ML Anthology

DeepView: View Synthesis with Learned Gradient Descent

John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, Richard Tucker
CVPR 2019
doi:10.1109/CVPR.2019.00247 /cvpr/2019/flynn2019cvpr-deepview/

Abstract

We present a novel approach to view synthesis using multiplane images (MPIs). Building on recent advances in learned gradient descent, our algorithm generates an MPI from a set of sparse camera viewpoints. The resulting method incorporates occlusion reasoning, improving performance on challenging scene features such as object boundaries, lighting reflections, thin structures, and scenes with high depth complexity. We show that our method achieves high-quality, state-of-the-art results on two datasets: the Kalantari light field dataset, and a new camera array dataset, Spaces, which we make publicly available.

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Cite

Text

Flynn et al. "DeepView: View Synthesis with Learned Gradient Descent." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00247

Markdown

[Flynn et al. "DeepView: View Synthesis with Learned Gradient Descent." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/flynn2019cvpr-deepview/) doi:10.1109/CVPR.2019.00247

BibTeX

@inproceedings{flynn2019cvpr-deepview,
  title     = {{DeepView: View Synthesis with Learned Gradient Descent}},
  author    = {Flynn, John and Broxton, Michael and Debevec, Paul and DuVall, Matthew and Fyffe, Graham and Overbeck, Ryan and Snavely, Noah and Tucker, Richard},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00247},
  url       = {https://mlanthology.org/cvpr/2019/flynn2019cvpr-deepview/}
}