EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth From Light Field Images. Shin, C., Jeon, H., Yoon, Y., Kweon, I. S., & Kim, S. J. In CVPR, pages 4748-4757, 2018. IEEE Computer Society.
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth From Light Field Images. [link]Link  EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth From Light Field Images. [link]Paper  bibtex   
@inproceedings{conf/cvpr/ShinJYKK18,
  added-at = {2019-01-07T00:00:00.000+0100},
  author = {Shin, Changha and Jeon, Hae-Gon and Yoon, Youngjin and Kweon, In So and Kim, Seon Joo},
  biburl = {https://www.bibsonomy.org/bibtex/2b8aed69cebf2830d9f2cb9b994205023/dblp},
  booktitle = {CVPR},
  crossref = {conf/cvpr/2018},
  ee = {https://doi.org/10.1109/CVPR.2018.00499},
  interhash = {307af7d94c1a9acde8eab27aeb3a3aca},
  intrahash = {b8aed69cebf2830d9f2cb9b994205023},
  keywords = {dblp},
  pages = {4748-4757},
  publisher = {IEEE Computer Society},
  timestamp = {2019-01-29T11:41:03.000+0100},
  title = {EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth From Light Field Images.},
  url = {http://dblp.uni-trier.de/db/conf/cvpr/cvpr2018.html#ShinJYKK18},
  year = 2018
}

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