FlowNet: Learning Optical Flow with Convolutional Networks

Citations Over TimeTop 1% of 2015 papers

Abstract

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 CNNs succeeded at. In this paper we construct 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 large 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.

Related Papers

• → An analysis of binarization ground truthing(2010)44 cited
• → Evaluation of manually created ground truth for multi-view people localization(2013)9 cited
• → Document Segmentation Using Pixel-Accurate Ground Truth(2010)7 cited
• → Tryggo: Old norse for truth: The real truth about ground truth: New insights into the challenges of generating ground truth maps for WSI CAD algorithm evaluation(2012)4 cited
• → Ground-Truth Data Modeling for Gait Analysis based on Kinect V2 Sensor Data(2022)