Optical flow evaluation results Statistics:     Average   SD   R2.5   R5.0   R10.0   A90   A95   A99  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
Average
interpolation
error
avg. Mequon
(Hidden texture)
im0   GT   im1
Schefflera
(Hidden texture)
im0   GT   im1
Urban
(Synthetic)
im0   GT   im1
Teddy
(Stereo)
im0   GT   im1
Backyard
(High-speed camera)
im0   GT   im1
Basketball
(High-speed camera)
im0   GT   im1
Dumptruck
(High-speed camera)
im0   GT   im1
Evergreen
(High-speed camera)
im0   GT   im1
rank all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext
SoftsplatAug [190]2.6 1.98 1 2.91 1 1.06 3 2.55 2 3.38 2 1.14 2 1.87 3 2.69 2 1.06 2 3.88 3 4.65 3 2.70 3 7.24 1 8.90 1 2.98 6 3.90 3 7.06 3 1.97 3 5.24 3 11.4 3 1.38 5 5.22 2 8.02 2 1.50 4
SoftSplat [169]5.3 2.06 2 3.06 3 1.14 9 2.80 5 3.91 6 1.24 3 1.99 5 2.73 3 1.21 6 3.84 2 4.64 2 2.69 2 8.10 18 10.0 18 2.96 2 4.10 5 7.53 5 1.98 6 5.49 5 12.1 5 1.39 6 5.40 3 8.33 3 1.50 4
IFRNet [193]8.0 2.08 3 3.03 2 1.16 12 2.78 4 3.73 4 1.38 47 1.74 1 2.58 1 1.04 1 3.96 4 4.78 4 2.96 10 7.55 5 9.28 5 3.12 22 4.42 9 8.20 9 2.02 11 5.56 7 12.3 6 1.37 2 5.64 8 8.70 8 1.51 6
EAFI [186]8.2 2.10 5 3.19 4 1.08 5 2.54 1 3.23 1 1.13 1 1.77 2 2.79 5 1.08 3 3.82 1 4.51 1 2.64 1 9.04 26 11.3 25 3.01 9 4.82 23 9.09 23 1.97 3 5.89 14 13.1 15 1.37 2 5.77 10 8.91 10 1.51 6
DistillNet [184]10.0 2.11 6 3.29 5 1.15 11 2.71 3 3.64 3 1.28 16 1.96 4 2.73 3 1.14 4 4.05 5 4.96 6 2.81 5 7.81 9 9.66 9 3.06 14 4.79 21 9.03 20 2.01 9 6.04 16 13.4 18 1.43 14 6.05 11 9.33 11 1.56 16
SepConv++ [185]13.0 2.39 23 4.17 25 1.20 24 2.98 8 4.21 9 1.28 16 3.34 24 3.23 8 2.20 88 4.49 12 5.81 17 2.87 7 7.64 7 9.42 7 2.97 3 3.77 2 6.80 2 1.96 1 5.26 4 11.6 4 1.36 1 5.71 9 8.86 9 1.45 1
FGME [158]13.2 2.08 3 3.34 7 0.98 1 3.32 22 4.43 13 1.63 112 2.46 6 3.28 9 1.41 17 4.08 6 4.85 5 3.05 18 7.36 3 9.08 3 3.03 11 4.17 7 7.62 7 2.06 22 4.95 2 10.7 2 1.44 15 5.45 4 8.41 5 1.57 17
BMBC [171]15.0 2.30 15 3.40 9 1.20 24 3.07 9 4.25 10 1.41 59 3.17 20 4.19 31 1.66 39 4.24 8 5.28 8 2.85 6 7.79 8 9.62 8 3.14 24 4.08 4 7.47 4 2.02 11 5.63 8 12.4 8 1.40 8 5.55 6 8.58 6 1.61 26
IDIAL [192]15.9 2.23 8 3.62 12 1.14 9 3.22 13 4.54 21 1.46 76 2.79 9 2.97 6 1.23 7 4.49 12 5.64 13 2.94 9 8.36 20 10.4 20 2.97 3 4.53 12 8.43 12 1.99 7 6.17 18 13.3 17 1.50 24 6.31 17 9.67 15 1.58 21
STAR-Net [164]17.1 2.18 7 3.37 8 1.21 42 3.46 31 4.88 31 1.47 79 3.04 18 3.53 15 1.58 31 4.41 11 5.44 11 2.76 4 7.51 4 9.27 4 2.98 6 4.65 13 8.72 13 1.99 7 6.21 20 13.4 18 1.41 9 6.17 13 9.45 13 1.49 3
EDSC [173]18.8 2.32 19 3.90 17 1.16 12 3.10 10 4.38 12 1.51 88 2.98 15 3.54 16 1.36 15 4.49 12 5.74 14 3.16 31 8.05 17 9.96 17 3.08 16 4.89 24 9.28 24 2.02 11 5.55 6 12.3 6 1.41 9 6.42 22 9.99 23 1.55 15
AdaCoF [165]22.8 2.41 25 4.10 24 1.26 135 3.10 10 4.32 11 1.43 65 3.48 29 3.31 10 1.78 56 4.84 23 5.94 24 2.93 8 8.68 23 10.8 22 3.14 24 4.13 6 7.59 6 1.97 3 5.77 12 12.9 13 1.37 2 5.60 7 8.67 7 1.48 2
DSepConv [162]27.5 2.47 26 4.39 31 1.21 42 3.32 22 4.60 23 1.72 133 3.28 21 3.66 17 1.50 24 5.11 30 6.36 28 3.23 66 7.85 10 9.69 10 3.11 20 4.68 15 8.78 15 2.04 19 5.65 9 12.5 9 1.44 15 6.54 27 10.2 27 1.58 21
GDCN [172]29.6 2.31 17 3.98 21 1.10 7 3.80 87 5.17 48 1.54 93 2.92 13 3.78 22 1.43 19 5.59 82 6.01 26 3.24 70 9.02 25 11.3 25 3.10 18 4.66 14 8.75 14 2.08 23 5.75 11 12.7 10 1.42 12 6.40 21 9.98 22 1.53 10
STSR [170]29.9 2.31 17 3.82 15 1.19 17 2.94 6 3.90 5 1.93 169 2.92 13 3.44 14 1.81 57 4.29 10 5.41 9 3.27 79 9.51 29 11.9 29 3.06 14 5.38 34 10.3 34 2.10 24 6.75 29 15.3 31 1.50 24 6.43 24 9.99 23 1.54 11
ProBoost-Net [191]32.1 2.27 12 3.90 17 1.07 4 3.70 71 5.05 40 1.78 144 2.98 15 3.38 12 1.65 38 4.53 16 5.76 15 3.33 106 8.75 24 10.9 24 3.25 29 5.01 25 9.45 25 2.14 26 6.02 15 13.5 20 1.45 17 6.50 26 10.1 26 1.59 23
MAF-net [163]32.2 2.23 8 3.84 16 1.08 5 3.53 42 4.85 30 1.78 144 2.83 11 3.70 18 1.58 31 4.83 22 5.88 18 3.31 99 9.44 28 11.8 28 3.27 30 5.27 29 10.0 29 2.15 27 6.30 21 14.2 22 1.54 46 6.38 20 9.90 21 1.63 28
CtxSyn [134]32.7 2.24 10 3.72 13 1.04 2 2.96 7 4.16 8 1.35 42 4.32 104 3.42 13 3.18 149 4.21 7 5.46 12 3.00 12 9.59 32 11.9 29 3.46 35 5.22 26 9.76 26 2.22 30 7.02 34 15.4 32 1.58 67 6.66 30 10.2 27 1.69 37
FRUCnet [153]32.9 2.61 33 4.34 28 1.52 186 3.30 19 4.52 18 1.72 133 3.14 19 3.70 18 1.76 53 4.74 20 5.99 25 3.29 84 8.11 19 10.0 18 2.97 3 4.48 10 8.35 11 2.02 11 5.78 13 12.7 10 1.45 17 6.06 12 9.38 12 1.57 17
ADC [161]32.9 2.54 31 4.31 26 1.29 154 3.27 16 4.46 14 1.62 110 3.76 55 3.76 20 1.70 47 5.27 37 6.37 29 3.19 46 8.66 22 10.8 22 3.11 20 4.78 19 9.04 21 2.01 9 5.72 10 12.8 12 1.41 9 6.56 28 10.2 27 1.51 6
CyclicGen [149]33.2 2.26 11 3.32 6 1.42 181 3.19 12 4.01 7 2.21 184 2.76 8 4.05 29 1.62 35 4.97 25 5.92 21 3.79 169 8.00 16 9.84 16 3.13 23 3.36 1 5.65 1 2.17 28 4.55 1 9.68 1 1.42 12 4.48 1 6.84 1 1.52 9
FeFlow [167]34.1 2.28 13 3.73 14 1.18 16 3.50 39 4.78 29 2.09 180 2.82 10 3.13 7 1.66 39 4.75 21 5.78 16 3.72 162 7.62 6 9.40 6 3.04 12 4.74 18 8.88 17 2.03 16 6.07 17 13.1 15 1.59 71 6.78 33 10.5 33 1.65 29
MPRN [151]35.2 2.53 29 4.43 32 1.21 42 3.78 84 4.97 34 1.57 99 3.39 26 5.49 38 1.28 8 5.03 26 6.58 32 3.19 46 9.53 30 11.9 29 3.31 32 5.25 28 9.92 27 2.22 30 6.87 31 15.5 33 1.49 21 6.72 31 10.4 31 1.60 25
TC-GAN [166]35.2 2.34 20 3.96 20 1.25 119 3.26 15 4.51 17 1.81 149 3.49 30 3.80 24 2.20 88 4.65 17 5.90 20 3.44 128 7.87 11 9.73 12 3.00 8 4.78 19 9.00 19 2.03 16 6.34 23 14.2 22 1.50 24 6.28 16 9.73 18 1.54 11
MV_VFI [183]35.7 2.35 21 3.98 21 1.25 119 3.25 14 4.49 15 1.81 149 3.46 28 3.81 25 2.21 92 4.66 19 5.92 21 3.44 128 7.87 11 9.72 11 3.01 9 4.80 22 9.05 22 2.04 19 6.33 22 14.2 22 1.50 24 6.27 15 9.70 17 1.54 11
DAIN [152]35.8 2.38 22 4.05 23 1.26 135 3.28 17 4.53 20 1.79 147 3.32 23 3.77 21 2.05 78 4.65 17 5.88 18 3.41 124 7.88 13 9.74 13 3.04 12 4.73 17 8.90 18 2.04 19 6.36 24 14.3 26 1.51 32 6.25 14 9.68 16 1.54 11
MS-PFT [159]36.3 2.53 29 4.35 29 1.16 12 3.61 57 5.03 36 1.69 126 3.30 22 4.25 33 1.77 55 5.13 31 6.55 31 3.19 46 7.94 15 9.81 15 3.21 27 4.49 11 8.24 10 2.22 30 6.55 26 13.9 21 1.79 131 6.42 22 9.89 20 1.69 37
DAI [168]39.2 2.30 15 3.42 10 1.47 185 3.46 31 4.66 25 1.92 163 2.55 7 3.78 22 1.33 10 4.27 9 5.10 7 4.24 182 9.07 27 11.3 25 3.08 16 5.28 30 10.1 31 2.02 11 6.56 27 14.7 28 1.39 6 6.48 25 10.0 25 1.59 23
MEMC-Net+ [160]43.3 2.39 23 3.92 19 1.28 145 3.36 25 4.52 18 2.07 179 3.37 25 3.86 26 2.20 88 4.84 23 5.93 23 3.72 162 8.55 21 10.6 21 3.14 24 4.70 16 8.81 16 2.03 16 6.40 25 14.2 22 1.58 67 6.37 19 9.87 19 1.57 17
MDP-Flow2 [68]44.1 2.89 37 5.38 39 1.19 17 3.47 33 5.07 43 1.26 5 3.66 44 6.10 72 2.48 115 5.20 33 7.48 43 3.14 28 10.2 36 12.8 37 3.61 60 6.13 59 11.8 54 2.31 62 7.36 39 16.8 37 1.49 21 7.75 54 12.1 53 1.69 37
PMMST [112]44.5 2.90 39 5.43 41 1.20 24 3.50 39 5.05 40 1.27 10 3.56 34 5.46 36 1.82 60 5.38 50 7.92 70 3.41 124 10.2 36 12.8 37 3.60 53 5.76 36 11.0 36 2.26 38 7.39 41 16.9 40 1.53 39 7.57 39 11.8 39 1.72 68
SuperSlomo [130]45.7 2.51 27 4.32 27 1.25 119 3.66 65 5.06 42 1.93 169 2.91 12 4.00 28 1.41 17 5.05 27 6.27 27 3.66 157 9.56 31 11.9 29 3.30 31 5.37 33 10.2 33 2.24 33 6.69 28 15.0 29 1.53 39 6.73 32 10.4 31 1.66 30
TOF-M [150]45.8 2.54 31 4.35 29 1.16 12 3.70 71 5.19 49 1.88 156 3.43 27 3.89 27 1.93 68 5.05 27 6.43 30 3.39 118 9.84 33 12.3 33 3.42 34 5.34 32 10.0 29 2.28 46 6.88 32 15.2 30 1.61 79 7.14 35 11.0 35 1.69 37
OFRI [154]48.4 2.28 13 3.45 11 1.35 174 3.44 28 4.57 22 2.13 182 3.02 17 3.34 11 1.73 50 4.51 15 5.42 10 3.88 170 7.89 14 9.76 14 3.10 18 5.24 27 9.92 27 2.10 24 6.78 30 14.3 26 1.82 136 6.32 18 9.62 14 1.75 110
CoT-AMFlow [174]48.8 2.89 37 5.43 41 1.19 17 3.48 34 5.11 46 1.25 4 3.86 63 6.56 95 2.48 115 5.19 32 7.47 42 3.10 22 10.3 41 12.8 37 3.61 60 6.15 63 11.9 63 2.31 62 7.41 45 17.0 45 1.48 19 7.76 58 12.1 53 1.73 77
FLAVR [188]52.4 3.02 59 4.65 33 1.34 172 3.70 71 4.49 15 1.71 131 3.52 33 4.19 31 1.68 44 8.08 183 9.60 161 3.65 156 7.35 2 9.04 2 2.94 1 4.20 8 7.73 8 1.96 1 6.17 18 13.0 14 1.62 89 5.53 5 8.40 4 1.57 17
SepConv-v1 [125]54.5 2.52 28 4.83 34 1.11 8 3.56 52 5.04 38 1.90 159 4.17 87 4.15 30 2.86 135 5.41 59 6.81 33 3.88 170 10.2 36 12.8 37 3.37 33 5.47 35 10.4 35 2.21 29 6.88 32 15.6 34 1.72 118 6.63 29 10.3 30 1.62 27
DeepFlow [85]55.3 2.98 50 5.67 56 1.22 74 3.88 99 5.78 98 1.52 89 3.62 37 5.93 64 1.34 11 5.39 55 7.20 37 3.17 35 11.0 74 13.9 81 3.63 74 5.91 42 11.3 41 2.29 51 7.14 35 16.3 35 1.49 21 7.80 63 12.2 61 1.70 45
CBF [12]60.8 2.83 34 5.20 35 1.23 95 3.97 112 5.79 100 1.56 95 3.62 37 5.47 37 1.60 34 5.21 34 7.12 34 3.29 84 10.1 34 12.6 34 3.62 67 5.97 45 11.5 45 2.31 62 7.76 77 17.8 76 1.61 79 7.60 42 11.9 42 1.76 124
DeepFlow2 [106]61.0 2.99 53 5.65 53 1.22 74 3.88 99 5.79 100 1.48 81 3.62 37 6.03 66 1.34 11 5.38 50 7.44 41 3.22 59 11.0 74 13.8 74 3.67 82 5.83 37 11.2 37 2.25 37 7.60 57 17.4 58 1.50 24 7.82 64 12.2 61 1.77 135
NN-field [71]62.2 2.98 50 5.70 57 1.20 24 3.31 21 4.73 27 1.26 5 4.69 127 5.91 62 2.03 77 5.99 126 9.13 143 3.57 148 10.3 41 12.8 37 3.60 53 6.24 71 12.0 70 2.31 62 7.39 41 16.9 40 1.54 46 7.69 50 12.0 48 1.72 68
NNF-Local [75]63.2 2.92 42 5.51 48 1.19 17 3.30 19 4.71 26 1.26 5 3.65 42 5.91 62 2.29 100 5.76 101 8.70 123 3.55 146 10.3 41 12.9 44 3.60 53 6.42 94 12.4 92 2.34 78 7.57 53 17.4 58 1.74 120 7.61 43 11.9 42 1.72 68
Aniso. Huber-L1 [22]64.9 2.95 45 5.44 44 1.24 109 4.42 151 6.27 150 1.67 122 3.79 56 5.70 46 1.50 24 5.31 40 7.42 40 3.24 70 11.1 85 14.0 93 3.61 60 5.91 42 11.4 43 2.24 33 7.60 57 17.3 52 1.51 32 7.62 45 11.9 42 1.73 77
IROF-TV [53]65.5 3.07 69 5.91 79 1.23 95 3.71 75 5.47 73 1.40 55 3.70 50 6.27 79 1.58 31 5.25 36 7.60 50 3.17 35 11.0 74 13.9 81 4.47 144 6.37 89 12.4 92 2.30 57 7.79 82 17.9 83 1.50 24 7.63 46 11.9 42 1.66 30
LME [70]65.8 2.95 45 5.59 51 1.19 17 3.68 68 5.50 76 1.38 47 4.06 77 7.00 118 1.71 49 5.38 50 7.92 70 3.18 39 11.2 99 14.1 102 4.51 175 6.29 76 12.2 77 2.31 62 7.33 37 16.8 37 1.51 32 7.83 65 12.3 65 1.70 45
CLG-TV [48]66.4 2.94 43 5.45 45 1.25 119 4.26 137 6.17 135 1.60 104 3.68 48 5.73 48 1.73 50 5.36 46 7.41 39 3.32 103 11.1 85 14.0 93 3.57 40 5.88 41 11.3 41 2.26 38 7.58 54 17.0 45 1.57 64 7.75 54 12.1 53 1.72 68
IROF++ [58]67.4 3.03 60 5.77 65 1.20 24 3.59 56 5.31 61 1.33 36 4.32 104 6.61 97 2.25 95 5.06 29 7.14 35 3.16 31 11.0 74 13.9 81 4.44 140 6.34 83 12.3 86 2.27 43 7.54 52 17.3 52 1.64 95 8.09 90 12.7 91 1.69 37
CombBMOF [111]68.4 3.16 98 5.88 74 1.24 109 3.54 46 5.24 53 1.34 40 4.01 72 6.45 90 2.20 88 5.62 90 8.22 88 3.29 84 10.7 56 13.5 57 3.62 67 6.20 67 11.9 63 2.27 43 7.78 81 17.3 52 1.56 59 7.75 54 12.1 53 1.71 57
NNF-EAC [101]68.7 3.01 56 5.60 52 1.25 119 3.63 60 5.36 66 1.29 21 4.17 87 7.03 120 2.99 139 5.50 69 7.96 72 3.28 81 11.2 99 14.1 102 3.60 53 5.86 40 11.2 37 2.26 38 7.43 46 17.0 45 1.54 46 7.79 62 12.2 61 1.73 77
ALD-Flow [66]70.3 3.28 128 6.45 129 1.24 109 3.81 89 5.73 95 1.41 59 3.62 37 6.28 80 1.35 14 5.58 79 8.39 102 3.04 16 10.8 59 13.5 57 4.15 120 5.96 44 11.4 43 2.29 51 7.34 38 16.8 37 1.51 32 8.25 116 12.9 109 1.70 45
DF-Auto [113]70.4 2.94 43 5.34 37 1.23 95 3.99 115 5.84 105 1.65 116 3.85 61 6.73 100 1.55 30 5.38 50 7.54 45 3.25 73 10.4 46 13.0 46 3.70 86 6.17 66 11.9 63 2.28 46 7.94 93 18.2 95 1.75 125 7.68 48 12.0 48 1.71 57
PH-Flow [99]72.2 3.12 84 6.01 94 1.20 24 3.39 26 4.94 33 1.28 16 3.70 50 6.43 86 2.48 115 5.23 35 7.58 49 3.22 59 10.4 46 13.1 49 3.62 67 6.84 153 13.3 151 2.47 138 7.84 86 18.1 91 1.58 67 7.87 71 12.3 65 1.73 77
WLIF-Flow [91]72.4 2.95 45 5.53 49 1.20 24 3.66 65 5.41 69 1.39 51 4.26 97 7.17 130 2.54 119 5.30 39 7.57 48 3.29 84 10.7 56 13.5 57 3.70 86 6.74 142 13.1 138 2.48 144 7.40 43 16.9 40 1.53 39 7.87 71 12.3 65 1.69 37
Second-order prior [8]73.4 2.91 41 5.39 40 1.24 109 4.26 137 6.21 143 1.56 95 3.82 58 6.34 83 1.62 35 5.39 55 7.68 52 3.04 16 11.1 85 13.9 81 3.59 44 6.14 61 11.9 63 2.31 62 7.61 59 17.4 58 1.63 94 7.90 73 12.4 75 1.78 143
p-harmonic [29]74.6 3.00 54 5.72 59 1.21 42 4.33 142 6.24 148 1.69 126 3.60 35 6.07 70 1.39 16 5.70 93 7.87 65 3.29 84 11.0 74 13.8 74 3.63 74 6.02 48 11.6 48 2.34 78 7.67 64 17.5 63 1.70 113 7.92 77 12.4 75 1.72 68
FMOF [92]75.7 3.16 98 5.92 82 1.23 95 3.48 34 5.07 43 1.28 16 4.59 122 6.82 104 2.78 130 5.71 95 8.42 103 3.40 121 10.4 46 13.0 46 3.67 82 6.49 101 12.6 102 2.28 46 7.64 61 17.5 63 1.48 19 8.06 88 12.6 86 1.67 33
Brox et al. [5]75.9 3.08 72 5.94 84 1.21 42 3.83 93 5.67 87 1.45 72 3.93 66 5.76 51 1.67 42 5.32 41 7.19 36 3.22 59 10.6 53 13.4 55 3.56 38 6.60 123 12.7 109 2.42 123 8.61 139 19.7 142 3.04 185 7.43 37 11.6 37 1.68 35
SIOF [67]77.3 3.06 67 5.74 63 1.24 109 4.40 150 6.40 161 1.63 112 4.17 87 7.43 144 1.93 68 5.40 58 7.75 57 3.44 128 10.1 34 12.6 34 3.58 42 6.10 54 11.8 54 2.29 51 7.52 50 17.2 50 1.53 39 7.96 82 12.5 85 1.73 77
MDP-Flow [26]79.2 2.86 35 5.34 37 1.20 24 3.49 38 5.15 47 1.34 40 4.01 72 5.51 39 2.28 97 5.58 79 7.91 69 3.33 106 11.2 99 14.0 93 4.49 155 6.72 136 13.1 138 2.54 162 7.71 68 17.7 72 1.74 120 7.83 65 12.3 65 1.70 45
HCFN [157]79.6 3.16 98 6.30 113 1.20 24 3.69 70 5.58 80 1.32 32 3.97 70 6.09 71 1.73 50 5.54 72 8.33 95 3.22 59 10.9 66 13.7 67 3.61 60 6.29 76 11.9 63 2.62 176 8.11 107 18.5 106 1.61 79 8.18 100 12.8 100 1.73 77
Local-TV-L1 [65]80.0 3.00 54 5.47 46 1.30 158 4.43 153 6.23 147 1.75 140 3.50 31 5.35 35 1.45 20 5.39 55 7.56 46 3.29 84 11.2 99 14.1 102 3.91 109 6.16 64 11.8 54 2.47 138 7.67 64 17.6 67 1.55 53 7.57 39 11.8 39 1.76 124
OAR-Flow [123]80.5 3.13 89 5.95 86 1.22 74 3.83 93 5.70 90 1.48 81 3.65 42 6.06 67 1.16 5 5.60 85 8.48 108 3.03 13 11.2 99 14.1 102 4.51 175 6.12 57 11.8 54 2.41 120 7.97 96 17.9 83 1.59 71 8.11 94 12.7 91 1.71 57
JOF [136]81.4 3.08 72 5.89 76 1.24 109 3.48 34 5.04 38 1.37 45 3.85 61 5.98 65 2.07 79 5.43 62 7.81 63 3.28 81 11.3 118 14.2 118 4.51 175 6.72 136 13.1 138 2.37 94 7.48 48 17.1 48 1.54 46 8.01 84 12.6 86 1.73 77
SegFlow [156]82.1 3.23 116 6.50 132 1.21 42 3.55 49 5.27 58 1.31 28 4.03 75 5.73 48 1.34 11 6.09 133 9.56 159 3.37 113 11.1 85 14.0 93 4.50 160 6.10 54 11.8 54 2.40 108 7.51 49 17.2 50 1.66 102 8.06 88 12.6 86 1.73 77
PRAFlow_RVC [177]85.9 3.33 138 6.76 147 1.20 24 3.56 52 5.25 54 1.32 32 3.94 67 6.33 82 2.41 110 5.65 92 8.49 109 3.12 26 10.3 41 12.9 44 3.62 67 6.41 91 12.4 92 2.30 57 7.37 40 16.9 40 2.11 163 8.63 160 13.5 159 1.82 174
F-TV-L1 [15]86.0 3.30 130 6.36 122 1.29 154 4.39 149 6.32 156 1.62 110 3.80 57 5.90 61 1.76 53 5.61 87 7.97 74 3.31 99 10.9 66 13.6 62 3.59 44 5.84 38 11.2 37 2.33 74 7.70 66 17.6 67 1.79 131 7.61 43 11.9 42 1.78 143
CPM-Flow [114]86.5 3.17 105 6.31 117 1.21 42 3.54 46 5.26 56 1.31 28 4.22 94 5.88 60 1.45 20 6.11 135 9.48 155 3.31 99 11.1 85 13.9 81 4.50 160 6.28 75 12.1 74 2.32 71 7.66 62 17.6 67 1.74 120 8.18 100 12.8 100 1.76 124
Ad-TV-NDC [36]86.6 3.23 116 5.70 57 1.44 183 4.78 177 6.46 165 1.92 163 3.67 45 5.86 58 1.50 24 5.97 123 8.14 86 3.51 139 10.8 59 13.5 57 3.63 74 6.24 71 12.0 70 2.40 108 7.70 66 17.3 52 1.51 32 7.48 38 11.7 38 1.73 77
UnDAF [187]87.0 3.33 138 6.85 150 1.22 74 3.74 80 5.62 85 1.28 16 4.28 99 7.97 160 2.83 132 6.35 149 10.1 168 3.16 31 10.4 46 13.0 46 3.59 44 6.12 57 11.8 54 2.33 74 7.74 74 17.8 76 1.56 59 7.93 78 12.4 75 1.76 124
VCN_RVC [178]87.5 3.82 173 8.35 176 1.21 42 3.55 49 5.29 60 1.30 26 4.20 91 7.12 128 1.89 65 6.70 159 10.9 174 3.18 39 10.9 66 13.7 67 3.61 60 6.20 67 11.9 63 2.24 33 7.73 72 17.8 76 1.55 53 8.10 91 12.7 91 1.85 178
Modified CLG [34]87.8 2.87 36 5.32 36 1.24 109 4.51 159 6.21 143 1.96 173 4.15 85 6.45 90 2.67 127 5.56 76 7.69 53 3.64 155 10.8 59 13.5 57 3.63 74 6.36 88 12.3 86 2.39 104 7.46 47 17.1 48 1.56 59 7.86 68 12.3 65 1.75 110
2DHMM-SAS [90]88.2 3.10 79 5.91 79 1.21 42 4.10 125 6.05 124 1.46 76 4.38 109 7.10 125 2.07 79 5.38 50 7.78 61 3.22 59 11.3 118 14.3 128 4.42 136 6.33 80 12.2 77 2.26 38 7.95 95 18.2 95 1.64 95 8.19 103 12.8 100 1.70 45
TC/T-Flow [77]88.7 3.21 113 6.24 107 1.22 74 3.90 104 5.86 107 1.43 65 3.69 49 5.83 54 1.50 24 5.88 115 8.93 133 3.15 29 11.1 85 13.9 81 4.50 160 6.23 69 12.0 70 2.26 38 8.61 139 19.0 120 1.93 148 8.16 99 12.8 100 1.70 45
COFM [59]90.1 3.03 60 5.76 64 1.22 74 3.55 49 5.21 51 1.32 32 3.82 58 6.98 116 2.81 131 5.41 59 7.97 74 3.30 94 10.8 59 13.6 62 3.62 67 7.01 168 13.7 166 2.40 108 8.00 101 18.5 106 1.98 151 7.91 74 12.4 75 1.80 163
DMF_ROB [135]90.1 3.15 95 6.13 101 1.22 74 3.96 109 5.87 108 1.56 95 5.24 153 7.74 156 2.62 121 5.73 98 8.32 94 3.19 46 11.0 74 13.8 74 4.50 160 6.07 51 11.7 51 2.37 94 7.66 62 17.5 63 1.50 24 8.10 91 12.7 91 1.73 77
Layers++ [37]90.8 2.96 48 5.56 50 1.22 74 3.29 18 4.64 24 1.26 5 4.07 78 7.24 131 3.08 143 5.48 66 8.10 82 3.25 73 12.0 184 15.2 185 4.62 187 7.29 178 14.3 178 2.44 130 7.63 60 17.5 63 1.54 46 7.84 67 12.3 65 1.70 45
FlowFields [108]91.2 3.15 95 6.30 113 1.21 42 3.57 54 5.34 64 1.32 32 4.73 129 6.89 109 3.23 152 5.85 110 8.96 136 3.08 19 10.8 59 13.6 62 4.19 121 6.57 115 12.8 122 2.36 90 7.72 69 17.8 76 1.67 106 8.20 105 12.9 109 1.74 101
nLayers [57]91.7 3.03 60 5.72 59 1.21 42 3.48 34 5.09 45 1.31 28 5.60 159 7.52 147 4.26 174 5.61 87 8.33 95 3.29 84 11.6 162 14.6 161 4.31 127 6.66 129 12.9 131 2.40 108 7.58 54 17.3 52 1.59 71 7.94 79 12.4 75 1.69 37
LDOF [28]91.8 3.03 60 5.66 55 1.28 145 4.06 120 5.53 78 2.40 188 4.32 104 6.43 86 2.00 73 5.45 65 7.56 46 3.60 153 10.2 36 12.7 36 3.59 44 6.39 90 12.4 92 2.29 51 8.36 123 19.4 131 2.21 168 7.57 39 11.8 39 1.86 180
TV-L1-MCT [64]91.9 3.17 105 6.05 97 1.22 74 3.87 96 5.82 103 1.40 55 4.48 116 7.75 157 2.24 94 5.37 48 7.76 59 3.24 70 11.6 162 14.7 167 4.31 127 6.08 52 11.7 51 2.31 62 8.07 105 18.6 110 2.15 165 7.68 48 12.0 48 1.68 35
ComplOF-FED-GPU [35]92.2 3.23 116 6.40 124 1.22 74 3.73 78 5.62 85 1.44 68 5.23 152 6.06 67 3.23 152 5.53 70 8.25 89 3.29 84 11.1 85 13.9 81 4.21 122 6.11 56 11.8 54 2.32 71 8.16 110 18.5 106 1.61 79 8.29 124 12.9 109 1.71 57
TC-Flow [46]93.2 3.31 132 6.70 143 1.22 74 3.91 106 5.95 111 1.45 72 3.64 41 5.84 55 1.28 8 5.70 93 8.50 111 3.22 59 11.2 99 14.1 102 4.44 140 6.34 83 12.3 86 2.41 120 7.79 82 17.9 83 1.55 53 8.42 140 13.2 142 1.74 101
AGIF+OF [84]94.0 3.12 84 5.95 86 1.20 24 3.64 62 5.39 67 1.40 55 3.96 69 6.44 89 2.28 97 5.48 66 8.03 78 3.25 73 11.4 129 14.3 128 4.49 155 6.91 159 13.5 161 2.37 94 7.85 88 17.9 83 1.54 46 8.44 144 13.2 142 1.73 77
AdaConv-v1 [124]94.1 3.57 161 6.88 153 1.41 179 4.34 145 5.67 87 2.52 190 5.00 142 5.86 58 2.98 137 6.91 165 8.89 131 4.89 186 10.2 36 12.8 37 3.21 27 5.33 31 10.1 31 2.27 43 7.30 36 16.6 36 1.92 147 6.94 34 10.8 34 1.67 33
DPOF [18]94.2 3.34 141 6.82 148 1.29 154 3.40 27 4.93 32 1.29 21 5.00 142 6.36 84 3.40 155 5.86 111 8.94 134 3.51 139 11.0 74 13.8 74 3.59 44 6.56 112 12.7 109 2.28 46 7.99 98 18.2 95 1.55 53 8.24 113 12.9 109 1.70 45
CRTflow [81]94.2 3.09 77 5.91 79 1.27 141 4.35 147 6.31 154 1.68 124 4.15 85 7.26 132 1.84 61 5.33 43 7.51 44 3.38 115 11.0 74 13.8 74 4.48 148 6.09 53 11.7 51 2.30 57 8.55 136 19.8 143 1.55 53 8.19 103 12.8 100 1.72 68
PGM-C [118]95.3 3.17 105 6.29 111 1.21 42 3.58 55 5.32 62 1.33 36 5.01 144 6.14 75 1.90 66 6.14 138 9.63 162 3.23 66 11.2 99 14.1 102 4.50 160 6.14 61 11.8 54 2.34 78 8.20 112 18.9 117 1.59 71 8.46 147 13.3 148 1.73 77
Classic++ [32]96.2 3.05 65 5.85 70 1.24 109 4.08 123 6.08 126 1.52 89 3.74 53 5.58 42 1.53 29 5.72 97 8.12 84 3.21 54 11.4 129 14.3 128 3.74 97 6.68 131 13.0 133 2.42 123 8.35 122 19.2 124 1.62 89 8.21 107 12.9 109 1.73 77
EAI-Flow [147]96.5 3.37 144 6.27 109 1.32 165 3.79 85 5.59 82 1.52 89 4.30 102 7.09 123 2.39 108 5.60 85 8.34 97 2.96 10 11.2 99 14.1 102 4.34 130 6.04 50 11.6 48 2.34 78 7.72 69 17.6 67 3.12 186 7.77 60 12.1 53 1.82 174
RAFT-TF_RVC [179]96.8 3.56 159 7.63 166 1.19 17 3.51 41 5.21 51 1.27 10 3.61 36 6.19 78 1.84 61 5.77 103 8.80 127 3.18 39 10.5 51 13.1 49 3.60 53 7.04 171 13.3 151 2.74 189 7.80 84 17.9 83 1.66 102 8.69 163 13.7 164 1.82 174
Sparse-NonSparse [56]96.9 3.07 69 5.88 74 1.21 42 3.61 57 5.33 63 1.33 36 4.29 101 7.47 145 2.19 87 5.37 48 7.74 55 3.21 54 11.5 144 14.5 149 4.36 131 6.66 129 12.9 131 2.41 120 8.69 146 20.1 150 1.67 106 8.27 121 13.0 123 1.70 45
ProFlow_ROB [142]97.7 3.16 98 6.30 113 1.21 42 3.77 83 5.71 92 1.39 51 4.12 82 5.27 34 1.62 35 6.15 139 9.68 163 3.11 24 11.5 144 14.5 149 4.50 160 5.85 39 11.2 37 2.24 33 8.50 132 19.4 131 1.56 59 8.70 164 13.6 162 1.85 178
ProbFlowFields [126]97.8 3.15 95 6.32 119 1.21 42 3.53 42 5.26 56 1.29 21 5.03 146 7.35 139 3.73 162 5.43 62 7.97 74 3.25 73 11.1 85 14.0 93 4.50 160 6.48 99 12.6 102 2.55 165 7.99 98 18.4 105 2.57 177 7.78 61 12.2 61 1.75 110
OFLAF [78]98.2 3.10 79 5.98 91 1.20 24 3.44 28 5.03 36 1.26 5 3.73 52 5.82 53 1.66 39 5.33 43 7.74 55 3.10 22 11.6 162 14.7 167 4.50 160 6.58 120 12.8 122 2.48 144 9.33 172 21.6 173 2.06 159 8.45 146 13.2 142 1.80 163
S2F-IF [121]98.5 3.26 123 6.66 140 1.20 24 3.53 42 5.25 54 1.29 21 4.11 81 6.64 98 2.34 102 5.89 116 9.06 141 3.08 19 11.4 129 14.3 128 4.51 175 6.41 91 12.4 92 2.40 108 7.84 86 18.1 91 1.76 128 8.33 131 13.1 133 1.75 110
Sparse Occlusion [54]98.9 3.16 98 6.18 105 1.23 95 4.14 132 6.24 148 1.45 72 3.67 45 5.84 55 1.52 28 5.61 87 8.26 90 3.15 29 11.5 144 14.4 139 4.48 148 6.26 73 12.1 74 2.46 135 8.52 134 19.6 140 1.54 46 8.28 123 13.0 123 1.75 110
MLDP_OF [87]99.0 3.08 72 5.98 91 1.21 42 4.01 116 6.01 120 1.49 84 3.67 45 6.14 75 1.47 22 5.78 104 8.13 85 3.95 174 11.3 118 14.2 118 3.87 105 6.71 134 13.0 133 2.51 154 7.73 72 17.7 72 1.71 115 8.18 100 12.8 100 1.76 124
PMF [73]99.3 3.14 91 6.13 101 1.20 24 3.73 78 5.60 83 1.27 10 5.24 153 8.98 176 3.76 163 5.75 99 8.56 117 3.28 81 10.8 59 13.6 62 3.62 67 6.55 108 12.7 109 2.35 87 8.41 129 19.5 137 1.64 95 8.57 155 13.4 154 1.70 45
HAST [107]99.5 3.01 56 5.73 61 1.21 42 3.45 30 5.01 35 1.27 10 6.39 173 8.24 166 4.09 168 5.43 62 7.96 72 3.03 13 11.2 99 14.2 118 3.59 44 7.47 182 14.7 182 2.47 138 8.68 145 20.1 150 1.53 39 8.35 134 13.1 133 1.77 135
TF+OM [98]99.9 3.33 138 6.83 149 1.25 119 3.65 63 5.43 71 1.47 79 3.82 58 6.43 86 1.68 44 6.01 128 9.04 140 3.19 46 11.2 99 14.1 102 4.38 133 6.46 98 12.5 98 2.34 78 8.30 121 19.2 124 1.86 140 8.05 87 12.6 86 1.75 110
FlowFields+ [128]100.0 3.14 91 6.26 108 1.22 74 3.54 46 5.27 58 1.30 26 4.74 132 7.10 125 3.20 150 6.01 128 9.35 151 3.11 24 11.1 85 13.9 81 4.50 160 6.57 115 12.8 122 2.40 108 7.89 92 18.2 95 1.80 133 8.22 109 12.9 109 1.73 77
BlockOverlap [61]100.4 2.98 50 5.47 46 1.33 168 4.38 148 6.09 127 1.88 156 4.26 97 5.57 41 3.14 146 5.56 76 7.32 38 4.14 179 11.1 85 13.9 81 3.77 100 6.41 91 12.3 86 2.54 162 7.75 75 17.4 58 3.02 184 7.32 36 11.4 36 1.78 143
FlowNetS+ft+v [110]101.2 3.07 69 5.81 67 1.28 145 4.57 166 6.29 152 2.41 189 4.01 72 5.64 44 2.13 84 5.55 74 7.77 60 3.88 170 11.3 118 14.2 118 4.46 143 5.99 47 11.5 45 2.35 87 8.63 142 20.0 147 1.62 89 7.70 51 12.0 48 1.74 101
C-RAFT_RVC [181]101.5 4.01 177 8.57 181 1.25 119 3.81 89 5.74 96 1.44 68 4.30 102 7.14 129 2.34 102 5.90 117 8.92 132 3.37 113 10.5 51 13.1 49 3.59 44 6.55 108 12.7 109 2.31 62 7.76 77 17.8 76 1.66 102 8.15 98 12.8 100 1.77 135
Filter Flow [19]102.5 3.13 89 5.90 77 1.28 145 4.56 165 6.38 160 1.85 154 4.22 94 6.28 80 2.10 82 5.91 118 7.97 74 3.44 128 10.4 46 13.1 49 3.69 85 6.43 96 12.5 98 2.40 108 8.17 111 18.8 115 1.62 89 7.94 79 12.4 75 1.78 143
LSM [39]104.5 3.12 84 6.05 97 1.21 42 3.68 68 5.47 73 1.33 36 4.38 109 7.66 154 2.01 74 5.55 74 8.19 87 3.19 46 11.5 144 14.5 149 4.43 137 6.83 150 13.3 151 2.37 94 8.70 147 20.1 150 1.72 118 8.34 133 13.1 133 1.71 57
EpicFlow [100]104.8 3.17 105 6.34 120 1.21 42 3.79 85 5.70 90 1.44 68 4.28 99 5.73 48 1.67 42 6.37 152 10.1 168 3.39 118 11.2 99 14.1 102 4.50 160 6.23 69 12.0 70 2.38 101 8.11 107 18.5 106 1.76 128 8.76 168 13.8 168 1.74 101
Black & Anandan [4]105.8 3.22 115 5.87 72 1.30 158 4.82 180 6.55 170 1.78 144 7.16 177 7.10 125 3.93 165 6.25 145 8.49 109 3.35 111 10.9 66 13.7 67 3.56 38 6.33 80 12.2 77 2.37 94 8.23 115 18.6 110 1.64 95 7.67 47 11.9 42 1.69 37
RNLOD-Flow [119]106.2 3.06 67 5.87 72 1.21 42 3.96 109 5.97 117 1.42 62 4.39 111 8.08 163 2.44 112 5.35 45 7.75 57 3.18 39 11.5 144 14.5 149 4.49 155 6.71 134 13.1 138 2.43 126 7.85 88 18.0 89 2.18 166 8.44 144 13.2 142 1.73 77
TCOF [69]106.6 3.12 84 5.94 84 1.21 42 4.60 169 6.64 176 1.76 142 4.13 83 7.30 134 1.81 57 5.42 61 7.88 66 3.25 73 11.3 118 14.2 118 3.63 74 6.42 94 12.4 92 2.36 90 9.08 169 21.0 169 1.59 71 8.37 136 13.1 133 1.76 124
Ramp [62]107.0 3.11 83 5.96 88 1.22 74 3.61 57 5.34 64 1.40 55 4.91 138 8.45 169 3.20 150 5.29 38 7.66 51 3.21 54 11.5 144 14.5 149 4.31 127 6.88 158 13.4 156 2.48 144 8.73 154 20.2 154 1.52 38 8.29 124 13.0 123 1.73 77
Fusion [6]108.1 3.04 64 5.86 71 1.22 74 3.75 82 5.47 73 1.42 62 4.08 79 5.55 40 3.08 143 5.80 106 8.10 82 3.19 46 11.4 129 14.3 128 3.73 94 6.99 165 13.7 166 2.60 171 8.40 128 19.4 131 1.65 100 8.50 149 13.3 148 1.80 163
ComponentFusion [94]108.1 3.41 146 7.08 156 1.20 24 3.63 60 5.44 72 1.27 10 4.20 91 6.49 92 2.43 111 5.59 82 8.38 101 3.32 103 11.4 129 14.4 139 4.11 118 6.26 73 12.1 74 2.35 87 9.30 171 21.6 173 2.80 182 8.68 161 13.6 162 1.73 77
Classic+NL [31]108.8 3.10 79 5.92 82 1.23 95 3.66 65 5.40 68 1.39 51 4.78 134 8.42 168 3.01 141 5.36 46 7.78 61 3.30 94 11.5 144 14.5 149 4.24 124 6.73 138 13.1 138 2.40 108 8.74 155 20.2 154 1.70 113 8.29 124 13.0 123 1.71 57
AggregFlow [95]108.9 3.80 172 8.08 172 1.23 95 3.87 96 5.83 104 1.43 65 4.21 93 6.79 102 2.85 134 6.11 135 9.36 152 3.31 99 10.6 53 13.3 53 3.67 82 6.13 59 11.8 54 2.34 78 8.70 147 19.8 143 2.30 172 8.27 121 13.0 123 1.75 110
Bartels [41]110.5 3.48 153 7.24 160 1.30 158 4.02 117 6.12 132 1.68 124 3.74 53 5.80 52 1.95 71 5.87 113 8.44 105 3.78 168 10.3 41 12.8 37 3.75 99 6.77 145 13.0 133 2.73 188 7.53 51 17.3 52 2.72 180 8.13 95 12.7 91 1.77 135
Occlusion-TV-L1 [63]111.0 3.14 91 6.13 101 1.25 119 4.47 157 6.61 173 1.66 119 3.51 32 5.71 47 1.70 47 6.33 147 9.58 160 3.51 139 11.0 74 13.9 81 3.57 40 6.48 99 12.6 102 2.52 159 8.36 123 18.1 91 2.00 154 8.32 130 13.0 123 1.79 157
HBM-GC [103]111.1 3.08 72 5.90 77 1.26 135 3.97 112 6.04 123 1.41 59 3.92 65 5.62 43 2.87 136 5.54 72 8.03 78 3.21 54 11.7 171 14.7 167 4.58 186 7.66 186 15.0 186 2.69 185 8.36 123 19.3 127 1.55 53 7.86 68 12.3 65 1.76 124
Classic+CPF [82]111.3 3.12 84 5.96 88 1.21 42 3.72 77 5.51 77 1.39 51 4.39 111 7.38 142 2.27 96 5.32 41 7.70 54 3.18 39 11.7 171 14.8 173 4.50 160 7.18 176 14.0 176 2.45 132 8.79 156 20.2 154 1.57 64 8.71 166 13.7 164 1.73 77
LFNet_ROB [145]111.5 3.65 166 7.73 167 1.23 95 3.88 99 5.80 102 1.49 84 4.57 119 8.96 175 2.02 76 5.62 90 8.44 105 3.18 39 11.0 74 13.8 74 4.47 144 7.19 177 14.1 177 2.47 138 7.59 56 17.4 58 1.82 136 8.10 91 12.7 91 1.78 143
FC-2Layers-FF [74]112.0 3.18 109 6.16 104 1.22 74 3.33 24 4.73 27 1.35 42 4.34 108 7.09 123 3.11 145 5.56 76 8.29 91 3.29 84 11.5 144 14.5 149 4.48 148 7.00 166 13.7 166 2.48 144 8.92 161 20.6 162 1.71 115 8.30 127 13.0 123 1.73 77
Efficient-NL [60]112.2 3.05 65 5.77 65 1.21 42 3.90 104 5.84 105 1.38 47 5.90 165 6.94 113 4.19 171 5.59 82 8.09 81 3.20 53 11.5 144 14.4 139 4.40 135 6.87 154 13.4 156 2.40 108 8.85 157 20.5 159 1.68 110 8.57 155 13.4 154 1.66 30
CNN-flow-warp+ref [115]112.5 2.90 39 5.43 41 1.25 119 4.10 125 5.95 111 1.83 153 4.92 139 7.63 152 2.45 113 6.13 137 7.85 64 3.72 162 11.3 118 14.2 118 4.51 175 6.03 49 11.6 48 2.46 135 9.00 162 20.8 167 1.65 100 7.91 74 12.4 75 1.76 124
FESL [72]113.0 3.16 98 6.02 95 1.21 42 3.65 63 5.42 70 1.35 42 4.39 111 7.61 151 2.18 86 5.71 95 8.35 99 3.30 94 11.6 162 14.7 167 4.51 175 6.73 138 13.1 138 2.47 138 8.70 147 20.1 150 1.56 59 8.42 140 13.2 142 1.75 110
Horn & Schunck [3]113.1 3.16 98 5.83 68 1.26 135 4.91 181 6.65 177 1.92 163 6.13 168 6.85 105 3.53 158 6.80 162 9.10 142 3.57 148 10.9 66 13.7 67 3.59 44 6.16 64 11.9 63 2.32 71 8.63 142 19.5 137 1.84 139 7.91 74 12.3 65 1.73 77
2D-CLG [1]113.5 3.01 56 5.65 53 1.28 145 4.59 168 6.17 135 1.95 172 5.18 150 6.06 67 3.15 148 6.01 128 7.88 66 3.97 175 11.4 129 14.4 139 4.69 188 5.98 46 11.5 45 2.45 132 8.89 160 20.5 159 1.67 106 7.74 53 12.0 48 1.71 57
SRR-TVOF-NL [89]113.5 3.32 136 6.46 130 1.23 95 3.96 109 5.96 114 1.59 100 4.68 125 7.90 159 3.52 157 5.99 126 8.77 125 3.23 66 11.2 99 14.1 102 4.45 142 6.79 148 13.2 148 2.31 62 7.88 90 18.0 89 1.50 24 8.37 136 13.1 133 1.75 110
RFlow [88]115.0 3.08 72 5.99 93 1.23 95 4.33 142 6.31 154 1.66 119 4.83 135 7.32 135 3.14 146 5.87 113 8.72 124 3.47 134 11.1 85 14.0 93 3.60 53 6.54 107 12.7 109 2.39 104 8.54 135 19.8 143 1.61 79 8.26 118 12.9 109 1.80 163
TriFlow [93]115.6 3.71 169 7.95 171 1.25 119 4.31 141 6.36 159 1.71 131 4.05 76 6.86 107 1.84 61 6.21 144 9.44 154 3.17 35 11.3 118 14.2 118 4.48 148 6.76 144 13.1 138 2.29 51 8.01 103 18.2 95 1.75 125 8.24 113 12.9 109 1.70 45
OFH [38]115.9 3.18 109 6.29 111 1.23 95 4.11 127 5.96 114 1.61 107 4.68 125 8.40 167 1.68 44 5.84 108 8.99 137 3.03 13 11.3 118 14.2 118 4.25 126 6.30 78 12.2 77 2.40 108 8.59 137 19.3 127 1.89 143 8.55 152 13.4 154 1.97 186
PWC-Net_RVC [143]116.2 3.66 168 7.76 168 1.21 42 3.91 106 5.97 117 1.37 45 3.88 64 6.73 100 1.48 23 6.36 150 10.1 168 3.13 27 11.8 177 14.9 178 4.49 155 6.78 147 13.1 138 2.34 78 7.72 69 17.7 72 1.66 102 8.57 155 13.5 159 1.88 181
3DFlow [133]116.6 3.26 123 6.37 123 1.21 42 3.70 71 5.55 79 1.46 76 4.51 117 6.52 93 2.28 97 5.84 108 8.84 129 3.59 151 11.2 99 14.1 102 3.79 102 7.04 171 13.7 166 2.68 184 8.59 137 19.4 131 1.82 136 8.26 118 12.9 109 1.77 135
CostFilter [40]117.8 3.46 152 7.24 160 1.19 17 3.71 75 5.60 83 1.27 10 5.63 161 9.41 183 3.86 164 6.37 152 10.1 168 3.23 66 11.2 99 14.0 93 3.78 101 6.35 86 12.2 77 2.40 108 8.86 159 20.6 162 1.69 111 8.80 170 13.8 168 1.74 101
SVFilterOh [109]117.8 3.23 116 6.35 121 1.23 95 3.53 42 5.19 49 1.31 28 5.91 166 8.20 165 4.22 172 5.75 99 8.52 113 3.43 126 11.4 129 14.3 128 4.53 182 6.97 163 13.6 164 2.38 101 7.94 93 18.3 102 1.57 64 8.31 129 13.0 123 1.79 157
Nguyen [33]118.0 3.26 123 6.11 100 1.33 168 4.94 182 6.51 168 1.91 162 4.09 80 7.32 135 1.96 72 6.19 143 8.53 114 3.60 153 11.1 85 13.9 81 3.58 42 6.55 108 12.7 109 2.36 90 9.44 174 21.8 177 1.80 133 7.86 68 12.3 65 1.74 101
S2D-Matching [83]118.1 3.21 113 6.22 106 1.22 74 3.97 112 5.95 111 1.48 81 4.57 119 7.70 155 2.84 133 5.48 66 8.06 80 3.48 136 11.4 129 14.3 128 4.14 119 6.97 163 13.6 164 2.56 169 8.09 106 18.6 110 1.74 120 8.21 107 12.9 109 1.76 124
Adaptive [20]119.5 3.24 120 6.44 127 1.25 119 4.57 166 6.61 173 1.72 133 3.94 67 6.12 74 1.81 57 5.86 111 8.66 122 3.47 134 11.6 162 14.6 161 3.59 44 6.55 108 12.7 109 2.51 154 9.03 165 20.6 162 1.59 71 8.13 95 12.7 91 1.78 143
FlowNet2 [120]120.2 4.84 186 10.1 187 1.29 154 4.11 127 6.13 133 1.61 107 4.73 129 7.06 121 2.36 105 6.36 150 10.0 166 3.38 115 11.2 99 14.1 102 3.71 89 6.44 97 12.5 98 2.33 74 8.45 130 19.4 131 1.61 79 8.03 86 12.6 86 1.77 135
Steered-L1 [116]120.4 2.97 49 5.73 61 1.21 42 3.81 89 5.72 94 1.60 104 8.15 180 9.24 180 6.46 188 6.42 154 9.21 149 4.28 183 11.4 129 14.3 128 3.80 103 6.52 105 12.7 109 2.43 126 8.20 112 19.0 120 2.54 175 8.33 131 13.1 133 1.70 45
IAOF [50]120.5 3.53 158 6.60 137 1.32 165 5.39 190 7.19 190 1.96 173 5.81 163 7.32 135 3.63 160 6.15 139 8.34 97 3.72 162 11.1 85 14.0 93 3.60 53 6.50 102 12.6 102 2.34 78 8.28 120 19.0 120 1.53 39 7.94 79 12.4 75 1.73 77
PBOFVI [189]120.5 3.31 132 6.60 137 1.21 42 4.34 145 6.42 163 1.69 126 4.98 141 8.07 162 2.35 104 5.76 101 8.63 120 3.45 132 11.5 144 14.5 149 4.50 160 6.34 83 12.2 77 2.30 57 8.39 127 18.8 115 1.80 133 8.26 118 13.0 123 1.74 101
Complementary OF [21]121.3 3.48 153 7.32 164 1.20 24 3.89 102 5.96 114 1.45 72 8.94 184 6.94 113 5.45 182 6.33 147 10.0 166 3.09 21 11.3 118 14.2 118 4.24 124 6.33 80 12.3 86 2.42 123 8.62 141 19.3 127 1.75 125 9.07 179 14.3 180 1.72 68
CompactFlow_ROB [155]121.3 3.91 176 8.50 180 1.24 109 3.94 108 5.94 110 1.54 93 5.28 155 8.58 171 2.62 121 8.69 186 14.5 188 3.26 78 10.9 66 13.7 67 3.64 81 6.87 154 13.4 156 2.33 74 8.50 132 19.6 140 1.53 39 8.22 109 12.9 109 1.75 110
FF++_ROB [141]123.0 3.27 126 6.67 141 1.20 24 3.74 80 5.58 80 1.38 47 4.86 137 7.42 143 2.99 139 6.57 158 10.4 172 3.54 144 11.5 144 14.5 149 4.51 175 6.62 126 12.8 122 2.47 138 7.97 96 18.3 102 1.90 144 8.24 113 12.9 109 1.78 143
TVL1_RVC [175]123.1 3.32 136 6.27 109 1.36 176 5.03 184 6.77 184 1.94 171 4.84 136 6.87 108 2.98 137 6.16 142 8.51 112 3.58 150 10.9 66 13.7 67 3.63 74 6.57 115 12.7 109 2.43 126 9.00 162 20.6 162 2.20 167 7.72 52 12.1 53 1.71 57
AugFNG_ROB [139]123.3 3.73 170 7.90 170 1.25 119 4.12 130 6.02 121 1.74 137 4.70 128 8.79 173 1.94 70 8.14 184 13.4 185 3.29 84 12.0 184 15.1 183 4.50 160 6.50 102 12.6 102 2.28 46 8.03 104 18.3 102 1.62 89 7.75 54 12.1 53 1.75 110
TV-L1-improved [17]123.8 3.09 77 6.03 96 1.25 119 4.55 164 6.59 172 1.70 129 5.88 164 5.66 45 4.09 168 5.53 70 7.88 66 3.22 59 11.4 129 14.4 139 3.61 60 6.73 138 13.1 138 2.51 154 9.48 175 22.1 179 1.94 149 8.25 116 12.9 109 1.79 157
TI-DOFE [24]124.7 3.41 146 6.44 127 1.44 183 5.20 188 6.82 187 2.01 177 4.19 90 6.41 85 1.88 64 6.98 166 9.50 156 3.70 160 10.8 59 13.6 62 3.61 60 6.59 121 12.8 122 2.36 90 8.13 109 18.2 95 1.77 130 8.53 151 12.4 75 2.33 190
EPPM w/o HM [86]125.0 3.35 142 6.86 152 1.21 42 3.85 95 5.88 109 1.29 21 7.03 175 9.47 186 3.97 167 6.15 139 9.51 157 3.38 115 10.6 53 13.3 53 3.62 67 7.00 166 13.7 166 2.37 94 8.85 157 20.5 159 2.62 179 8.42 140 13.2 142 1.76 124
GraphCuts [14]126.9 3.65 166 7.01 155 1.27 141 3.89 102 5.71 92 1.59 100 7.54 178 5.84 55 4.31 175 5.98 125 8.42 103 3.45 132 11.4 129 14.4 139 4.09 116 6.56 112 12.8 122 2.30 57 8.70 147 20.2 154 1.98 151 8.59 159 13.5 159 1.73 77
BriefMatch [122]127.7 3.25 122 6.49 131 1.25 119 3.87 96 5.67 87 1.97 175 6.16 169 6.17 77 4.79 178 6.83 164 8.37 100 5.73 189 11.0 74 13.8 74 3.73 94 6.75 143 13.0 133 2.61 173 7.99 98 17.9 83 3.29 187 8.22 109 12.8 100 2.32 189
LSM_FLOW_RVC [182]128.0 4.28 183 9.20 183 1.31 162 4.09 124 6.17 135 1.50 87 5.13 148 9.21 179 2.39 108 7.80 178 13.2 184 3.16 31 11.2 99 14.1 102 4.47 144 6.31 79 12.2 77 2.39 104 8.26 116 19.0 120 1.58 67 8.52 150 13.3 148 1.80 163
NL-TV-NCC [25]128.6 3.37 144 6.58 136 1.24 109 4.23 136 6.41 162 1.49 84 4.39 111 6.68 99 2.07 79 7.19 174 11.2 178 3.35 111 10.7 56 13.4 55 4.00 113 6.95 160 13.4 156 2.44 130 9.06 166 20.0 147 2.13 164 8.42 140 13.1 133 1.78 143
IAOF2 [51]129.8 3.43 149 6.70 143 1.28 145 4.62 171 6.77 184 1.74 137 4.41 115 6.89 109 2.12 83 5.97 123 8.53 114 3.33 106 11.6 162 14.7 167 4.06 115 6.87 154 13.4 156 2.51 154 8.26 116 18.7 114 1.61 79 8.22 109 12.9 109 1.74 101
EPMNet [131]130.0 4.90 187 10.5 191 1.28 145 4.04 119 5.98 119 1.60 104 4.73 129 7.06 121 2.36 105 8.74 188 15.0 189 3.48 136 11.2 99 14.1 102 3.71 89 6.70 133 13.0 133 2.34 78 8.45 130 19.4 131 1.61 79 8.38 138 13.1 133 1.78 143
TriangleFlow [30]131.5 3.24 120 6.31 117 1.26 135 4.29 140 6.29 152 1.66 119 4.67 124 6.85 105 2.48 115 5.78 104 8.47 107 3.30 94 11.4 129 14.4 139 3.47 36 6.63 128 12.8 122 2.37 94 9.67 179 22.5 180 2.08 161 9.69 187 15.2 187 1.90 183
ResPWCR_ROB [140]131.9 3.52 157 7.36 165 1.23 95 4.06 120 6.18 140 1.53 92 4.57 119 6.90 111 1.91 67 7.44 177 12.2 182 3.40 121 11.5 144 14.6 161 4.39 134 7.10 174 13.7 166 2.54 162 7.81 85 17.8 76 1.67 106 9.04 178 14.2 177 1.71 57
LocallyOriented [52]132.5 3.29 129 6.53 134 1.26 135 4.64 172 6.69 179 1.74 137 5.61 160 7.56 148 3.67 161 6.73 160 9.84 165 3.18 39 11.5 144 14.4 139 3.71 89 6.57 115 12.7 109 2.45 132 8.71 151 19.3 127 1.71 115 8.40 139 13.1 133 1.72 68
Correlation Flow [76]133.7 3.27 126 6.50 132 1.20 24 4.42 151 6.56 171 1.65 116 3.98 71 6.10 72 2.30 101 5.93 120 8.94 134 3.32 103 11.6 162 14.6 161 3.84 104 7.63 185 14.8 183 2.65 182 9.95 183 23.0 183 2.01 156 8.73 167 13.7 164 1.71 57
ContinualFlow_ROB [148]134.0 3.79 171 8.09 173 1.25 119 4.03 118 6.11 130 1.61 107 4.76 133 7.58 149 2.38 107 7.09 169 11.7 179 3.17 35 12.2 188 15.4 188 4.49 155 6.35 86 12.3 86 2.29 51 8.71 151 20.0 147 1.61 79 9.02 176 14.2 177 1.78 143
ACK-Prior [27]135.0 3.30 130 6.56 135 1.21 42 3.81 89 5.78 98 1.42 62 7.13 176 6.90 111 5.04 179 6.02 131 8.78 126 3.70 160 11.7 171 14.7 167 4.57 185 6.95 160 13.5 161 2.50 150 8.36 123 19.2 124 2.53 174 8.56 154 13.4 154 1.73 77
ROF-ND [105]135.3 3.18 109 5.83 68 1.21 42 4.13 131 6.13 133 1.92 163 4.22 94 7.51 146 2.22 93 7.10 170 10.8 173 3.53 142 11.4 129 14.3 128 4.48 148 6.95 160 13.5 161 2.53 160 8.21 114 18.6 110 1.90 144 9.08 180 14.2 177 1.81 172
HBpMotionGpu [43]136.2 3.63 164 7.28 162 1.35 174 4.78 177 6.69 179 1.92 163 4.33 107 7.01 119 2.56 120 6.46 155 9.81 164 3.40 121 11.5 144 14.4 139 5.69 193 6.83 150 13.3 151 2.55 165 7.40 43 16.9 40 1.51 32 8.30 127 13.0 123 1.79 157
StereoOF-V1MT [117]136.5 3.56 159 7.20 159 1.22 74 4.27 139 6.18 140 1.70 129 6.10 167 6.80 103 3.43 156 7.17 173 9.52 158 4.01 178 11.2 99 14.1 102 4.43 137 6.61 125 12.5 98 2.60 171 9.49 176 21.6 173 2.05 157 8.01 84 12.4 75 1.78 143
H+S_RVC [176]137.0 3.43 149 6.69 142 1.28 145 4.50 158 6.02 121 1.90 159 5.12 147 7.34 138 2.66 125 7.02 168 8.60 119 3.54 144 11.5 144 14.5 149 3.88 106 6.62 126 12.8 122 2.43 126 8.64 144 19.5 137 2.06 159 8.36 135 12.8 100 1.76 124
Dynamic MRF [7]141.2 3.19 112 6.41 125 1.22 74 4.11 127 6.21 143 1.56 95 5.37 157 7.35 139 2.70 128 6.74 161 9.18 147 4.19 180 11.1 85 13.9 81 4.48 148 7.02 169 13.7 166 2.62 176 9.26 170 21.4 172 2.23 169 8.57 155 13.3 148 1.80 163
LiteFlowNet [138]141.9 3.86 174 8.34 175 1.22 74 3.80 87 5.75 97 1.44 68 5.33 156 9.45 185 2.66 125 8.72 187 14.4 187 3.88 170 11.8 177 14.8 173 4.50 160 7.03 170 13.7 166 2.40 108 9.07 168 20.4 158 1.69 111 8.13 95 12.7 91 1.78 143
FOLKI [16]142.3 3.64 165 7.12 157 1.65 188 5.22 189 6.72 182 2.36 187 5.20 151 8.08 163 3.96 166 7.93 180 9.33 150 5.52 188 11.2 99 14.0 93 3.70 86 6.56 112 12.6 102 2.74 189 8.00 101 18.2 95 2.88 183 7.96 82 12.3 65 1.78 143
Shiralkar [42]142.7 3.57 161 7.31 163 1.22 74 4.46 156 6.33 158 1.65 116 5.49 158 6.98 116 2.73 129 7.42 176 10.9 174 3.43 126 11.5 144 14.4 139 3.73 94 6.57 115 12.7 109 2.48 144 9.58 177 21.9 178 1.88 142 9.18 183 14.4 182 1.75 110
SimpleFlow [49]143.0 3.10 79 5.97 90 1.22 74 4.19 134 6.11 130 1.64 115 9.91 187 9.43 184 6.53 189 5.58 79 8.29 91 3.30 94 11.6 162 14.6 161 4.43 137 7.42 180 14.6 181 2.56 169 10.7 187 25.2 187 2.73 181 9.16 182 14.4 182 1.73 77
Rannacher [23]143.5 3.31 132 6.72 146 1.25 119 4.60 169 6.66 178 1.72 133 6.36 172 6.54 94 4.25 173 5.91 118 8.87 130 3.49 138 11.5 144 14.5 149 3.63 74 6.73 138 13.1 138 2.53 160 9.35 173 21.7 176 1.98 151 8.70 164 13.7 164 1.75 110
SILK [80]143.8 3.45 151 6.85 150 1.36 176 5.11 186 6.70 181 2.21 184 11.1 189 9.96 187 6.24 187 6.49 156 8.82 128 3.59 151 11.4 129 14.3 128 3.54 37 6.87 154 13.3 151 2.63 178 7.76 77 17.7 72 1.87 141 8.20 105 12.7 91 1.80 163
Learning Flow [11]144.0 3.14 91 6.09 99 1.27 141 4.51 159 6.53 169 1.67 122 11.5 192 12.9 192 7.17 192 6.31 146 8.30 93 3.66 157 11.7 171 14.8 173 3.89 107 6.59 121 12.8 122 2.48 144 8.27 119 18.9 117 1.96 150 8.68 161 13.4 154 1.80 163
OFRF [132]147.3 4.02 178 8.26 174 1.33 168 4.53 162 6.49 166 1.81 149 4.60 123 7.27 133 2.13 84 6.02 131 9.15 144 3.39 118 11.8 177 14.9 178 4.23 123 7.13 175 13.9 174 2.39 104 9.02 164 20.8 167 1.59 71 8.79 169 13.8 168 1.77 135
Adaptive flow [45]147.7 3.60 163 6.30 113 1.54 187 5.14 187 6.79 186 2.14 183 4.52 118 6.60 96 3.01 141 6.54 157 8.64 121 4.23 181 12.1 187 15.2 185 4.09 116 7.57 183 14.9 185 2.64 180 7.75 75 17.8 76 2.28 171 8.47 148 13.3 148 1.71 57
UnFlow [127]148.9 4.05 179 8.73 182 1.31 162 4.44 155 6.28 151 1.87 155 4.92 139 7.36 141 2.62 121 5.95 122 9.00 138 3.27 79 12.0 184 15.2 185 4.37 132 7.59 184 14.8 183 2.61 173 7.77 80 17.6 67 1.64 95 10.4 188 15.4 188 2.33 190
StereoFlow [44]149.1 5.35 192 10.3 189 1.42 181 5.03 184 7.21 191 1.76 142 4.14 84 6.94 113 2.01 74 5.83 107 8.55 116 3.33 106 13.7 190 17.3 190 4.70 189 8.71 191 17.2 191 2.70 186 7.88 90 18.1 91 1.61 79 8.82 172 13.9 173 1.79 157
IRR-PWC_RVC [180]149.5 4.57 184 10.0 186 1.28 145 4.06 120 6.17 135 1.63 112 5.02 145 8.82 174 2.47 114 9.64 190 16.3 190 3.21 54 11.7 171 14.8 173 4.56 184 7.08 173 13.9 174 2.38 101 8.71 151 19.9 146 1.59 71 9.08 180 14.3 180 1.77 135
2bit-BM-tele [96]150.5 3.31 132 6.41 125 1.34 172 4.53 162 6.62 175 1.80 148 6.23 170 9.24 180 6.19 186 5.94 121 8.59 118 3.55 146 11.3 118 14.2 118 4.03 114 7.72 187 15.1 187 3.02 191 12.2 191 28.7 192 4.77 193 7.76 58 12.1 53 1.82 174
IIOF-NLDP [129]151.7 3.36 143 6.62 139 1.21 42 4.22 135 6.32 156 1.59 100 5.16 149 7.63 152 2.63 124 6.10 134 9.20 148 3.53 142 11.6 162 14.6 161 4.79 191 7.42 180 14.5 180 2.71 187 12.0 190 28.2 190 3.38 188 8.93 174 13.9 173 1.74 101
SPSA-learn [13]155.8 3.89 175 7.79 169 1.27 141 4.43 153 6.17 135 1.81 149 9.03 185 8.47 170 5.47 183 6.80 162 9.40 153 3.72 162 11.5 144 14.5 149 3.91 109 6.51 104 12.6 102 2.46 135 11.9 188 27.9 189 4.54 191 10.5 190 16.5 190 1.75 110
FFV1MT [104]157.1 4.09 181 8.38 177 1.31 162 4.68 174 6.18 140 2.02 178 6.95 174 11.5 189 3.35 154 7.12 171 9.16 145 3.98 176 11.3 118 14.1 102 3.74 97 6.77 145 12.7 109 2.50 150 9.59 178 21.0 169 2.05 157 8.87 173 13.8 168 1.90 183
SegOF [10]158.8 3.51 155 7.12 157 1.32 165 4.17 133 6.10 128 1.59 100 8.69 182 7.75 157 5.15 180 8.58 185 14.3 186 4.29 184 11.7 171 14.8 173 4.50 160 6.79 148 13.2 148 2.50 150 10.1 184 23.5 184 2.55 176 8.80 170 13.8 168 1.72 68
PGAM+LK [55]162.1 4.08 180 8.41 178 1.65 188 4.74 176 6.45 164 2.27 186 8.87 183 12.2 190 6.88 190 8.06 182 10.9 174 4.83 185 11.4 129 14.3 128 3.90 108 6.83 150 13.2 148 2.55 165 8.26 116 18.9 117 2.27 170 8.55 152 13.3 148 1.90 183
Heeger++ [102]162.2 4.76 185 9.63 185 1.33 168 4.65 173 6.22 146 1.90 159 7.84 179 9.26 182 3.57 159 7.12 171 9.16 145 3.98 176 11.9 181 15.0 181 4.47 144 6.52 105 12.2 77 2.61 173 9.82 180 20.6 162 2.00 154 9.02 176 14.0 175 1.79 157
SLK [47]163.3 3.51 155 6.96 154 1.41 179 4.72 175 6.10 128 1.98 176 9.84 186 7.59 150 5.20 181 7.98 181 11.0 177 6.14 190 11.8 177 14.9 178 3.71 89 6.60 123 12.7 109 2.50 150 9.87 182 22.8 182 2.08 161 8.94 175 14.0 175 2.03 187
WRT [146]165.4 3.42 148 6.71 145 1.23 95 4.33 142 6.06 125 1.89 158 9.93 188 8.00 161 5.95 185 6.98 166 9.01 139 3.77 167 11.9 181 15.1 183 3.97 112 7.82 188 15.4 188 2.64 180 12.5 192 29.5 193 3.47 189 10.5 190 16.6 191 1.80 163
HCIC-L [97]166.2 4.98 189 9.28 184 1.77 191 4.97 183 6.87 189 2.11 181 5.70 162 10.0 188 4.41 177 7.85 179 11.8 180 3.68 159 10.9 66 13.7 67 3.72 93 8.18 190 16.1 190 2.55 165 9.06 166 21.0 169 2.58 178 9.57 186 15.0 186 1.81 172
WOLF_ROB [144]168.3 5.06 190 10.3 189 1.30 158 4.79 179 6.72 182 1.75 140 6.29 171 9.03 177 4.14 170 7.37 175 11.8 180 3.33 106 11.9 181 15.0 181 4.48 148 7.40 179 14.3 178 2.51 154 10.5 186 23.9 185 1.74 120 9.44 184 14.8 184 1.78 143
Pyramid LK [2]176.7 4.16 182 8.44 179 1.74 190 5.83 191 6.82 187 2.76 191 11.4 190 8.60 172 5.89 184 12.4 192 16.7 191 7.03 192 14.3 191 18.1 191 3.92 111 6.69 132 12.2 77 2.63 178 10.3 185 24.0 186 2.45 173 11.1 192 17.4 192 2.55 192
GroupFlow [9]179.8 4.94 188 10.2 188 1.36 176 4.51 159 6.50 167 1.92 163 8.67 181 9.13 178 4.38 176 8.83 189 13.0 183 5.40 187 12.9 189 16.3 189 4.53 182 7.89 189 15.5 189 2.65 182 9.85 181 22.6 181 1.91 146 9.52 185 14.9 185 1.88 181
Periodicity [79]190.7 5.27 191 11.1 192 1.83 192 7.09 192 7.33 192 2.86 192 11.4 190 12.2 190 7.13 191 10.5 191 17.1 192 6.14 190 14.9 192 19.0 192 4.71 190 9.13 192 17.9 192 3.16 192 11.9 188 27.8 188 3.76 190 10.4 188 15.8 189 2.29 188
AVG_FLOW_ROB [137]192.8 14.6 193 20.0 193 3.66 193 11.3 193 12.1 193 4.33 193 13.4 193 14.1 193 7.93 193 19.0 193 25.3 193 10.2 193 18.3 193 23.1 193 5.58 192 16.7 193 32.2 193 4.90 193 16.6 193 28.6 191 4.56 192 15.9 193 19.8 193 4.61 193
Move the mouse over the numbers in the table to see the corresponding images. Click to compare with the ground truth.

References

Methodtime*framescolor Reference and notes
[1] 2D-CLG 844 2 gray The 2D-CLG method by Bruhn et al. as implemented by Stefan Roth. [A. Bruhn, J. Weickert, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. IJCV 63(3), 2005.] Parameters were set to match the published performance on Yosemite sequence, which may not be optimal for other sequences.
[2] Pyramid LK 12 2 color A modification of Bouguet's pyramidal implementation of Lucas-Kanade.
[3] Horn & Schunck 49 2 gray A modern Matlab implementation of the Horn & Schunck method by Deqing Sun. Parameters set to optimize AAE on all training data.
[4] Black & Anandan 328 2 gray A modern Matlab implementation of the Black & Anandan method by Deqing Sun.
[5] Brox et al. 18 2 color T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004. (Improved using separate robust functions as proposed in A. Bruhn and J. Weickert, Towards ultimate motion estimation, ICCV 2005; improved by training on the training set.)
[6] Fusion 2,666 2 color V. Lempitsky, S. Roth, and C. Rother. Discrete-continuous optimization for optical flow estimation. CVPR 2008.
[7] Dynamic MRF 366 2 gray B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic MRFs. CVPR 2008. (Method improved since publication.)
[8] Second-order prior 14 2 gray W. Trobin, T. Pock, D. Cremers, and H. Bischof. An unbiased second-order prior for high-accuracy motion estimation. DAGM 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[9] GroupFlow 600 2 gray X. Ren. Local Grouping for Optical Flow. CVPR 2008.
[10] SegOF 60 2 color L. Xu, J. Chen, and J. Jia. Segmentation based variational model for accurate optical flow estimation. ECCV 2008. Code available.
[11] Learning Flow 825 2 gray D. Sun, S. Roth, J.P. Lewis, and M. Black. Learning optical flow (SRF-LFC). ECCV 2008.
[12] CBF 69 2 color W. Trobin, T. Pock, D. Cremers, and H. Bischof. Continuous energy minimization via repeated binary fusion. ECCV 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[13] SPSA-learn 200 2 color Y. Li and D. Huttenlocher. Learning for optical flow using stochastic optimization. ECCV 2008.
[14] GraphCuts 1,200 2 color T. Cooke. Two applications of graph-cuts to image processing. DICTA 2008.
[15] F-TV-L1 8 2 gray A. Wedel, T. Pock, J. Braun, U. Franke, and D. Cremers. Duality TV-L1 flow with fundamental matrix prior. IVCNZ 2008.
[16] FOLKI 1.4 2 gray G. Le Besnerais and F. Champagnat. Dense optical flow by iterative local window registration. ICIP 2005.
[17] TV-L1-improved 2.9 2 gray A. Wedel, T. Pock, C. Zach, H. Bischof, and D. Cremers. An improved algorithm for TV-L1 optical flow computation. Proceedings of the Dagstuhl Visual Motion Analysis Workshop 2008. Code at GPU4Vision.
[18] DPOF 287 2 color C. Lei and Y.-H. Yang. Optical flow estimation on coarse-to-fine region-trees using discrete optimization. ICCV 2009. (Method improved since publication.)
[19] Filter Flow 34,000 2 color S. Seitz and S. Baker. Filter flow. ICCV 2009.
[20] Adaptive 9.2 2 gray A. Wedel, D. Cremers, T. Pock, and H. Bischof. Structure- and motion-adaptive regularization for high accuracy optic flow. ICCV 2009.
[21] Complementary OF 44 2 color H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[22] Aniso. Huber-L1 2 2 gray M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009. Code at GPU4Vision.
[23] Rannacher 0.12 2 gray J. Rannacher. Realtime 3D motion estimation on graphics hardware. Bachelor thesis, Heidelberg University, 2009.
[24] TI-DOFE 260 2 gray C. Cassisa, S. Simoens, and V. Prinet. Two-frame optical flow formulation in an unwarped multiresolution scheme. CIARP 2009.
[25] NL-TV-NCC 20 2 color M. Werlberger, T. Pock, and H. Bischof. Motion estimation with non-local total variation regularization. CVPR 2010.
[26] MDP-Flow 188 2 color L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. CVPR 2010.
[27] ACK-Prior 5872 2 color K. Lee, D. Kwon, I. Yun, and S. Lee. Optical flow estimation with adaptive convolution kernel prior on discrete framework. CVPR 2010.
[28] LDOF 122 2 color T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI 33(3):500-513, 2011.
[29] p-harmonic 565 2 gray J. Gai and R. Stevenson. Optical flow estimation with p-harmonic regularization. ICIP 2010.
[30] TriangleFlow 4200 2 gray B. Glocker, H. Heibel, N. Navab, P. Kohli, and C. Rother. TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. ECCV 2010.
[31] Classic+NL 972 2 color D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR 2010. Matlab code.
[32] Classic++ 486 2 gray A modern implementation of the classical formulation descended from Horn & Schunck and Black & Anandan; see D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, CVPR 2010.
[33] Nguyen 33 2 gray D. Nguyen. Tuning optical flow estimation with image-driven functions. ICRA 2011.
[34] Modified CLG 133 2 gray R. Fezzani, F. Champagnat, and G. Le Besnerais. Combined local global method for optic flow computation. EUSIPCO 2010.
[35] ComplOF-FED-GPU 0.97 2 color P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, and J. Weickert. A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. CVGPU Workshop 2010.
[36] Ad-TV-NDC 35 2 gray M. Nawaz. Motion estimation with adaptive regularization and neighborhood dependent constraint. DICTA 2010.
[37] Layers++ 18206 2 color D. Sun, E. Sudderth, and M. Black. Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010.
[38] OFH 620 3 color H. Zimmer, A. Bruhn, J. Weickert. Optic flow in harmony. IJCV 93(3) 2011.
[39] LSM 1615 2 color K. Jia, X. Wang, and X. Tang. Optical flow estimation using learned sparse model. ICCV 2011.
[40] CostFilter 55 2 color C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. CVPR 2011.
[41] Bartels 0.15 2 gray C. Bartels and G. de Haan. Smoothness constraints in recursive search motion estimation for picture rate conversion. IEEE TCSVT 2010. Version improved since publication: mapped on GPU.
[42] Shiralkar 600 2 gray M. Shiralkar and R. Schalkoff. A self organization-based optical flow estimator with GPU implementation. MVA 23(6):1229-1242.
[43] HBpMotionGpu 1000 5 gray S. Grauer-Gray and C. Kambhamettu. Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. WACV 2009. (Method improved since publication.)
[44] StereoFlow 7200 2 color G. Rosman, S. Shem-Tov, D. Bitton, T. Nir, G. Adiv, R. Kimmel, A. Feuer, and A. Bruckstein. Over-parameterized optical flow using a stereoscopic constraint. SSVM 2011:761-772.
[45] Adaptive flow 121 2 gray Tarik Arici and Vural Aksakalli. Energy minimization based motion estimation using adaptive smoothness priors. VISAPP 2012.
[46] TC-Flow 2500 5 color S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer. Modeling temporal coherence for optical flow. ICCV 2011.
[47] SLK 300 2 gray T. Corpetti and E. Mémin. Stochastic uncertainty models for the luminance consistency assumption. IEEE TIP 2011.
[48] CLG-TV 29 2 gray M. Drulea. Total variation regularization of local-global optical flow. ITSC 2011. Matlab code.
[49] SimpleFlow 1.7 2 color M. Tao, J. Bai, P. Kohli, S. Paris. SimpleFlow: a non-iterative, sublinear optical flow algorithm. EUROGRAPHICS 2012.
[50] IAOF 57 2 gray D. Nguyen. Improving motion estimation using image-driven functions and hybrid scheme. PSIVT 2011.
[51] IAOF2 56 2 gray Duc Dung Nguyen and Jae Wook Jeon. Enhancing accuracy and sharpness of motion field with adaptive scheme and occlusion-aware filter. IET Image Processing 7.2 (2013): 144-153.
[52] LocallyOriented 9541 2 gray Y.Niu, A. Dick, and M. Brooks. Locally oriented optical flow computation. To appear in TIP 2012.
[53] IROF-TV 261 2 color H. Rashwan, D. Puig, and M. Garcia. On improving the robustness of differential optical flow. ICCV 2011 Artemis workshop.
[54] Sparse Occlusion 2312 2 color Alper Ayvaci, Michalis Raptis, and Stefano Soatto. Sparse occlusion detection with optical flow. IJCV 97(3):322-338, 2012.
[55] PGAM+LK 0.37 2 gray A. Alba, E. Arce-Santana, and M. Rivera. Optical flow estimation with prior models obtained from phase correlation. ISVC 2010.
[56] Sparse-NonSparse 713 2 color Zhuoyuan Chen, Jiang Wang, and Ying Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. CVPR 2012.
[57] nLayers 36150 4 color D. Sun, E. Sudderth, and M. Black. Layered segmentation and optical flow estimation over time. CVPR 2012.
[58] IROF++ 187 2 color H. Rashwan, D. Puig, and M. Garcia. Variational optical flow estimation based on stick tensor voting. IEEE TIP 2013.
[59] COFM 600 3 color M. Mozerov. Constrained optical flow estimation as a matching problem. IEEE TIP 2013.
[60] Efficient-NL 400 2 color P. Krähenbühl and V. Koltun. Efficient nonlocal regularization for optical flow. ECCV 2012.
[61] BlockOverlap 2 2 gray Michael Santoro, Ghassan AlRegib, and Yucel Altunbasak. Motion estimation using block overlap minimization. MMSP 2012.
[62] Ramp 1200 2 color A. Singh and N. Ahuja. Exploiting ramp structures for improving optical flow estimation. ICPR 2012.
[63] Occlusion-TV-L1 538 3 gray C. Ballester, L. Garrido, V. Lazcano, and V. Caselles. A TV-L1 optical flow method with occlusion detection. DAGM-OAGM 2012.
[64] TV-L1-MCT 90 2 color M. Mohamed and B. Mertsching. TV-L1 optical flow estimation with image details recovering based on modified census transform. ISVC 2012.
[65] Local-TV-L1 500 2 gray L. Raket. Local smoothness for global optical flow. ICIP 2012.
[66] ALD-Flow 61 2 color M. Stoll, A. Bruhn, and S. Volz. Adaptive integration of feature matches into variational optic flow methods. ACCV 2012.
[67] SIOF 234 2 color L. Xu, Z. Dai, and J. Jia. Scale invariant optical flow. ECCV 2012.
[68] MDP-Flow2 342 2 color L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012. Code available.
[69] TCOF 1421 all gray J. Sanchez, A. Salgado, and N. Monzon. Optical flow estimation with consistent spatio-temporal coherence models. VISAPP 2013.
[70] LME 476 2 color W. Li, D. Cosker, M. Brown, and R. Tang. Optical flow estimation using Laplacian mesh energy. CVPR 2013.
[71] NN-field 362 2 color L. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013.
[72] FESL 3310 2 color Weisheng Dong, Guangming Shi, Xiaocheng Hu, and Yi Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. IEEE TIP 23(10):4527-4538, 2014.
[73] PMF 35 2 color J. Lu, H. Yang, D. Min, and M. Do. PatchMatch filter: efficient edge-aware filtering meets randomized search for fast correspondence field estimation. CVPR 2013.
[74] FC-2Layers-FF 2662 4 color D. Sun, J. Wulff, E. Sudderth, H. Pfister, and M. Black. A fully-connected layered model of foreground and background flow. CVPR 2013.
[75] NNF-Local 673 2 color Zhuoyuan Chen, Hailin Jin, Zhe Lin, Scott Cohen, and Ying Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013.
[76] Correlation Flow 290 2 color M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code.
[77] TC/T-Flow 341 5 color M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013.
[78] OFLAF 1530 2 color T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013.
[79] Periodicity 8000 4 color Georgii Khachaturov, Silvia Gonzalez-Brambila, and Jesus Gonzalez-Trejo. Periodicity-based computation of optical flow. Computacion y Sistemas (CyS) 2014.
[80] SILK 572 2 gray Pascal Zille, Thomas Corpetti, Liang Shao, and Xu Chen. Observation model based on scale interactions for optical flow estimation. IEEE TIP 23(8):3281-3293, 2014.
[81] CRTflow 13 3 color O. Demetz, D. Hafner, and J. Weickert. The complete rank transform: a tool for accurate and morphologically invariant matching of structures. BMVC 2013.
[82] Classic+CPF 640 2 gray Zhigang Tu, Nico van der Aa, Coert Van Gemeren, and Remco Veltkamp. A combined post-filtering method to improve accuracy of variational optical flow estimation. Pattern Recognition 47(5):1926-1940, 2014.
[83] S2D-Matching 1200 2 color Marius Leordeanu, Andrei Zanfir, and Cristian Sminchisescu. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013.
[84] AGIF+OF 438 2 gray Zhigang Tu, Ronald Poppe, and Remco Veltkamp. Adaptive guided image filter for warping in variational optical flow computation. Signal Processing 127:253-265, 2016.
[85] DeepFlow 13 2 color P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[86] EPPM w/o HM 2.5 2 color L. Bao, Q. Yang, and H. Jin. Fast edge-preserving PatchMatch for large displacement optical flow. CVPR 2014.
[87] MLDP_OF 165 2 gray M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia, and D. Puig. Illumination-robust optical flow approach using local directional pattern. IEEE TCSVT 24(9):1499-1508, 2014.
[88] RFlow 20 2 gray S. Ali, C. Daul, and W. Blondel. Robust and accurate optical flow estimation for weak texture and varying illumination condition: Application to cystoscopy. IPTA 2014.
[89] SRR-TVOF-NL 32 all color P. Pohl, M. Sirotenko, E. Tolstaya, and V. Bucha. Edge preserving motion estimation with occlusions correction for assisted 2D to 3D conversion. IS&T/SPIE Electronic Imaging 2014.
[90] 2DHMM-SAS 157 2 color M.-C. Shih, R. Shenoy, and K. Rose. A two-dimensional hidden Markov model with spatially-adaptive states with application of optical flow. ICIP 2014 submission.
[91] WLIF-Flow 700 2 color Z. Tu, R. Veltkamp, N. van der Aa, and C. Van Gemeren. Weighted local intensity fusion method for variational optical flow estimation. Submitted to TIP 2014.
[92] FMOF 215 2 color N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014.
[93] TriFlow 150 2 color TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914.
[94] ComponentFusion 6.5 2 color Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941.
[95] AggregFlow 1642 2 color D. Fortun, P. Bouthemy, and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. Preprint arXiv:1407.5759.
[96] 2bit-BM-tele 124 2 gray R. Xu and D. Taubman. Robust dense block-based motion estimation using a two-bit transform on a Laplacian pyramid. ICIP 2013.
[97] HCIC-L 330 2 color Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114.
[98] TF+OM 600 2 color R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015.
[99] PH-Flow 800 2 color J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015.
[100] EpicFlow 16 2 color J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015.
[101] NNF-EAC 380 2 color Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336.
[102] Heeger++ 6600 5 gray Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238.
[103] HBM-GC 330 2 color A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015.
[104] FFV1MT 358 5 gray F. Solari, M. Chessa, N. Medathati, and P. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation? Submitted to Signal Processing: Image Communication 2015.
[105] ROF-ND 4 2 color S. Ali, C. Daul, E. Galbrun, and W. Blondel. Illumination invariant large displacement optical flow using robust neighbourhood descriptors. Submitted to CVIU 2015.
[106] DeepFlow2 16 2 color J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015.
[107] HAST 2667 2 color Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221.
[108] FlowFields 15 2 color C. Bailer, B. Taetz, and D. Stricker. Flow Fields: Dense unregularized correspondence fields for highly accurate large displacement optical flow estimation. ICCV 2015.
[109] SVFilterOh 1.56 2 color Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788.
[110] FlowNetS+ft+v 0.5 2 color Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235.
[111] CombBMOF 51 2 color M. Brüggemann, R. Kays, P. Springer, and O. Erdler. Combined block-matching and adaptive differential motion estimation in a hierarchical multi-scale framework. ICGIP 2014. (Method improved since publication.)
[112] PMMST 182 2 color F. Zhang, S. Xu, and X. Zhang. High accuracy correspondence field estimation via MST based patch matching. Submitted to TIP 2015.
[113] DF-Auto 70 2 color N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015.
[114] CPM-Flow 3 2 color Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241.
[115] CNN-flow-warp+ref 1.4 3 color D. Teney and M. Hebert. Learning to extract motion from videos in convolutional neural networks. ArXiv 1601.07532, 2016.
[116] Steered-L1 804 2 color Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016.
[117] StereoOF-V1MT 343 2 gray Anonymous. Visual features for action-oriented tasks: a cortical-like model for disparity and optic flow computation. BMVC 2016 submission 132.
[118] PGM-C 5 2 color Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016.
[119] RNLOD-Flow 1040 2 gray C. Zhang, Z. Chen, M. Wang, M. Li, and S. Jiang. Robust non-local TV-L1 optical flow estimation with occlusion detection. IEEE TIP 26(8):4055-4067, 2017.
[120] FlowNet2 0.091 2 color Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900.
[121] S2F-IF 20 2 color Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765.
[122] BriefMatch 0.068 2 gray G. Eilertsen, P.-E. Forssen, and J. Unger. Dense binary feature matching for real-time optical flow estimation. SCIA 2017 submission 62.
[123] OAR-Flow 60 2 color Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20.
[124] AdaConv-v1 2.8 2 color Simon Niklaus, Long Mai, and Feng Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017.
[125] SepConv-v1 0.2 2 color Simon Niklaus, Long Mai, and Feng Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017.
[126] ProbFlowFields 37 2 color A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017.
[127] UnFlow 0.12 2 color Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018.
[128] FlowFields+ 10.5 2 color C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. Submitted to PAMI 2017.
[129] IIOF-NLDP 150 2 color D.-H. Trinh, W. Blondel, and C. Daul. A general form of illumination-invariant descriptors in variational optical flow estimation. ICIP 2017.
[130] SuperSlomo 0.5 2 color Anonymous. (Interpolation results only.) Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. CVPR 2018 submission 325.
[131] EPMNet 0.061 2 color Anonymous. EPM-convolution multilayer-network for optical flow estimation. ICME 2018 submission 1119.
[132] OFRF 90 2 color Tan Khoa Mai, Michele Gouiffes, and Samia Bouchafa. Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints. IET Image Processing 2020.
[133] 3DFlow 328 2 color J. Chen, Z. Cai, J. Lai, and X. Xie. A filtering based framework for optical flow estimation. IEEE TCSVT 2018.
[134] CtxSyn 0.07 2 color Simon Niklaus and Feng Liu. (Interpolation results only.) Context-aware synthesis for video frame interpolation. CVPR 2018.
[135] DMF_ROB 10 2 color ROB 2018 baseline submission, based on: P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[136] JOF 657 2 gray C. Zhang, L. Ge, Z. Chen, M. Li, W. Liu, and H. Chen. Refined TV-L1 optical flow estimation using joint filtering. Submitted to IEEE TMM, 2018.
[137] AVG_FLOW_ROB N/A 2 N/A Average flow field of ROB 2018 training set.
[138] LiteFlowNet 0.06 2 color T.-W. Hui, X. Tang, and C. C. Loy. LiteFlowNet: A lightweight convolutional neural network for optical flow estimation. CVPR 2018.
[139] AugFNG_ROB 0.10 all color Anonymous. FusionNet and AugmentedFlowNet: Selective proxy ground truth for training on unlabeled images. ECCV 2018 submission 2834.
[140] ResPWCR_ROB 0.2 2 color Anonymous. Learning optical flow with residual connections. ROB 2018 submission.
[141] FF++_ROB 17.43 2 color R. Schuster, C. Bailer, O. Wasenmueller, D. Stricker. FlowFields++: Accurate optical flow correspondences meet robust interpolation. ICIP 2018. Submitted to ROB 2018.
[142] ProFlow_ROB 76 3 color Anonymous. ProFlow: Learning to predict optical flow. BMVC 2018 submission 277.
[143] PWC-Net_RVC 0.069 2 color D. Sun, X. Yang, M.-Y. Liu, and J. Kautz. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. CVPR 2018. Also RVC 2020 baseline submission.
[144] WOLF_ROB 0.02 2 color Anonymous. Reversed deep neural network for optical flow. ROB 2018 submission.
[145] LFNet_ROB 0.068 2 color Anonymous. Learning a flow network. ROB 2018 submission.
[146] WRT 9 2 color L. Mei, J. Lai, X. Xie, J. Zhu, and J. Chen. Illumination-invariance optical flow estimation using weighted regularization transform. Submitted to IEEE TCSVT 2018.
[147] EAI-Flow 2.1 2 color Anonymous. Hierarchical coherency sensitive hashing and interpolation with RANSAC for large displacement optical flow. CVIU 2018 submission 17-678.
[148] ContinualFlow_ROB 0.5 all color Michal Neoral, Jan Sochman, and Jiri Matas. Continual occlusions and optical flow estimation. ACCV 2018.
[149] CyclicGen 0.088 2 color Anonymous. (Interpolation results only.) Deep video frame interpolation using cyclic frame generation. AAAI 2019 submission 323.
[150] TOF-M 0.393 2 color Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William Freeman. Video enhancement with task-oriented flow. arXiv 1711.09078, 2017.
[151] MPRN 0.32 4 color Anonymous. (Interpolation results only.) Multi-frame pyramid refinement network for video frame interpolation. CVPR 2019 submission 1361.
[152] DAIN 0.13 2 color Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. (Interpolation results only.) DAIN: Depth-aware video frame interpolation. CVPR 2019.
[153] FRUCnet 0.65 2 color Van Thang Nguyen, Kyujoong Lee, and Hyuk-Jae Lee. (Interpolation results only.) A stacked deep MEMC network for frame rate up conversion and its application to HEVC. Submitted to IEEE TCSVT 2019.
[154] OFRI 0.31 2 color Anonymous. (Interpolation results only.) Efficient video frame interpolation via optical flow refinement. CVPR 2019 submission 6743.
[155] CompactFlow_ROB 0.05 2 color Anonymous. CompactFlow: spatially shiftable window revisited. CVPR 2019 submission 1387.
[156] SegFlow 3.2 2 color Jun Chen, Zemin Cai, Jianhuang Lai, and Xiaohua Xie. Efficient segmentation-based PatchMatch for large displacement optical flow estimation. IEEE TCSVT 2018.
[157] HCFN 0.18 2 color Anonymous. Practical coarse-to-fine optical flow with deep networks. ICCV 2019 submission 116.
[158] FGME 0.23 2 color Bo Yan, Weimin Tan, Chuming Lin, and Liquan Shen. (Interpolation results only.) Fine-grained motion estimation for video frame interpolation. IEEE Transactions on Broadcasting, 2020.
[159] MS-PFT 0.44 2 color Xianhang Cheng and Zhenzhong Chen. (Interpolation results only.) A multi-scale position feature transform network for video frame interpolation. IEEE TCSVT 2020.
[160] MEMC-Net+ 0.12 2 color Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. (Interpolation results only.) MEMC-Net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. Submitted to PAMI 2018.
[161] ADC 0.01 2 color Anonymous. (Interpolation results only.) Learning spatial transform for video frame interpolation. ICCV 2019 submission 5424.
[162] DSepConv 0.3 2 color Xianhang Cheng and Zhenzhong Chen. (Interpolation results only.) Video frame interpolation via deformable separable convolution. AAAI 2020.
[163] MAF-net 0.3 2 color Mengshun Hu, Jing Xiao, Liang Liao, Zheng Wang, Chia-Wen Lin, Mi Wang, and Shinichi Satoh. Capturing small, fast-moving objects: Frame interpolation via recurrent motion enhancement. IEEE TCSVT 2021.
[164] STAR-Net 0.049 2 color Anonymous. (Interpolation results only.) Space-time-aware multiple resolution for video enhancement. CPVR 2020 submission 430.
[165] AdaCoF 0.03 2 color Hyeongmin Lee, Taeoh Kim, Tae-young Chung, Daehyun Pak, Yuseok Ban, and Sangyoun Lee. (Interpolation results only.) AdaCoF: Adaptive collaboration of flows for video frame interpolation. CVPR 2020. Code available.
[166] TC-GAN 0.13 2 color Anonymous. (Interpolation results only.) A temporal and contextual generative adversarial network for video frame interpolation. CVPR 2020 submission 111.
[167] FeFlow 0.52 2 color Shurui Gui, Chaoyue Wang, Qihua Chen, and Dacheng Tao. (Interpolation results only.) FeatureFlow: Robust video interpolation via structure-to-texture generation. CVPR 2020. Code available.
[168] DAI 0.23 2 color Anonymous. (Interpolation results only.) Deep animation inbetweening. CVPR 2020 submission 6404.
[169] SoftSplat 0.1 2 color Simon Niklaus and Feng Liu. (Interpolation results only.) Softmax splatting for video frame interpolation. CVPR 2020.
[170] STSR 5.35 2 color Anonymous. (Interpolation results only.) Spatial and temporal video super-resolution with a frequency domain loss. ECCV 2020 submission 2340.
[171] BMBC 0.77 2 color Anonymous. (Interpolation results only.) BMBC: Bilateral motion estimation with bilateral cost volume for video interpolation. ECCV 2020 submission 2095.
[172] GDCN 1.0 2 color Anonymous. (Interpolation results only.) Video interpolation via generalized deformable convolution. ECCV 2020 submission 4347.
[173] EDSC 0.56 2 color Xianhang Cheng and Zhenzhong Chen. (Interpolation results only.) Multiple video frame interpolation via enhanced deformable separable convolution. Submitted to PAMI 2020.
[174] CoT-AMFlow 0.04 2 color Anonymous. CoT-AMFlow: Adaptive modulation network with co-teaching strategy for unsupervised optical flow estimation. CoRL 2020 submission 36.
[175] TVL1_RVC 11.6 2 color RVC 2020 baseline submission by Toby Weed, based on: Javier Sanchez, Enric Meinhardt-Llopis, and Gabriele Facciolo. TV-L1 optical flow estimation. IPOL 3:137-150, 2013.
[176] H+S_RVC 44.7 2 color RVC 2020 baseline submission by Toby Weed, based on: Enric Meinhardt-Llopis, Javier Sanchez, and Daniel Kondermann. Horn-Schunck optical flow with a multi-scale strategy. IPOL 3:151–172, 2013.
[177] PRAFlow_RVC 0.34 2 color Zhexiong Wan, Yuxin Mao, and Yuchao Dai. Pyramid recurrent all-pairs flow. RVC 2020 submission.
[178] VCN_RVC 0.84 2 color Gengshan Yang and Deva Ramanan. Volumetric correspondence networks for optical flow. NeurIPS 2019. RVC 2020 submission.
[179] RAFT-TF_RVC 1.51 2 color Deqing Sun, Charles Herrmann, Varun Jampani, Mike Krainin, Forrester Cole, Austin Stone, Rico Jonschkowski, Ramin Zabih, William Freeman, and Ce Liu. A TensorFlow implementation of RAFT (Zachary Teed and Jia Deng. RAFT: Recurrent all-pairs field transforms for optical flow. ECCV 2020.) RVC 2020 submission.
[180] IRR-PWC_RVC 0.18 2 color Junhwa Hur and Stefan Roth. Iterative residual refinement for joint optical flow and occlusion estimation. CVPR 2019. RVC 2020 submission.
[181] C-RAFT_RVC 0.60 2 color Henrique Morimitsu and Xiangyang Ji. Classification RAFT. RVC 2020 submission.
[182] LSM_FLOW_RVC 0.2 2 color Chengzhou Tang, Lu Yuan, and Ping Tan. LSM: Learning subspace minimization for low-level vision. CVPR 2020. RVC 2020 submission.
[183] MV_VFI 0.23 2 color Zhenfang Wang, Yanjiang Wang, and Baodi Liu. (Interpolation results only.) Multi-view based video interpolation algorithm. ICASSP 2021 submission.
[184] DistillNet 0.12 2 color Anonymous. (Interpolation results only.) A teacher-student optical-flow distillation framework for video frame interpolation. CVPR 2021 submission 7534.
[185] SepConv++ 0.1 2 color Simon Niklaus, Long Mai, and Oliver Wang. (Interpolation results only.) Revisiting adaptive convolutions for video frame interpolation. WACV 2021.
[186] EAFI 0.18 2 color Anonymous. (Interpolation results only.) Error-aware spatial ensembles for video frame interpolation. ICCV 2021 submission 8020.
[187] UnDAF 0.04 2 color Anonymous. UnDAF: A general unsupervised domain adaptation framework for disparity, optical flow or scene flow estimation. CVPR 2021 submission 236.
[188] FLAVR 0.029 all color Anonymous. (Interpolation results only.) FLAVR frame interpolation. NeurIPS 2021 submission 1300.
[189] PBOFVI 150 2 color Zemin Cai, Jianhuang Lai, Xiaoxin Liao, and Xucong Chen. Physics-based optical flow under varying illumination. Submitted to IEEE TCSVT 2021.
[190] SoftsplatAug 0.17 2 color Anonymous. (Interpolation results only.) Transformation data augmentation for sports video frame interpolation. ICCV 2021 submission 3245.
[191] ProBoost-Net 0.6 2 color Anonymous. (Interpolation results only.) Progressive Boosting Video Frame Interpolation. ACMMM 2021 submission 358.
[192] IDIAL 0.05 2 color Anonymous. (Interpolation results only.) Video frame interpolation via inter-frame distillation and intra-frame aggregation learning. AAAI 2022 submission 705.
[193] IFRNet 0.029 2 color Anonymous. (Interpolation results only.) IFRNet: Intermediate feature refine network for efficient frame interpolation. CVPR 2022 submission 3885.
* The "time" column lists the reported runtime in seconds on the "Urban" sequence. Note that these runtimes are not normalized by processor speed or type.