Shape Anchors for Data-Driven Multi-view Reconstruction
We present a data-driven method for building dense 3D reconstructions using a combination of recognition and multi-view cues. Our approach is based on the idea that there are image patches that are so distinctive that we can accurately estimate their latent 3D shapes solely using recognition. We cal...
Main Authors: | Xiao, Jianxiong, Torralba, Antonio, Owens, Andrew Hale, Freeman, William T. |
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Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2014
|
Online Access: | http://hdl.handle.net/1721.1/91001 https://orcid.org/0000-0001-9020-9593 https://orcid.org/0000-0002-2231-7995 https://orcid.org/0000-0003-4915-0256 |
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