Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation

14 Sep 2018Deqing SunXiaodong YangMing-Yu LiuJan Kautz

We investigate two crucial and closely related aspects of CNNs for optical flow estimation: models and training. First, we design a compact but effective CNN model, called PWC-Net, according to simple and well-established principles: pyramidal processing, warping, and cost volume processing... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Optical Flow Estimation KITTI 2012 PWC-Net + ft - axXiv Average End-Point Error 1.5 # 1
Optical Flow Estimation KITTI 2015 PWC-Net + ft - axXiv Fl-all 7.72 # 2
Optical Flow Estimation Sintel-clean PWC-Net Average End-Point Error 3.45 # 2
Optical Flow Estimation Sintel-final PWC-Net Average End-Point Error 4.6 # 4