Papers › PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

7 Sep 2017CVPR 2018 6arXiv:1709.02371archive 2025-07-28

Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436) images. Our models are available on https://github.com/NVlabs/PWC-Net.

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NVlabs/PWC-Net officialmentioned in papermentioned on GitHubpytorchNOASSERTION report
MurrayC7/PWC-Net mentioned on GitHubpytorch report
daigo0927/PWC-Net_tf mentioned on GitHubtf report
daigo0927/pwcnet mentioned on GitHubtf report
fpsandnoob/pwc_net mentioned on GitHubmindspore report
goutamgmb/NTIRE21_BURSTSR mentioned on GitHubpytorch report
kuangzijian/Flow-Based-Video-Matting mentioned on GitHubpytorch report
neu-vi/ezflow mentioned on GitHubpytorchMIT report
neu-vig/ezflow mentioned on GitHubpytorchMIT report
philferriere/tfoptflow mentioned on GitHubtf report
rickyHong/tfoptflow-repl mentioned on GitHubtf report
sniklaus/pytorch-pwc mentioned on GitHubpytorch report
yanqi1811/PWC-Net mentioned on GitHubpytorch report
zyong812/pwc-net_Pytorch mentioned on GitHubpytorch report
open-mmlab/mmflow pytorchApache-2.0 report

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Tasks

Dense Pixel Correspondence EstimationOptical Flow Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dense Pixel Correspondence Estimation HPatches PWC-Net Viewpoint I AEPE 4.43 #3 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches PWC-Net Viewpoint II AEPE 11.44 #3 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches PWC-Net Viewpoint III AEPE 15.47 #3 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches PWC-Net Viewpoint IV AEPE 20.17 #3 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches PWC-Net Viewpoint V AEPE 28.30 #3 of 8 Archive leaderboard report
Optical Flow Estimation KITTI 2015 (train) PWC-Net EPE 10.35 #19 of 19 Archive leaderboard report
Optical Flow Estimation KITTI 2015 (train) PWC-Net F1-all 33.7 #19 of 19 Archive leaderboard report
Optical Flow Estimation Spring PWCNet 1px total 82.265 #11 of 11 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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