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. 2019 May 29;19(11):2459. doi: 10.3390/s19112459

Table 2.

Quantitative optical flow comparison result on the KITTI flow 2012 and KITTI flow 2015 datasets. The Flying Chairs dataset [35] is denoted as C, Flying Things dataset [37] as T, and KITTI raw dataset as K. The learning approach (G) is supervised by the ground truth, and (U) represents an unsupervised method. Non-Oc indicates a non-occluded region.

Method Dataset KITTI Flow 2012 KITTI Flow 2015 Runtime (ms)
EPE (Train) EPE (Test) Non-Oc EPE (Train) AEE (Train) Fl (Train) Non-Oc GPU
FlowNetC [35] C (G) 9.35 - 7.23 12.52 - 47.93% 9.35 51.4
FlowNetS [35] C (G) 8.26 - 6.85 15.44 - 52.86% 8.12 20.2
DSTFlow [38] K (U) 10.43 12.4 - 16.79 14.61 36.00% - -
FlowNet2 [36] C (G) + T (G) 4.09 - 3.42 10.06 9.17 30.37% 4.93 101.6
Ours (w/o flow cons.) K (U) 5.33 4.72 6.21 11.28 10.11 35.42% 5.13 38.1
Ours (w/o synt cons.) K (U) 5.02 4.55 6.69 10.74 9.58 34.17% 5.08 38.2
Ours (w/o FLC) K (U) 4.41 4.38 5.45 10.33 9.37 31.64% 4.96 40.4
Ours K (U) 4.16 4.07 4.52 10.05 9.12 29.78% 4.81 42.8