解读： FlowNet learning optical flow with convolutional networks

贡献点：

           Construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. 

           Train CNN end-to-end to learn predicting the optical flow field from a pair of images

            Propose and compare two architectures: a genetic architecture and another one including a layer that correlates feature vectors at different image locations.

数据库： KITTI Sintel

 

算法：

          While optical flow estimation needs precise per-pixel localization, it also requires finding correspondences between two input images.

          Not only learning image feature representations, but also learn to match them at different locations in the two images. 

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