DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration Article Swipe
Nianjin Ye
,
Chuan Wang
,
Shuaicheng Liu
,
Lanpeng Jia
,
Jue Wang
,
Yongqing Cui
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.05131
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.05131
Image alignment by mesh warps, such as meshflow, is a fundamental task which has been widely applied in various vision applications(e.g., multi-frame HDR/denoising, video stabilization). Traditional mesh warp methods detect and match image features, where the quality of alignment highly depends on the quality of image features. However, the image features are not robust in occurrence of low-texture and low-light scenes. Deep homography methods, on the other hand, are free from such problem by learning deep features for robust performance. However, a homography is limited to plane motions. In this work, we present a deep meshflow motion model, which takes two images as input and output a sparse motion field with motions located at mesh vertexes. The deep meshflow enjoys the merics of meshflow that can describe nonlinear motions while also shares advantage of deep homography that is robust against challenging textureless scenarios. In particular, a new unsupervised network structure is presented with content-adaptive capability. On one hand, the image content that cannot be aligned under mesh representation are rejected by our learned mask, similar to the RANSAC procedure. On the other hand, we learn multiple mesh resolutions, combining to a non-uniform mesh division. Moreover, a comprehensive dataset is presented, covering various scenes for training and testing. The comparison between both traditional mesh warp methods and deep based methods show the effectiveness of our deep meshflow motion model.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1912.05131
- https://arxiv.org/pdf/1912.05131
- OA Status
- green
- Cited By
- 10
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2995743703
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2995743703Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1912.05131Digital Object Identifier
- Title
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DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image RegistrationWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-12-11Full publication date if available
- Authors
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Nianjin Ye, Chuan Wang, Shuaicheng Liu, Lanpeng Jia, Jue Wang, Yongqing CuiList of authors in order
- Landing page
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https://arxiv.org/abs/1912.05131Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1912.05131Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1912.05131Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Homography, Computer vision, RANSAC, Deep learning, Image (mathematics), Mathematics, Projective test, Projective space, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 1, 2024: 3, 2023: 2, 2021: 3, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scenes. | 60 |
| abstract_inverted_index.similar | 174 |
| abstract_inverted_index.various | 18, 201 |
| abstract_inverted_index.However, | 47, 80 |
| abstract_inverted_index.covering | 200 |
| abstract_inverted_index.describe | 126 |
| abstract_inverted_index.features | 50, 76 |
| abstract_inverted_index.learning | 74 |
| abstract_inverted_index.meshflow | 95, 118, 123, 225 |
| abstract_inverted_index.methods, | 63 |
| abstract_inverted_index.motions. | 87 |
| abstract_inverted_index.multiple | 185 |
| abstract_inverted_index.rejected | 169 |
| abstract_inverted_index.testing. | 206 |
| abstract_inverted_index.training | 204 |
| abstract_inverted_index.Moreover, | 194 |
| abstract_inverted_index.advantage | 132 |
| abstract_inverted_index.alignment | 1, 38 |
| abstract_inverted_index.combining | 188 |
| abstract_inverted_index.division. | 193 |
| abstract_inverted_index.features, | 33 |
| abstract_inverted_index.features. | 46 |
| abstract_inverted_index.low-light | 59 |
| abstract_inverted_index.meshflow, | 7 |
| abstract_inverted_index.nonlinear | 127 |
| abstract_inverted_index.presented | 151 |
| abstract_inverted_index.structure | 149 |
| abstract_inverted_index.vertexes. | 115 |
| abstract_inverted_index.comparison | 208 |
| abstract_inverted_index.homography | 62, 82, 135 |
| abstract_inverted_index.occurrence | 55 |
| abstract_inverted_index.presented, | 199 |
| abstract_inverted_index.procedure. | 178 |
| abstract_inverted_index.scenarios. | 142 |
| abstract_inverted_index.Traditional | 25 |
| abstract_inverted_index.capability. | 154 |
| abstract_inverted_index.challenging | 140 |
| abstract_inverted_index.fundamental | 10 |
| abstract_inverted_index.low-texture | 57 |
| abstract_inverted_index.multi-frame | 21 |
| abstract_inverted_index.non-uniform | 191 |
| abstract_inverted_index.particular, | 144 |
| abstract_inverted_index.textureless | 141 |
| abstract_inverted_index.traditional | 211 |
| abstract_inverted_index.performance. | 79 |
| abstract_inverted_index.resolutions, | 187 |
| abstract_inverted_index.unsupervised | 147 |
| abstract_inverted_index.comprehensive | 196 |
| abstract_inverted_index.effectiveness | 221 |
| abstract_inverted_index.HDR/denoising, | 22 |
| abstract_inverted_index.representation | 167 |
| abstract_inverted_index.stabilization). | 24 |
| abstract_inverted_index.content-adaptive | 153 |
| abstract_inverted_index.applications(e.g., | 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |