Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections Article Swipe
Zhenzhang Ye
,
Tarun Yenamandra
,
Florian Bernard
,
Daniel Cremers
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.17229
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.17229
Graph matching aims to establish correspondences between vertices of graphs such that both the node and edge attributes agree. Various learning-based methods were recently proposed for finding correspondences between image key points based on deep graph matching formulations. While these approaches mainly focus on learning node and edge attributes, they completely ignore the 3D geometry of the underlying 3D objects depicted in the 2D images. We fill this gap by proposing a trainable framework that takes advantage of graph neural networks for learning a deformable 3D geometry model from inhomogeneous image collections, i.e.,~a set of images that depict different instances of objects from the same category. Experimentally, we demonstrate that our method outperforms recent learning-based approaches for graph matching considering both accuracy and cycle-consistency error, while we in addition obtain the underlying 3D geometry of the objects depicted in the 2D images.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.17229
- https://arxiv.org/pdf/2103.17229
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307125234
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4307125234Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.17229Digital Object Identifier
- Title
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Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image CollectionsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-31Full publication date if available
- Authors
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Zhenzhang Ye, Tarun Yenamandra, Florian Bernard, Daniel CremersList of authors in order
- Landing page
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https://arxiv.org/abs/2103.17229Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2103.17229Direct link to full text PDF
- Open access
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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/2103.17229Direct OA link when available
- Concepts
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Computer science, Matching (statistics), Consistency (knowledge bases), Graph, Artificial intelligence, Deep learning, Pattern recognition (psychology), Geometry, Computer vision, Mathematics, Theoretical computer science, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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