- Abstract:
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The objective of this work is to reconstruct the 3D surfaces of sculptures from one or more images using a view-dependent representation. To this end, we train a network, SiDeNet, to predict the Silhouette and Depth of the surface given a variable number of images; the silhouette is predicted at a different viewpoint from the inputs (e.g. from the side), while the depth is predicted at the viewpoint of the input images. This has three benefits. First, the network learns a representation of sh...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 5.5mb)
-
- Publisher copy:
- 10.1007/s11263-018-1124-0
-
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- Publisher:
- Springer Publisher's website
- Journal:
- International Journal of Computer Vision Journal website
- Publication date:
- 2018-10-22
- Acceptance date:
- 2018-10-03
- DOI:
- EISSN:
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1573-1405
- ISSN:
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0920-5691
- Pubs id:
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pubs:944864
- URN:
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uri:066e1725-1271-4177-aea7-ea190027d13d
- UUID:
-
uuid:066e1725-1271-4177-aea7-ea190027d13d
- Local pid:
- pubs:944864
- Copyright holder:
- Wiles and Zisserman
- Copyright date:
- 2018
- Notes:
- © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- License:
- CC BY
Journal article
Learning to predict 3D surfaces of sculptures from single and multiple views
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