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

Shay Zweig, Lior Wolf
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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-theart 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
more » ... endencies between neurons and multilayer supervision into our learning process. We also show the importance of the image contour to the learning process. Our method is robust and outperforms EpicFlow on competitive optical flow benchmarks with several underlying matching algorithms. This leads to state-of-the-art performance on the Sintel and KITTI 2012 benchmarks.
doi:10.1109/cvpr.2017.674 dblp:conf/cvpr/ZweigW17 fatcat:pup4gsguqvb2nagebnzing7xua