- Abstract:
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In this paper, we aim to understand the semantics and 3D structure of a scene from a single depth image. Recent deep neural networks based methods aim to simultaneously learn object class labels and infer the 3D shape of a scene represented by a large voxel grid. However, individual objects within the scene are usually only represented by a few voxels leading to a loss of geometric detail. In addition, significant computational and memory resources are required to process the large scale voxe...
Expand abstract - Publication status:
- Published
- Peer review status:
- Reviewed (other)
- Version:
- Publisher's Version
- Files:
-
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(pdf, 397.0KB)
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- Publisher copy:
- 10.1109/CVPRW.2018.00069
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- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Pages:
- 422-425
- Publication date:
- 2018-12-17
- Acceptance date:
- 2018-04-19
- DOI:
- EISSN:
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2160-7516
- ISSN:
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2160-7508
- Pubs id:
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pubs:909845
- URN:
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uri:bbcdf929-45b7-46f0-8122-996c2da0f9c3
- UUID:
-
uuid:bbcdf929-45b7-46f0-8122-996c2da0f9c3
- Local pid:
- pubs:909845
- ISBN:
- 9781538661000
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2018
- Notes:
- © 2018 IEEE. This paper has been presented at Computer Vision and Pattern Recognition (CVPR) Workshops 2018, 18-22 June 2018, Salt Lake City, USA. This is the Open Access version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/CVPRW.2018.00069
Conference item
Learning 3D scene semantics and structure from a single depth image
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