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Dense 3D object reconstruction from a single depth view
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Yang, Bo, Rosa, Stefano, Markham, Andrew, Trigoni, Niki and Wen, Hongkai (2019) Dense 3D object reconstruction from a single depth view. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (12). pp. 2820-2834. doi:10.1109/TPAMI.2018.2868195 ISSN 0162-8828.
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WRAP-dense-3D-object-reconstruction-single-depth-view-Wen-2018.pdf - Accepted Version - Requires a PDF viewer. Download (7Mb) | Preview |
Official URL: https://doi.org/10.1109/TPAMI.2018.2868195
Abstract
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 resolution of 2563 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of 3D encoder-decoder and the conditional adversarial networks framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Three-dimensional modeling -- Software, Geometry | ||||||||
Journal or Publication Title: | IEEE Transactions on Pattern Analysis and Machine Intelligence | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 0162-8828 | ||||||||
Official Date: | 1 December 2019 | ||||||||
Dates: |
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Volume: | 41 | ||||||||
Number: | 12 | ||||||||
Page Range: | pp. 2820-2834 | ||||||||
DOI: | 10.1109/TPAMI.2018.2868195 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 3 September 2018 | ||||||||
Date of first compliant Open Access: | 3 September 2018 | ||||||||
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