Convolutional neural network
Computer vision
Reliability (semiconductor)
Pattern recognition (psychology)
Image (mathematics)
Optics
Power (physics)
Philosophy
Linguistics
Quantum mechanics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.15655
- https://arxiv.org/pdf/2310.15655
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387947372
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4387947372Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.15655Digital Object Identifier
- Title
-
Breaking of brightness consistency in optical flow with a lightweight CNN networkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-24Full publication date if available
- Authors
-
Yi-Cheng Lin, Shuo Wang, Yunlong Jiang, Bin HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.15655Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.15655Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.15655Direct OA link when available
- Concepts
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Optical flow, Computer science, Consistency (knowledge bases), Feature (linguistics), Artificial intelligence, Brightness, Convolutional neural network, Computer vision, Reliability (semiconductor), Pattern recognition (psychology), Image (mathematics), Optics, Power (physics), Philosophy, Linguistics, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.directly, | 99 |
| abstract_inverted_index.networks. | 122 |
| abstract_inverted_index.replacing | 153 |
| abstract_inverted_index.VINS-Mono. | 159 |
| abstract_inverted_index.brightness | 13, 46 |
| abstract_inverted_index.commercial | 74 |
| abstract_inverted_index.end-to-end | 114 |
| abstract_inverted_index.consistency | 14, 47, 57 |
| abstract_inverted_index.constructed | 151 |
| abstract_inverted_index.invariance. | 42 |
| abstract_inverted_index.lightweight | 28 |
| abstract_inverted_index.performance | 17, 134 |
| abstract_inverted_index.reliability | 108 |
| abstract_inverted_index.translation | 167 |
| abstract_inverted_index.illumination | 34 |
| abstract_inverted_index.light-robust | 60 |
| abstract_inverted_index.unsupervised | 115 |
| abstract_inverted_index.convolutional | 36, 55, 81 |
| abstract_inverted_index.environments. | 23 |
| abstract_inverted_index.illumination. | 141 |
| abstract_inverted_index.repeatability | 131 |
| abstract_inverted_index.simultaneously. | 90 |
| abstract_inverted_index.https://github.com/linyicheng1/LET-NET. | 177 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |