Journal article
Dense 3D object reconstruction from a single depth view
- Abstract:
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In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high r...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, pdf, 6.6MB)
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- Publisher copy:
- 10.1109/TPAMI.2018.2868195
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Authors
Bibliographic Details
- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Journal:
- IEEE Transactions on Pattern Analysis and Machine Intelligence Journal website
- Volume:
- 41
- Issue:
- 12
- Pages:
- 2820-2834
- Publication date:
- 2018-09-03
- Acceptance date:
- 2018-08-22
- DOI:
- EISSN:
-
1939-3539
- Source identifiers:
-
909843
Item Description
- Keywords:
- Pubs id:
-
pubs:909843
- UUID:
-
uuid:6e2957a8-300c-49a7-b08e-99f1a755fd9c
- Local pid:
- pubs:909843
- Deposit date:
- 2018-08-24
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2018
- Notes:
- © 2018 IEEE. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/TPAMI.2018.2868195
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