InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation

@article{Zweig2017InterpoNetAB,
  title={InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation},
  author={Shay Zweig and Lior Wolf},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={6363-6372}
}
Sparse-to-dense interpolation for optical flow is a fundamental phase in the pipeline of most of the leading optical flow estimation algorithms. The current state-of-the-art method for interpolation, EpicFlow, is a local average method based on an edge aware geodesic distance. We propose a new data-driven sparse-to-dense interpolation algorithm based on a fully convolutional network. We draw inspiration from the filling-in process in the visual cortex and introduce lateral dependencies between… CONTINUE READING
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