FlowNet: Learning Optical Flow with Convolutional Networks
This addresses optical flow estimation for computer vision, introducing a novel CNN-based approach to a task where CNNs had not been successful before.
The paper tackled optical flow estimation by constructing convolutional neural networks (CNNs) as a supervised learning task, achieving competitive accuracy on datasets like Sintel and KITTI at 5-10 fps.
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks where CNNs were successful. In this paper we construct appropriate CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.