From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks Article Swipe
K L Navaneet
,
Ansu Mathew
,
Shashank Kashyap
,
Wei-Chih Hung
,
Varun Jampani
,
R. Venkatesh Babu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.01939
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.01939
Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. In this work, we propose a deep learning technique for 3D object reconstruction from a single image. Contrary to recent works that either use 3D supervision or multi-view supervision, we use only single view images with no pose information during training as well. This makes our approach more practical requiring only an image collection of an object category and the corresponding silhouettes. We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision. A key novelty of the proposed technique is to impose 3D geometric reasoning into predicted 3D point clouds by rotating them with randomly sampled poses and then enforcing cycle consistency on both 3D reconstructions and poses. In addition, using single-view supervision allows us to do test-time optimization on a given test image. Experiments on the synthetic ShapeNet and real-world Pix3D datasets demonstrate that our approach, despite using less supervision, can achieve competitive performance compared to pose-supervised and multi-view supervised approaches.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2005.01939
- https://arxiv.org/pdf/2005.01939
- OA Status
- green
- Cited By
- 2
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3022178356
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3022178356Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2005.01939Digital Object Identifier
- Title
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From Image Collections to Point Clouds with Self-supervised Shape and Pose NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-05-05Full publication date if available
- Authors
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K L Navaneet, Ansu Mathew, Shashank Kashyap, Wei-Chih Hung, Varun Jampani, R. Venkatesh BabuList of authors in order
- Landing page
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https://arxiv.org/abs/2005.01939Publisher landing page
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https://arxiv.org/pdf/2005.01939Direct 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
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https://arxiv.org/pdf/2005.01939Direct OA link when available
- Concepts
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Point cloud, Artificial intelligence, Computer science, Pose, Novelty, Object (grammar), Computer vision, Consistency (knowledge bases), Image (mathematics), Point (geometry), Key (lock), 3D pose estimation, Mathematics, Geometry, Computer security, Philosophy, TheologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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-
2022: 1, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
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| primary_location.pdf_url | https://arxiv.org/pdf/2005.01939 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
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| primary_location.is_accepted | False |
| primary_location.is_published | False |
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| primary_location.landing_page_url | http://arxiv.org/abs/2005.01939 |
| publication_date | 2020-05-05 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2962885944, https://openalex.org/W2342277278, https://openalex.org/W2902435045, https://openalex.org/W2962988048, https://openalex.org/W2963846414, https://openalex.org/W2963026643, https://openalex.org/W2963527086, https://openalex.org/W2966288165, https://openalex.org/W2963850211, https://openalex.org/W2603429625, https://openalex.org/W2560722161, https://openalex.org/W2980240874, https://openalex.org/W2737780766, https://openalex.org/W2965412140, https://openalex.org/W2964137676, https://openalex.org/W2190691619, https://openalex.org/W2962912205, https://openalex.org/W2963739349, https://openalex.org/W2551540143, https://openalex.org/W2964121028, https://openalex.org/W2963111259, https://openalex.org/W2950289461, https://openalex.org/W2883993491 |
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