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Sparse-to-dense interpolation for optical flow is a fundamental phase in thepipeline of most of the leading optical flow estimation algorithms. The currentstate-of-the-art method for interpolation, EpicFlow, is a local average methodbased on an edge aware geodesic distance. We propose a new data-drivensparse-to-dense interpolation algorithm based on a fully convolutional network.We draw inspiration from the filling-in process in the visual cortex andintroduce lateral dependencies between neurons and multi-layer supervision intoour learning process. We also show the importance of the image contour to thelearning process. Our method is robust and outperforms EpicFlow on competitiveoptical flow benchmarks with several underlying matching algorithms. This leadsto state-of-the-art performance on the Sintel and KITTI 2012 benchmarks.

InterpoNet, A brain inspired neural network for optical flow dense interpolation