{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pwc-net-cnns-for-optical-flow-using-pyramid","title":"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume","arxiv_id":"1709.02371","date":"2017-09-07","proceeding":"CVPR 2018 6","authors":["Deqing Sun","Xiaodong Yang","Ming-Yu Liu","Jan Kautz"],"abstract":"We present a compact but effective CNN model for optical flow, called\nPWC-Net. PWC-Net has been designed according to simple and well-established\nprinciples: pyramidal processing, warping, and the use of a cost volume. Cast\nin a learnable feature pyramid, PWC-Net uses the cur- rent optical flow\nestimate to warp the CNN features of the second image. It then uses the warped\nfeatures and features of the first image to construct a cost volume, which is\nprocessed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in\nsize and easier to train than the recent FlowNet2 model. Moreover, it\noutperforms all published optical flow methods on the MPI Sintel final pass and\nKITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436)\nimages. 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