Optical flow evaluation results Statistics:     Average   SD   R2.5   R5.0   R10.0   A50   A75   A95  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
A95
angle
error
avg. Army
(Hidden texture)
GT   im0   im1
Mequon
(Hidden texture)
GT   im0   im1
Schefflera
(Hidden texture)
GT   im0   im1
Wooden
(Hidden texture)
GT   im0   im1
Grove
(Synthetic)
GT   im0   im1
Urban
(Synthetic)
GT   im0   im1
Yosemite
(Synthetic)
GT   im0   im1
Teddy
(Stereo)
GT   im0   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
RAFT-it+_RVC [198]6.8 6.80 2 47.4 70 3.53 1 5.31 4 30.0 6 5.25 13 4.10 2 25.5 3 3.30 3 1.60 1 47.6 12 1.34 1 9.19 1 12.0 1 4.78 1 4.00 1 18.0 4 3.50 1 3.21 2 8.07 14 1.99 5 2.08 5 4.16 7 1.38 4
RAFT-it [194]10.8 7.67 6 49.9 88 4.13 6 5.85 10 29.7 5 5.30 15 4.37 3 33.2 13 3.45 4 1.89 2 11.6 2 1.66 3 9.86 4 13.5 9 5.91 8 5.05 3 12.3 2 5.18 4 4.94 25 8.90 32 1.93 4 2.03 3 3.50 2 1.73 6
NNF-Local [75]14.5 7.47 4 40.1 10 3.98 5 6.49 18 30.3 7 5.60 23 5.82 7 26.1 4 4.68 22 3.86 14 53.5 13 2.99 23 9.77 3 12.4 3 5.36 5 8.67 10 31.8 11 7.03 8 5.10 33 10.1 70 3.70 21 2.31 14 5.34 18 1.21 2
MS_RAFT+_RVC [195]14.5 7.63 5 48.2 75 3.68 2 9.18 53 26.2 1 9.78 100 5.20 5 40.1 27 4.83 27 1.92 3 25.7 6 1.64 2 9.37 2 12.3 2 5.13 3 4.72 2 10.3 1 4.70 3 2.88 1 8.57 21 1.59 1 1.86 2 2.98 1 1.33 3
NN-field [71]18.2 8.38 16 43.1 28 4.19 7 7.34 31 28.7 4 6.26 34 5.82 7 28.9 8 4.68 22 2.94 5 54.1 14 2.16 6 10.4 7 13.2 6 5.24 4 6.12 4 17.5 3 4.46 2 6.24 71 10.6 88 4.10 23 2.35 18 6.44 29 1.14 1
MDP-Flow2 [68]22.5 8.02 10 38.6 6 5.75 26 5.17 2 31.1 8 4.55 3 5.48 6 30.8 11 4.22 13 4.49 25 99.9 96 3.27 33 11.3 18 13.4 8 8.04 28 10.8 22 54.4 48 10.5 29 4.84 18 9.33 46 4.31 29 2.69 34 4.85 12 2.20 9
PMMST [112]24.2 8.63 20 31.3 1 6.03 31 8.51 44 26.8 3 8.18 69 7.50 16 28.0 7 6.07 44 4.26 22 34.8 9 3.29 34 10.9 11 13.2 6 6.26 10 10.4 20 29.9 10 9.42 18 5.00 28 10.1 70 4.37 30 3.25 48 4.40 9 3.36 20
OFLAF [78]24.6 7.70 8 39.8 8 4.74 12 6.40 17 32.5 13 5.82 29 4.73 4 25.3 2 3.96 9 4.47 24 99.9 96 3.55 53 10.2 6 13.0 4 6.29 11 13.3 47 42.1 27 9.90 25 5.10 33 8.01 11 4.66 42 2.75 35 5.59 22 6.33 53
RAFT-TF_RVC [179]25.0 11.9 66 60.0 139 4.37 8 7.08 23 35.2 21 6.40 38 8.50 25 40.6 31 7.83 66 2.53 4 10.8 1 1.95 4 11.2 17 14.1 19 6.03 9 6.86 6 18.4 5 6.78 6 4.67 14 9.33 46 1.83 3 2.57 28 4.73 11 2.23 10
CoT-AMFlow [174]29.8 8.40 17 41.2 17 6.40 37 5.67 7 31.4 9 5.14 12 6.10 11 37.4 19 4.73 24 4.68 28 99.9 96 3.35 40 11.3 18 13.6 11 9.40 59 10.2 17 54.9 50 9.76 24 5.14 36 9.97 66 4.51 36 2.79 36 5.28 17 3.75 29
nLayers [57]33.7 8.19 13 45.3 55 4.62 10 9.65 69 31.7 11 8.88 90 8.87 27 33.6 14 8.22 73 3.62 10 99.9 96 2.93 20 10.5 8 13.6 11 6.52 12 11.3 29 33.4 12 9.45 20 6.02 66 8.56 20 4.99 49 2.31 14 6.80 34 5.53 46
NNF-EAC [101]35.9 8.80 22 40.8 14 6.14 34 6.13 12 39.3 36 5.36 16 6.97 13 35.1 16 4.73 24 5.83 51 87.9 77 3.49 49 12.1 37 14.6 27 8.87 40 12.5 43 41.2 23 11.8 49 5.35 45 10.1 70 4.61 39 3.19 45 7.62 46 3.99 34
UnDAF [187]36.6 8.95 25 46.1 59 6.07 33 5.77 9 32.1 12 4.85 7 8.11 20 34.6 15 4.52 17 4.79 33 99.9 96 3.31 37 11.4 21 13.6 11 8.98 45 11.9 37 55.4 52 10.2 27 5.18 38 10.0 68 4.57 38 5.19 80 10.4 68 3.76 30
PRAFlow_RVC [177]36.9 12.3 72 53.1 102 6.42 38 9.74 71 34.8 18 8.93 93 13.5 56 41.8 34 9.99 77 3.59 8 99.9 96 3.06 25 11.7 26 14.8 36 6.69 13 6.64 5 20.5 8 6.99 7 3.90 4 9.57 53 1.60 2 2.49 25 4.60 10 1.77 7
ComponentFusion [94]37.4 8.30 15 49.1 81 5.87 29 5.69 8 35.4 23 5.40 17 7.24 14 35.3 17 4.99 30 3.69 11 99.9 96 2.32 9 11.7 26 14.1 19 8.75 38 15.8 80 66.6 77 15.0 92 5.71 55 8.88 31 5.09 57 2.64 31 5.25 16 3.68 26
FC-2Layers-FF [74]38.6 8.26 14 41.4 19 6.27 35 8.85 48 37.9 30 7.81 53 6.02 10 31.8 12 6.25 46 3.91 16 88.8 80 2.86 18 11.0 14 13.7 15 7.20 16 16.5 88 40.4 19 16.3 106 7.14 92 10.7 92 6.61 81 2.15 7 3.87 4 2.77 12
LME [70]39.8 7.85 9 42.7 26 6.01 30 5.33 5 34.6 16 4.91 9 14.6 61 54.5 66 40.7 129 4.66 27 73.0 21 3.25 30 11.5 23 13.8 17 9.65 68 11.6 30 70.4 88 12.1 54 5.14 36 9.97 66 4.51 36 2.86 37 6.45 30 4.29 40
FESL [72]39.8 7.69 7 40.2 11 4.90 13 11.0 95 48.5 69 9.07 95 10.5 37 42.1 36 6.42 48 3.60 9 99.9 96 2.55 12 10.9 11 13.6 11 8.86 39 11.1 27 36.4 14 10.6 30 6.73 86 10.2 74 5.95 69 2.51 27 5.37 20 3.35 19
HAST [107]40.7 6.42 1 43.9 37 3.97 4 7.16 26 33.1 14 5.92 31 3.76 1 23.5 1 2.83 1 3.36 6 99.9 96 2.08 5 10.0 5 13.0 4 4.83 2 16.7 93 59.3 57 19.4 127 11.3 138 12.9 126 17.6 148 2.67 33 4.13 6 2.93 15
TC/T-Flow [77]41.1 9.01 27 38.1 4 3.81 3 6.64 20 55.1 91 4.62 4 8.13 21 46.4 52 4.20 12 5.32 43 99.9 96 2.88 19 11.5 23 14.1 19 7.28 18 8.85 11 38.5 17 9.44 19 5.85 62 10.7 92 10.0 124 3.61 57 10.0 64 8.53 88
Layers++ [37]43.5 9.07 28 44.4 40 8.41 73 8.47 43 31.5 10 8.03 62 5.85 9 37.9 21 6.02 43 3.76 13 62.6 15 2.79 15 10.5 8 13.5 9 8.22 33 17.6 101 55.0 51 14.5 89 7.44 99 10.9 96 5.70 68 2.27 12 4.86 13 9.14 92
PMF [73]43.8 9.38 35 48.9 79 4.67 11 7.10 24 37.5 27 5.58 22 7.91 18 30.0 10 4.06 10 4.89 39 99.9 96 3.29 34 10.6 10 14.0 18 5.76 7 12.4 42 54.8 49 11.5 42 11.2 137 17.9 157 11.1 129 2.06 4 4.95 15 4.00 35
WLIF-Flow [91]44.0 8.04 11 40.7 12 5.53 22 7.98 39 34.6 16 7.20 46 8.75 26 40.5 29 5.74 41 4.27 23 96.8 93 2.94 21 13.4 99 16.1 105 9.77 72 13.5 51 41.3 25 11.8 49 5.65 52 9.12 38 5.96 70 2.29 13 7.30 39 6.96 64
3DFlow [133]44.1 9.37 33 41.4 19 5.02 15 7.10 24 38.0 31 5.51 21 7.41 15 53.2 64 3.81 8 4.74 29 14.8 4 4.00 66 12.0 35 15.2 54 8.89 42 19.9 120 60.3 58 18.1 123 8.06 107 9.24 43 10.1 125 2.26 10 4.12 5 2.04 8
ALD-Flow [66]44.7 8.18 12 41.6 24 4.51 9 6.32 15 54.8 89 5.03 11 10.7 41 61.8 74 4.24 14 4.24 21 99.9 96 2.61 13 11.7 26 14.3 22 7.21 17 10.8 22 61.4 60 9.93 26 5.96 65 9.55 52 9.77 123 3.87 63 15.7 77 9.38 100
Efficient-NL [60]46.5 8.40 17 43.6 34 5.38 20 9.12 52 37.4 26 7.83 55 11.1 45 56.6 68 6.16 45 5.71 48 99.9 96 3.32 38 11.3 18 14.7 31 7.66 20 16.6 92 37.6 15 12.7 64 6.93 90 10.8 94 5.98 73 2.89 39 5.47 21 2.90 14
SVFilterOh [109]47.2 8.96 26 55.2 116 5.63 23 7.20 27 37.8 29 6.39 37 6.18 12 43.0 40 5.02 31 3.71 12 99.9 96 2.49 11 11.1 16 14.3 22 5.54 6 13.5 51 50.6 42 14.1 82 10.1 129 16.8 152 12.4 140 2.20 8 4.91 14 2.50 11
RNLOD-Flow [119]47.5 7.26 3 38.7 7 5.25 19 7.65 35 43.0 46 6.20 33 12.7 52 75.2 82 4.59 19 3.52 7 99.9 96 2.46 10 11.4 21 14.9 41 7.74 22 16.2 82 40.2 18 16.2 103 8.20 109 12.2 116 7.63 101 2.49 25 5.98 25 7.10 68
ProFlow_ROB [142]48.0 10.5 50 51.4 91 5.14 18 7.28 28 50.8 76 5.66 25 17.6 65 65.7 78 5.43 36 3.88 15 98.0 94 2.18 7 12.3 46 15.2 54 7.81 24 12.1 40 52.1 44 12.9 67 4.24 8 9.88 63 3.68 19 3.79 62 19.4 84 6.62 58
AGIF+OF [84]48.2 8.89 24 44.1 38 6.77 42 10.2 78 44.4 53 8.51 85 10.2 34 43.5 42 6.63 52 4.80 34 99.9 96 3.25 30 11.7 26 14.7 31 9.52 63 13.7 54 40.9 20 12.6 61 5.73 58 8.99 33 5.96 70 2.36 20 7.50 42 7.55 72
TC-Flow [46]49.9 8.59 19 41.0 16 5.12 16 5.47 6 45.6 57 4.31 2 10.2 34 94.7 128 3.49 5 6.08 57 99.9 96 3.50 50 11.9 33 14.5 25 7.90 26 11.9 37 61.7 63 11.5 42 5.72 56 9.85 61 11.5 134 4.02 67 15.0 75 9.14 92
PH-Flow [99]50.3 10.2 44 44.6 44 7.86 55 9.41 60 42.0 42 8.11 65 8.41 24 38.9 23 7.40 62 6.39 64 99.9 96 3.93 63 11.8 31 14.5 25 7.72 21 13.8 56 42.9 31 13.1 69 7.21 93 10.3 77 7.61 100 2.34 17 4.20 8 4.20 38
IROF++ [58]50.5 9.29 32 45.0 52 6.30 36 9.55 65 43.0 46 8.20 70 10.8 42 43.1 41 7.57 63 6.28 62 99.9 96 3.90 61 12.2 41 14.8 36 9.41 61 15.2 75 44.1 36 14.5 89 5.27 40 9.48 50 3.69 20 2.62 30 6.48 31 4.14 37
PWC-Net_RVC [143]51.2 17.5 106 49.3 84 9.65 98 9.55 65 42.8 44 8.41 75 19.6 69 46.1 51 10.6 81 6.18 61 74.3 23 3.39 42 12.4 49 15.4 67 8.92 43 11.8 34 45.5 37 11.5 42 4.32 10 9.79 57 2.49 10 3.08 41 6.38 28 2.86 13
Correlation Flow [76]52.0 9.27 31 38.3 5 5.40 21 6.33 16 36.7 24 4.85 7 18.4 67 99.9 138 3.58 6 4.87 37 35.8 10 3.47 46 12.9 74 15.8 86 9.17 53 16.0 81 68.6 81 16.5 113 6.59 82 9.85 61 7.93 109 2.88 38 7.57 45 3.06 16
GMFlow_RVC [196]52.2 19.5 109 45.1 53 13.9 123 9.94 75 26.3 2 10.2 104 9.46 30 27.3 6 7.68 65 6.61 69 39.2 11 5.12 93 13.1 85 16.4 118 9.40 59 10.0 15 20.2 7 9.27 17 7.28 96 10.5 85 4.10 23 1.84 1 3.73 3 1.45 5
Classic+CPF [82]52.3 9.64 38 43.4 30 7.93 58 9.43 61 46.1 60 7.82 54 10.6 40 51.0 59 6.68 53 5.09 41 99.9 96 3.22 29 12.0 35 15.2 54 9.15 51 14.7 67 34.4 13 13.4 74 6.42 76 10.1 70 6.89 86 2.26 10 6.77 33 7.08 67
JOF [136]53.8 8.67 21 45.9 57 6.43 39 10.3 81 48.5 69 8.94 94 8.37 23 39.4 25 7.19 57 4.81 35 99.9 96 2.79 15 11.9 33 14.6 27 8.03 27 13.9 59 43.0 32 10.8 31 9.32 122 11.4 104 11.3 133 2.21 9 7.25 37 7.07 66
CostFilter [40]54.5 10.5 50 46.8 67 6.98 46 7.51 32 38.1 32 6.31 36 9.22 28 29.7 9 4.74 26 5.86 53 99.9 96 3.97 65 10.9 11 14.3 22 6.77 14 13.4 48 56.1 53 12.3 57 11.6 141 20.5 161 14.2 142 2.12 6 8.52 53 6.71 60
ProbFlowFields [126]55.5 16.3 96 53.8 106 10.9 114 7.72 36 40.6 40 7.12 43 14.1 59 45.2 50 10.5 79 6.67 70 62.6 15 4.39 81 12.9 74 15.4 67 9.64 67 10.1 16 63.2 69 10.8 31 4.86 20 8.16 17 4.69 44 3.36 50 9.23 60 3.72 28
VCN_RVC [178]56.0 17.3 104 47.3 69 10.1 105 10.3 81 43.6 50 9.46 99 15.8 64 37.4 19 12.4 89 8.26 83 73.5 22 4.90 91 12.2 41 15.3 62 8.42 35 11.7 32 53.2 46 12.2 55 4.55 12 8.36 19 3.12 16 3.52 52 8.67 57 4.35 41
HCFN [157]56.4 8.86 23 40.7 12 5.67 24 5.27 3 43.8 51 4.64 5 7.53 17 39.4 25 3.23 2 4.87 37 99.9 96 3.58 54 11.0 14 13.7 15 7.15 15 15.4 77 53.4 47 16.3 106 14.7 154 16.8 152 17.8 149 5.63 88 12.6 70 10.6 117
IIOF-NLDP [129]57.3 12.0 67 43.5 32 5.86 28 9.35 56 35.0 19 6.81 41 11.2 46 88.4 123 4.51 16 6.09 58 28.8 7 4.09 73 14.6 132 18.4 146 9.71 69 14.6 66 66.2 76 14.7 91 4.90 24 9.54 51 4.82 47 3.09 42 7.73 49 3.18 17
Sparse-NonSparse [56]57.9 9.96 39 44.2 39 8.85 82 9.39 59 50.6 75 8.08 64 10.1 33 43.7 44 7.21 58 6.10 59 88.2 78 3.41 44 12.5 55 15.5 77 8.96 44 16.3 84 41.9 26 16.2 103 6.48 78 9.05 35 6.27 77 2.33 16 7.33 40 7.76 80
MLDP_OF [87]58.2 11.8 65 41.7 25 8.40 72 6.97 21 35.2 21 5.88 30 11.3 49 65.3 77 5.23 34 4.76 32 99.9 96 3.09 26 12.2 41 14.8 36 8.87 40 13.4 48 48.1 39 17.2 119 10.0 128 10.9 96 18.1 150 3.58 56 8.07 52 4.53 44
Ramp [62]58.6 10.2 44 44.4 40 8.09 67 9.47 62 46.1 60 8.17 68 9.51 31 42.4 38 6.88 56 5.40 44 99.9 96 3.53 51 12.5 55 15.2 54 9.71 69 16.7 93 42.1 27 16.5 113 6.76 87 10.0 68 7.07 90 2.46 24 5.84 24 5.24 45
LSM [39]58.8 10.0 42 42.9 27 8.48 74 9.36 58 49.6 73 7.99 59 10.5 37 43.6 43 6.80 54 5.80 49 88.6 79 3.38 41 12.5 55 15.4 67 9.03 47 16.5 88 42.3 29 16.3 106 6.94 91 9.84 58 6.71 82 2.42 23 7.96 51 7.72 77
WRT [146]58.9 10.9 55 48.8 77 5.12 16 10.2 78 39.2 34 8.48 81 39.5 116 99.9 138 4.62 20 4.75 31 14.6 3 3.39 42 11.7 26 15.0 45 9.93 81 21.2 124 64.7 74 17.5 121 5.35 45 8.80 28 5.37 63 3.22 47 7.63 47 3.38 22
OAR-Flow [123]59.0 11.1 59 48.8 77 5.85 27 9.88 72 82.9 163 6.47 39 27.3 91 99.9 138 8.06 69 6.75 71 99.9 96 2.80 17 12.4 49 15.1 52 8.20 32 10.3 18 58.1 55 8.37 13 4.07 5 8.06 13 5.45 65 4.81 77 9.74 63 6.36 54
MCPFlow_RVC [197]61.1 22.3 128 57.7 129 14.2 126 15.2 109 40.3 38 14.0 117 22.2 77 42.4 38 19.2 109 5.49 46 32.3 8 4.07 71 12.7 66 16.2 110 7.35 19 9.94 14 19.3 6 9.67 22 4.77 16 10.3 77 2.34 9 3.72 60 7.07 35 4.12 36
Classic+NL [31]62.5 10.1 43 44.9 50 8.90 83 9.49 63 51.6 79 7.87 56 9.93 32 43.9 46 7.31 61 6.07 56 99.9 96 3.78 59 12.5 55 15.3 62 9.06 48 17.1 97 41.0 21 15.8 100 7.32 97 10.8 94 6.80 85 2.35 18 5.62 23 7.69 76
FMOF [92]62.6 9.17 29 43.6 34 8.04 64 10.0 76 48.1 66 8.48 81 8.35 22 38.3 22 6.49 50 5.08 40 99.9 96 3.45 45 12.6 61 15.4 67 9.19 54 18.1 108 41.2 23 15.5 97 6.67 85 10.6 88 7.47 97 3.00 40 16.1 79 7.74 78
MDP-Flow [26]62.8 11.2 60 43.1 28 9.86 102 8.14 42 35.1 20 8.21 71 11.2 46 42.1 36 9.44 76 6.41 65 99.9 96 4.20 77 12.2 41 14.6 27 10.0 84 11.7 32 63.6 70 9.60 21 5.56 50 10.9 96 4.39 31 5.78 89 99.9 158 8.99 89
TV-L1-MCT [64]63.2 9.57 36 44.7 46 8.66 77 10.9 94 48.1 66 9.11 96 11.8 50 58.1 70 6.61 51 4.74 29 99.9 96 3.34 39 12.9 74 15.2 54 9.89 77 17.8 105 47.8 38 16.0 102 5.28 41 8.09 15 7.71 102 3.33 49 7.26 38 7.53 71
IROF-TV [53]63.5 10.4 48 44.5 43 8.16 68 9.69 70 51.1 78 8.44 76 12.6 51 46.8 53 7.27 60 6.80 72 87.5 76 3.93 63 13.0 79 15.7 82 10.4 89 18.3 110 86.9 151 13.7 78 4.44 11 7.40 5 3.05 14 2.60 29 7.55 44 7.56 73
CombBMOF [111]63.8 12.5 73 41.3 18 6.50 41 8.58 46 36.9 25 6.88 42 10.9 43 35.9 18 5.21 33 10.4 93 85.0 74 5.90 103 11.6 25 15.2 54 8.15 30 27.3 136 60.9 59 35.6 149 9.20 120 14.7 141 6.75 84 3.17 44 7.53 43 4.20 38
NL-TV-NCC [25]64.5 10.7 53 40.8 14 6.45 40 8.52 45 41.1 41 6.30 35 11.2 46 93.6 127 4.18 11 5.99 55 75.9 27 4.02 67 13.2 88 16.2 110 10.1 86 16.7 93 70.9 90 16.3 106 6.56 81 9.91 65 7.05 89 4.76 75 16.9 80 3.56 24
COFM [59]65.0 9.37 33 55.5 118 6.86 44 7.28 28 44.2 52 6.17 32 14.3 60 47.6 56 8.22 73 4.15 19 99.9 96 2.23 8 13.2 88 16.2 110 12.2 123 17.6 101 75.4 98 15.5 97 6.20 70 8.77 27 7.35 94 3.62 58 5.35 19 6.43 56
OFH [38]65.8 12.6 74 43.4 30 9.45 91 7.30 30 64.4 102 5.27 14 27.6 92 99.9 138 4.87 29 6.60 68 99.9 96 3.74 57 12.4 49 14.7 31 9.62 65 15.5 78 74.1 95 15.6 99 4.60 13 9.39 48 4.64 40 5.39 84 26.0 96 6.68 59
FlowFields+ [128]66.4 20.3 113 52.0 95 10.3 110 10.3 81 44.8 55 8.44 76 19.5 68 40.2 28 14.1 96 10.2 91 66.6 17 6.28 108 12.8 70 15.4 67 9.97 82 10.3 18 61.8 64 10.2 27 4.85 19 10.9 96 4.79 46 3.97 65 12.8 71 3.85 31
Adaptive [20]66.7 10.2 44 46.1 59 4.95 14 9.63 67 55.4 92 7.80 52 36.7 111 99.9 138 7.64 64 6.15 60 78.7 30 2.96 22 12.1 37 14.8 36 9.09 49 12.3 41 85.8 149 6.06 5 8.72 114 12.5 122 4.97 48 3.55 55 34.8 100 9.13 91
S2F-IF [121]67.1 20.0 112 51.4 91 9.91 103 9.64 68 48.3 68 7.93 57 19.7 70 41.7 33 13.6 95 9.98 89 84.3 70 5.40 97 12.8 70 15.3 62 9.92 80 10.9 25 62.4 67 10.9 35 5.00 28 10.4 82 5.30 62 3.71 59 8.55 55 3.87 32
AggregFlow [95]67.6 13.2 76 62.1 144 6.79 43 14.9 108 73.1 113 10.6 107 26.8 90 55.1 67 20.5 111 5.48 45 99.9 96 3.67 55 12.5 55 15.0 45 7.76 23 8.55 9 38.2 16 8.90 15 5.78 59 10.6 88 4.74 45 5.43 85 8.53 54 7.58 74
FlowFields [108]68.0 20.3 113 52.3 97 10.2 106 10.2 78 49.0 71 8.46 79 20.3 71 40.5 29 14.7 98 10.8 98 76.6 28 6.16 106 12.9 74 15.4 67 10.0 84 11.1 27 69.9 85 11.0 37 4.97 27 8.63 24 5.12 59 4.04 69 14.0 72 3.98 33
S2D-Matching [83]68.7 9.96 39 53.0 101 8.51 75 9.53 64 53.0 81 7.94 58 20.5 72 99.9 138 6.80 54 5.30 42 83.0 33 3.53 51 12.4 49 15.2 54 9.16 52 17.3 98 41.1 22 16.8 117 7.75 103 10.5 85 7.90 108 2.36 20 6.34 27 9.58 105
Complementary OF [21]69.3 13.6 80 46.2 61 9.35 88 6.20 13 50.4 74 4.92 10 12.8 54 58.8 72 5.45 37 7.89 79 99.9 96 5.59 99 12.3 46 14.6 27 9.99 83 18.9 115 69.9 85 14.3 86 5.44 47 7.80 7 7.78 104 6.13 95 26.9 97 9.66 108
Sparse Occlusion [54]69.6 9.98 41 41.5 23 7.82 54 9.00 51 40.5 39 8.28 73 13.5 56 85.5 120 5.96 42 5.82 50 99.9 96 3.90 61 13.0 79 15.9 89 9.77 72 13.8 56 49.9 41 12.3 57 13.6 152 15.7 148 7.81 106 3.51 51 9.05 58 6.42 55
HBM-GC [103]69.9 10.9 55 57.5 128 7.03 48 9.35 56 40.2 37 8.80 88 8.09 19 52.3 62 6.42 48 6.91 74 84.3 70 6.17 107 11.8 31 14.7 31 8.40 34 14.7 67 43.2 33 12.6 61 9.81 127 17.8 156 8.50 114 3.54 54 10.1 65 10.1 113
2DHMM-SAS [90]71.5 10.3 47 44.8 48 8.03 63 10.5 89 52.4 80 8.21 71 21.6 74 97.4 133 8.20 72 6.88 73 99.9 96 3.86 60 12.4 49 15.0 45 9.87 75 17.7 103 43.3 34 15.9 101 6.81 88 10.2 74 7.15 93 2.65 32 7.68 48 7.29 69
PBOFVI [189]71.6 10.9 55 47.7 74 8.77 79 6.98 22 53.2 83 5.50 20 12.7 52 99.9 138 3.61 7 4.15 19 78.8 31 3.02 24 14.4 128 17.6 137 11.3 108 18.0 106 51.9 43 18.5 126 5.67 54 12.2 116 7.80 105 3.11 43 10.1 65 8.10 83
ACK-Prior [27]72.8 10.7 53 37.9 3 7.90 57 6.01 11 38.5 33 4.80 6 10.2 34 41.5 32 4.35 15 4.56 26 99.9 96 3.75 58 13.2 88 15.9 89 11.3 108 27.3 136 82.2 108 23.1 136 11.6 141 14.9 145 16.2 146 6.43 100 15.5 76 6.11 50
SimpleFlow [49]72.8 11.3 62 46.6 63 9.79 101 10.7 92 45.0 56 9.15 97 23.1 79 99.9 138 8.38 75 8.00 81 99.9 96 3.72 56 12.7 66 15.5 77 9.36 58 16.3 84 42.6 30 15.3 96 5.91 64 9.61 54 5.39 64 2.39 22 7.08 36 9.41 101
EPPM w/o HM [86]72.9 15.3 91 41.4 19 8.08 66 7.60 33 33.9 15 5.66 25 13.0 55 47.0 54 5.57 38 8.73 86 99.9 96 4.81 90 12.6 61 15.7 82 10.8 98 18.6 112 62.9 68 16.4 111 11.9 146 12.5 122 17.2 147 3.20 46 7.49 41 6.00 48
RFlow [88]73.1 11.5 63 45.3 55 8.80 81 6.22 14 49.2 72 5.41 18 26.2 88 99.9 138 5.04 32 4.14 18 99.9 96 3.11 27 12.6 61 15.0 45 9.87 75 16.5 88 83.2 144 13.8 81 6.62 83 8.58 22 6.16 74 6.33 98 99.9 158 12.0 123
PGM-C [118]73.2 20.6 117 54.2 109 9.59 96 10.1 77 60.8 96 8.47 80 22.3 78 44.3 49 14.8 101 10.7 97 99.9 96 4.15 75 13.1 85 15.4 67 9.90 78 11.8 34 64.6 73 12.0 53 4.86 20 7.96 9 5.01 52 4.78 76 14.4 73 7.01 65
SegFlow [156]73.2 20.5 115 54.3 110 9.56 93 10.3 81 62.6 100 8.50 83 21.9 75 44.1 48 14.7 98 10.6 95 99.9 96 4.05 69 13.2 88 15.3 62 10.1 86 11.8 34 65.3 75 12.3 57 5.03 31 8.62 23 5.06 56 4.18 70 10.1 65 5.68 47
Occlusion-TV-L1 [63]73.3 10.4 48 44.9 50 6.90 45 8.77 47 53.3 84 7.54 48 33.8 106 99.9 138 7.96 67 5.88 54 99.9 96 3.48 48 13.6 108 16.3 114 10.6 94 9.50 12 80.1 105 8.60 14 6.12 68 8.69 25 4.39 31 6.52 103 99.9 158 9.37 97
ROF-ND [105]75.6 12.0 67 39.9 9 8.22 71 6.49 18 45.6 57 5.49 19 13.5 56 92.7 124 4.83 27 8.05 82 23.7 5 5.54 98 14.2 124 17.5 133 11.2 106 20.1 122 72.0 92 15.0 92 13.0 150 13.3 131 10.5 128 3.52 52 6.67 32 3.36 20
CPM-Flow [114]78.5 20.6 117 54.3 110 9.58 94 10.3 81 62.5 99 8.50 83 21.9 75 43.8 45 14.7 98 10.6 95 99.9 96 4.06 70 13.2 88 15.4 67 9.85 74 12.6 44 68.9 82 13.3 71 5.02 30 9.16 40 5.04 54 5.27 81 19.2 82 9.63 107
ComplOF-FED-GPU [35]79.3 13.2 76 44.7 46 7.95 59 9.18 53 82.6 162 5.63 24 15.3 62 58.5 71 5.67 39 7.59 77 99.9 96 4.68 86 12.3 46 14.8 36 9.20 55 18.2 109 83.8 145 16.4 111 7.54 101 9.84 58 11.1 129 5.44 86 31.6 99 7.74 78
SRR-TVOF-NL [89]79.9 14.4 87 46.7 64 8.18 70 13.1 102 74.0 115 8.44 76 24.1 83 63.2 76 11.9 88 6.51 67 85.1 75 3.25 30 12.1 37 15.0 45 10.3 88 17.5 99 61.6 61 13.4 74 10.4 133 12.3 119 8.92 117 5.52 87 7.83 50 7.58 74
EpicFlow [100]80.1 20.6 117 54.1 108 9.59 96 10.3 81 62.9 101 8.55 86 26.3 89 99.4 137 15.1 103 10.4 93 99.9 96 4.07 71 13.1 85 15.4 67 9.90 78 11.6 30 67.3 79 11.9 52 4.86 20 7.97 10 4.99 49 5.34 83 19.2 82 9.74 110
Steered-L1 [116]80.8 9.19 30 36.3 2 6.06 32 4.59 1 39.2 34 4.30 1 9.30 29 52.3 62 4.57 18 4.86 36 99.9 96 3.30 36 13.6 108 15.9 89 12.3 124 24.7 135 77.0 101 20.2 128 15.1 156 13.7 136 40.0 158 14.7 135 91.5 156 20.9 137
C-RAFT_RVC [181]80.9 31.8 143 61.9 143 12.9 118 21.3 124 42.6 43 20.0 128 25.0 86 44.0 47 19.5 110 12.4 104 72.6 19 7.52 117 13.9 117 16.8 125 11.8 114 10.9 25 28.8 9 11.3 39 6.25 73 11.9 112 4.64 40 3.76 61 6.24 26 3.26 18
TCOF [69]81.7 13.6 80 44.8 48 8.02 62 9.90 73 54.6 88 8.02 61 31.3 102 99.9 138 15.4 105 6.49 66 82.4 32 4.76 88 14.9 136 18.0 143 9.50 62 9.71 13 48.3 40 12.6 61 10.1 129 12.7 124 8.96 118 4.29 71 9.21 59 6.80 61
DMF_ROB [135]82.0 16.2 95 49.6 86 9.96 104 9.93 74 75.9 117 7.56 49 30.5 99 99.9 138 11.8 86 16.5 116 99.9 96 4.36 80 12.6 61 14.9 41 9.62 65 12.9 46 76.3 100 11.5 42 4.68 15 8.80 28 5.10 58 7.02 111 91.0 155 9.62 106
DPOF [18]82.2 17.4 105 49.1 81 7.77 53 12.3 98 45.6 57 8.91 92 10.9 43 26.6 5 8.09 70 7.81 78 99.3 95 5.31 94 13.5 104 16.0 100 11.0 101 17.5 99 61.8 64 12.2 55 13.1 151 10.9 96 18.1 150 5.04 78 9.50 62 4.49 42
DeepFlow2 [106]83.1 14.3 85 47.4 70 7.03 48 10.4 87 77.1 120 8.01 60 23.3 80 99.9 138 11.8 86 16.1 112 99.9 96 4.41 82 12.4 49 15.0 45 8.16 31 13.4 48 68.4 80 14.2 83 5.65 52 9.07 37 8.50 114 8.52 118 92.9 157 10.6 117
LiteFlowNet [138]84.5 24.0 132 51.5 93 13.3 120 11.7 96 43.1 48 9.98 102 21.5 73 47.6 56 13.0 91 10.9 100 71.9 18 6.87 111 13.3 97 16.2 110 11.5 112 18.7 113 52.2 45 16.7 115 6.18 69 9.32 45 4.21 26 5.83 91 24.4 94 7.46 70
Aniso. Huber-L1 [22]84.5 11.7 64 43.8 36 8.16 68 13.6 104 66.3 104 12.0 110 35.9 109 99.9 138 10.5 79 10.0 90 72.9 20 5.00 92 13.4 99 16.3 114 9.61 64 15.1 73 63.7 71 7.96 12 8.96 117 11.6 108 7.95 110 4.02 67 26.9 97 7.97 82
FF++_ROB [141]85.2 21.4 123 55.5 118 10.2 106 10.6 91 54.1 86 8.70 87 24.1 83 59.5 73 16.2 107 11.5 102 74.8 25 7.42 115 13.0 79 15.7 82 10.6 94 14.7 67 70.0 87 13.7 78 5.31 44 8.76 26 7.53 99 4.57 73 14.8 74 13.9 127
TF+OM [98]86.1 12.1 69 51.3 90 7.13 50 8.92 50 44.4 53 8.13 67 33.8 106 54.4 65 45.8 133 6.28 62 90.1 82 4.63 85 12.7 66 15.2 54 10.6 94 18.9 115 99.9 166 11.3 39 7.81 104 14.2 138 6.33 78 7.09 112 43.5 103 8.22 85
F-TV-L1 [15]87.2 15.6 92 47.4 70 13.4 121 18.8 119 99.1 172 11.6 108 43.1 127 99.9 138 11.3 85 14.7 108 99.9 96 7.03 112 12.2 41 14.9 41 9.00 46 13.5 51 99.9 166 7.56 11 6.41 75 10.5 85 4.23 27 3.91 64 80.3 115 3.38 22
CVENG22+RIC [199]91.0 19.5 109 55.1 115 8.94 84 11.8 97 71.1 110 8.89 91 29.2 93 99.9 138 14.1 96 10.3 92 99.9 96 4.03 68 14.5 131 17.7 139 11.0 101 12.0 39 90.1 154 11.7 47 4.86 20 7.94 8 4.99 49 5.98 93 35.9 101 9.94 112
TV-L1-improved [17]91.3 10.9 55 45.2 54 7.42 51 8.12 40 54.0 85 6.79 40 36.5 110 99.9 138 7.26 59 5.84 52 99.9 96 3.15 28 13.2 88 15.9 89 9.11 50 22.1 129 99.9 166 20.8 129 9.59 125 13.3 131 9.04 119 6.19 96 88.8 152 9.71 109
SIOF [67]91.8 10.6 52 49.7 87 7.01 47 14.8 107 85.9 165 8.40 74 49.7 135 98.3 134 49.2 136 12.0 103 99.9 96 5.88 102 13.5 104 15.9 89 10.8 98 16.3 84 74.2 96 13.6 77 5.51 49 9.02 34 4.42 34 6.52 103 19.5 87 9.85 111
CompactFlow_ROB [155]92.0 34.2 147 63.6 147 16.6 134 19.0 121 46.3 62 18.4 125 37.1 112 62.7 75 45.3 132 15.6 111 99.9 96 10.6 126 13.6 108 16.3 114 11.1 104 14.1 61 64.2 72 12.8 65 4.96 26 8.13 16 2.28 8 5.98 93 22.2 90 6.91 62
Brox et al. [5]92.5 16.0 93 49.2 83 12.0 116 12.3 98 80.4 125 10.3 105 23.7 82 73.1 81 13.2 92 24.2 125 99.9 96 4.23 78 14.7 133 16.8 125 15.4 146 10.7 21 96.7 159 9.71 23 5.88 63 9.05 35 3.01 13 8.78 119 67.7 111 9.37 97
DeepFlow [85]93.3 14.7 88 49.0 80 9.78 100 12.9 100 79.3 124 9.80 101 30.1 96 96.1 131 24.4 117 21.4 123 99.9 96 5.36 96 12.5 55 15.1 52 8.59 36 14.0 60 71.9 91 15.1 95 5.46 48 8.01 11 8.73 116 14.2 133 99.9 158 15.7 133
CRTflow [81]93.8 15.0 89 46.3 62 7.89 56 8.87 49 54.9 90 7.15 44 30.1 96 99.9 138 8.03 68 9.30 88 99.9 96 4.50 84 13.0 79 15.7 82 8.05 29 32.5 141 99.9 166 34.3 148 6.62 83 9.72 56 7.52 98 9.30 122 99.9 158 14.7 129
LocallyOriented [52]94.9 17.0 103 55.7 120 8.00 61 17.0 116 82.3 161 12.1 111 42.4 125 99.9 138 14.8 101 9.13 87 89.2 81 4.79 89 13.4 99 16.1 105 9.20 55 10.8 22 58.1 55 11.8 49 6.89 89 10.6 88 7.14 92 7.78 117 74.9 112 9.42 102
BriefMatch [122]96.0 9.63 37 44.4 40 5.74 25 7.64 34 51.0 77 5.70 27 10.5 37 39.2 24 4.63 21 3.95 17 99.9 96 2.72 14 16.2 147 17.6 137 33.0 159 41.4 153 99.4 165 43.4 155 12.7 149 13.2 129 67.6 162 79.5 153 99.9 158 99.9 189
ContinualFlow_ROB [148]98.1 30.9 140 63.0 146 13.9 123 22.3 126 47.9 65 20.9 129 34.2 108 99.9 138 31.9 123 13.3 105 99.9 96 7.83 119 13.7 114 16.5 122 11.4 110 21.5 126 70.6 89 21.3 132 4.20 7 10.3 77 2.80 11 4.01 66 8.59 56 3.70 27
ResPWCR_ROB [140]98.5 23.4 130 46.7 64 16.7 136 14.7 106 42.9 45 13.2 113 23.4 81 48.7 58 22.0 114 16.4 115 84.5 72 11.3 130 13.2 88 15.8 86 12.6 129 15.2 75 72.1 93 16.3 106 9.13 119 12.3 119 7.37 95 7.09 112 17.6 81 9.31 96
Classic++ [32]98.6 11.2 60 49.4 85 9.13 85 9.34 55 68.4 107 8.11 65 30.7 100 95.1 130 10.2 78 5.59 47 99.9 96 3.47 46 13.5 104 16.1 105 10.4 89 19.7 119 99.9 166 17.6 122 8.38 110 11.5 107 8.30 113 7.20 115 99.9 158 9.54 104
Dynamic MRF [7]98.6 14.0 84 50.7 89 9.58 94 7.75 37 85.7 164 5.76 28 31.5 103 99.9 138 5.23 34 7.97 80 99.9 96 4.10 74 13.0 79 15.6 80 10.7 97 30.4 140 99.9 166 29.5 145 5.64 51 7.52 6 9.61 122 67.3 151 99.9 158 66.7 151
CBF [12]99.8 12.2 71 41.4 19 8.65 76 16.5 113 47.1 64 16.6 121 24.7 85 88.1 122 12.9 90 11.0 101 99.9 96 4.18 76 14.9 136 17.5 133 14.0 141 15.0 72 79.8 104 8.97 16 14.9 155 15.1 146 15.9 145 5.78 89 63.0 110 10.1 113
Rannacher [23]99.9 13.8 83 47.5 73 10.8 113 10.5 89 62.0 98 8.84 89 41.1 122 99.9 138 11.0 83 8.49 84 99.9 96 4.28 79 13.5 104 16.1 105 9.72 71 22.5 131 99.9 166 17.0 118 7.66 102 9.88 63 7.82 107 4.72 74 75.1 113 9.37 97
IRR-PWC_RVC [180]100.9 41.0 153 70.3 194 16.1 131 29.3 134 56.2 94 26.3 137 42.9 126 92.7 124 53.0 138 28.8 133 77.5 29 23.8 139 13.0 79 15.8 86 10.5 93 13.7 54 61.6 61 11.5 42 7.43 98 11.9 112 4.00 22 6.43 100 22.6 91 6.22 52
AugFNG_ROB [139]101.5 34.3 149 65.6 151 20.9 142 25.8 130 66.4 105 24.3 133 40.9 120 99.9 138 40.6 128 15.2 110 96.0 90 9.61 122 13.4 99 15.6 80 12.3 124 15.1 73 73.5 94 14.2 83 5.29 42 11.2 102 2.87 12 5.30 82 19.4 84 4.51 43
TriFlow [93]102.8 16.1 94 57.1 126 7.97 60 13.3 103 65.2 103 12.3 112 48.8 132 99.9 138 61.8 143 7.03 75 91.4 84 5.33 95 13.4 99 15.3 62 11.4 110 14.3 65 76.1 99 13.3 71 22.7 161 14.3 140 26.7 154 5.93 92 11.8 69 7.86 81
EAI-Flow [147]102.9 25.2 133 57.0 125 16.6 134 19.3 122 76.7 118 14.1 118 29.6 95 51.5 61 22.8 115 23.6 124 93.8 87 10.3 124 12.8 70 16.0 100 10.4 89 15.6 79 74.4 97 14.4 88 12.6 148 11.3 103 6.90 87 5.08 79 20.5 89 8.20 84
p-harmonic [29]103.4 15.1 90 48.5 76 14.1 125 10.4 87 53.1 82 9.31 98 41.9 123 99.9 138 15.1 103 19.4 122 99.9 96 10.6 126 12.7 66 14.9 41 11.8 114 18.0 106 85.6 148 18.4 125 7.85 105 10.4 82 5.46 66 6.98 110 99.9 158 9.28 95
FlowNet2 [120]103.5 32.0 144 68.0 157 14.9 128 35.2 138 77.9 123 29.8 143 32.9 104 51.2 60 35.4 126 16.3 113 99.9 96 10.5 125 13.2 88 15.9 89 12.0 117 14.1 61 99.9 166 10.8 31 8.84 116 20.1 158 4.66 42 4.49 72 9.46 61 3.65 25
SegOF [10]104.4 22.8 129 54.8 112 15.4 130 27.9 133 56.0 93 27.4 138 39.3 115 87.9 121 33.2 125 37.5 138 75.4 26 22.3 137 14.4 128 16.3 114 14.7 144 21.7 127 99.9 166 24.5 138 4.09 6 7.28 2 2.18 7 6.79 108 48.3 106 6.93 63
CLG-TV [48]104.5 12.1 69 43.5 32 9.42 90 14.1 105 60.9 97 13.2 113 33.2 105 99.9 138 11.2 84 10.8 98 84.7 73 5.82 101 14.8 135 17.9 141 12.1 121 13.8 56 99.9 166 11.3 39 10.9 135 14.2 138 9.22 120 6.69 107 99.9 158 8.52 87
DF-Auto [113]104.5 20.5 115 55.9 122 9.21 86 22.7 127 74.8 116 19.3 127 44.7 129 93.5 126 57.8 141 27.1 130 99.9 96 5.70 100 15.0 139 18.9 153 11.8 114 7.13 7 57.9 54 7.09 9 10.2 132 13.9 137 4.23 27 8.94 120 54.0 108 9.21 94
OFRF [132]104.8 13.1 75 58.6 133 9.77 99 49.6 151 99.9 173 43.7 152 49.2 133 99.9 138 32.0 124 16.3 113 96.4 91 10.0 123 12.1 37 14.7 31 7.87 25 14.9 71 43.6 35 13.3 71 8.66 113 12.0 115 11.8 137 21.9 141 19.7 88 36.6 145
Local-TV-L1 [65]104.8 16.5 98 52.3 97 11.7 115 27.7 132 96.9 169 22.5 131 68.8 143 99.9 138 47.5 134 34.6 137 99.9 96 7.04 113 12.6 61 15.0 45 9.25 57 17.7 103 84.7 146 13.7 78 5.09 32 7.36 4 5.03 53 20.7 138 88.8 152 29.4 143
TriangleFlow [30]105.4 13.2 76 46.8 67 9.41 89 10.8 93 73.2 114 7.30 47 26.1 87 99.9 138 5.70 40 7.23 76 99.9 96 4.46 83 17.0 152 21.3 158 15.2 145 23.0 132 69.8 84 22.9 135 9.71 126 16.1 149 9.40 121 6.89 109 23.8 92 11.5 121
LSM_FLOW_RVC [182]106.2 34.2 147 59.8 138 25.5 148 21.5 125 99.9 173 18.4 125 37.6 113 99.9 138 29.5 119 26.8 129 99.9 96 16.7 136 13.3 97 16.4 118 11.1 104 21.3 125 67.0 78 21.3 132 5.21 39 8.80 28 3.50 17 6.38 99 15.8 78 6.00 48
Bartels [41]106.3 13.3 79 55.0 113 10.2 106 8.13 41 43.2 49 7.67 50 18.1 66 69.0 80 6.30 47 8.49 84 99.9 96 6.05 105 13.9 117 16.1 105 13.9 139 21.8 128 99.9 166 21.5 134 10.6 134 13.5 133 20.3 152 12.3 128 99.9 158 26.9 141
Fusion [6]106.4 16.3 96 53.8 106 12.5 117 7.93 38 37.7 28 7.75 51 15.6 63 41.8 34 13.2 92 13.5 106 83.1 34 7.77 118 15.4 141 18.5 147 14.2 143 33.1 142 89.0 152 24.8 140 11.8 144 14.7 141 8.27 112 11.4 126 99.9 158 13.3 125
EPMNet [131]106.8 29.4 139 62.1 144 16.4 133 36.2 139 95.3 168 29.0 142 30.2 98 47.4 55 30.9 122 18.3 119 99.9 96 11.1 128 13.2 88 15.9 89 12.0 117 14.1 61 99.9 166 10.8 31 8.19 108 16.9 154 4.18 25 6.63 106 19.4 84 6.18 51
WOLF_ROB [144]108.2 21.5 124 55.2 116 10.2 106 37.6 141 99.9 173 17.3 122 54.7 138 99.9 138 23.9 116 25.1 126 99.9 96 12.8 131 12.8 70 15.4 67 11.2 106 18.5 111 61.8 64 17.2 119 5.80 61 9.26 44 6.20 76 11.4 126 24.3 93 15.7 133
LFNet_ROB [145]112.8 28.9 138 53.4 104 17.2 137 15.3 111 46.9 63 13.7 116 31.0 101 96.3 132 21.7 113 18.4 120 92.1 85 14.4 133 13.7 114 16.6 123 12.0 117 18.8 114 90.4 155 16.7 115 6.48 78 10.9 96 5.97 72 6.22 97 99.9 158 10.3 115
LDOF [28]114.2 17.9 108 53.5 105 8.72 78 18.7 118 92.6 167 11.8 109 29.5 94 67.1 79 20.9 112 29.0 134 99.9 96 8.92 121 14.2 124 16.4 118 13.7 136 18.9 115 97.5 163 15.0 92 6.26 74 10.3 77 10.1 125 10.4 124 99.9 158 10.3 115
StereoFlow [44]114.2 48.0 159 74.6 197 41.1 160 61.0 156 99.9 173 51.6 155 71.4 144 99.9 138 63.9 145 65.6 153 99.9 96 61.2 153 16.2 147 15.9 89 22.6 153 7.22 8 77.8 103 7.39 10 3.38 3 7.35 3 1.99 5 7.18 114 99.9 158 11.4 120
CNN-flow-warp+ref [115]114.6 21.3 122 57.3 127 15.1 129 16.8 115 54.4 87 15.9 120 41.0 121 99.9 138 28.8 118 28.6 132 99.9 96 7.45 116 14.0 121 15.9 89 14.0 141 16.5 88 84.8 147 10.9 35 5.30 43 8.23 18 8.13 111 99.9 189 99.9 158 99.9 189
FlowNetS+ft+v [110]114.8 16.5 98 52.1 96 8.06 65 17.4 117 76.9 119 13.4 115 46.3 130 99.9 138 30.4 121 29.8 135 99.9 96 13.8 132 15.5 142 18.5 147 13.8 138 12.7 45 89.5 153 11.7 47 9.24 121 13.5 133 12.1 138 7.39 116 57.9 109 9.52 103
Filter Flow [19]115.8 21.6 126 57.7 129 14.4 127 24.6 129 77.5 122 18.1 123 54.3 137 80.8 83 66.3 149 52.8 146 91.0 83 46.5 147 13.6 108 16.0 100 12.3 124 17.0 96 69.6 83 14.2 83 12.0 147 16.1 149 7.39 96 6.58 105 37.5 102 8.36 86
Shiralkar [42]115.9 16.8 101 44.6 44 9.46 92 16.5 113 98.8 171 8.05 63 42.0 124 99.9 138 10.8 82 18.4 120 99.9 96 8.02 120 12.9 74 15.5 77 10.4 89 30.2 139 99.9 166 25.1 141 11.4 140 11.8 111 15.8 144 22.2 142 99.9 158 17.5 136
Learning Flow [11]118.0 13.7 82 52.8 99 7.67 52 12.9 100 87.1 166 10.0 103 40.5 118 95.0 129 13.4 94 38.1 139 99.9 96 4.74 87 17.1 154 21.7 159 12.5 128 24.2 134 99.9 166 13.5 76 7.95 106 12.7 124 6.98 88 23.9 144 99.9 158 14.9 130
StereoOF-V1MT [117]118.2 16.5 98 46.0 58 9.27 87 18.9 120 99.9 173 7.16 45 40.5 118 99.9 138 8.18 71 14.7 108 96.4 91 6.30 109 14.1 123 16.8 125 12.9 131 29.9 138 91.4 156 27.0 143 5.78 59 10.4 82 10.4 127 99.9 189 99.9 158 99.9 189
Second-order prior [8]118.3 14.3 85 46.7 64 8.79 80 15.2 109 72.5 112 10.5 106 39.2 114 99.9 138 16.6 108 17.5 117 99.9 96 6.01 104 14.4 128 17.5 133 10.8 98 38.6 151 99.9 166 24.7 139 11.3 138 12.2 116 11.2 131 9.13 121 89.5 154 15.6 132
Ad-TV-NDC [36]118.5 31.0 141 53.3 103 33.1 156 70.2 157 99.9 173 49.0 154 93.2 190 99.9 138 54.0 139 38.9 140 95.0 88 29.4 141 13.8 116 17.3 132 8.71 37 14.7 67 77.1 102 13.0 68 6.24 71 9.84 58 5.19 61 46.4 149 76.4 114 54.0 149
GraphCuts [14]120.6 21.7 127 52.8 99 10.4 111 39.2 143 99.9 173 23.1 132 39.7 117 58.0 69 49.6 137 25.6 128 74.6 24 7.31 114 13.6 108 15.9 89 13.2 134 37.8 148 97.6 164 16.2 103 9.36 123 11.4 104 11.7 135 10.3 123 99.9 158 15.0 131
IAOF2 [51]121.3 16.8 101 59.5 136 10.5 112 20.1 123 69.0 109 18.1 123 53.3 136 99.9 138 56.8 140 55.3 150 95.0 88 54.7 151 14.2 124 17.2 131 11.0 101 19.2 118 81.1 106 14.3 86 11.8 144 13.2 129 13.0 141 13.5 130 45.0 104 9.00 90
TVL1_RVC [175]123.0 31.1 142 60.0 139 29.1 152 48.7 150 99.9 173 41.9 150 91.6 189 99.9 138 71.6 152 52.2 145 99.9 96 43.9 146 14.2 124 16.9 128 12.0 117 14.2 64 99.9 166 13.2 70 4.82 17 9.21 41 3.55 18 21.1 139 99.9 158 22.6 139
2D-CLG [1]124.0 46.1 157 67.5 156 28.2 151 39.5 144 77.3 121 38.9 146 93.9 192 99.9 138 74.9 156 53.6 147 99.9 96 51.0 149 13.9 117 15.9 89 13.5 135 24.0 133 99.9 166 21.2 130 4.28 9 7.24 1 4.50 35 12.7 129 99.9 158 11.6 122
HBpMotionGpu [43]125.8 19.5 109 63.6 147 13.5 122 31.0 135 99.9 173 27.4 138 99.9 195 99.9 138 59.3 142 18.2 118 99.9 96 6.69 110 13.6 108 16.0 100 12.4 127 16.2 82 91.5 157 11.1 38 11.1 136 13.0 128 6.73 83 21.3 140 99.9 158 21.6 138
SPSA-learn [13]127.0 23.6 131 55.7 120 20.1 141 32.9 137 99.9 173 25.2 134 91.2 188 99.9 138 64.5 147 49.6 143 99.9 96 31.2 142 14.0 121 16.0 100 13.0 133 19.9 120 99.9 166 23.3 137 6.53 80 9.13 39 4.40 33 15.8 136 99.9 158 16.5 135
Modified CLG [34]128.0 27.9 136 58.6 133 23.4 145 26.5 131 71.1 110 26.2 136 93.4 191 99.9 138 73.1 154 49.2 142 99.9 96 22.6 138 15.2 140 18.1 144 13.7 136 16.3 84 99.9 166 12.8 65 6.43 77 10.2 74 11.7 135 11.3 125 99.9 158 11.3 119
IAOF [50]129.6 20.6 117 55.0 113 17.4 138 36.6 140 99.9 173 27.6 140 99.9 195 99.9 138 75.5 157 32.7 136 93.3 86 25.5 140 13.9 117 16.6 123 12.1 121 36.5 146 92.3 158 12.3 57 9.40 124 11.7 109 7.13 91 26.2 145 47.4 105 28.8 142
UnFlow [127]129.9 49.8 160 69.9 193 23.3 144 32.8 136 57.8 95 33.0 144 49.4 134 98.6 135 39.8 127 53.6 147 99.9 96 52.1 150 16.2 147 18.5 147 20.6 150 35.3 145 99.9 166 38.4 151 8.81 115 11.4 104 3.08 15 6.46 102 99.9 158 6.47 57
GroupFlow [9]131.5 27.3 135 66.8 154 21.6 143 41.1 145 99.9 173 35.1 145 71.4 144 99.9 138 61.8 143 25.1 126 99.9 96 14.4 133 14.7 133 17.9 141 11.7 113 40.6 152 97.2 162 40.6 153 5.72 56 11.7 109 6.48 79 16.4 137 53.8 107 23.0 140
Black & Anandan [4]132.9 21.5 124 51.7 94 19.8 140 38.6 142 99.9 173 25.8 135 81.3 148 99.9 138 65.5 148 50.4 144 99.9 96 31.6 143 15.5 142 19.1 154 12.8 130 22.4 130 96.8 161 18.2 124 10.1 129 12.4 121 5.16 60 13.9 132 99.9 158 12.9 124
BlockOverlap [61]134.4 17.6 107 56.5 124 13.1 119 24.0 128 66.9 106 21.5 130 67.6 142 99.9 138 49.0 135 28.2 131 99.9 96 15.2 135 17.6 157 18.3 145 32.7 158 38.1 149 81.6 107 26.6 142 14.5 153 15.5 147 67.6 162 39.8 147 82.5 116 84.7 152
Nguyen [33]134.5 27.2 134 56.2 123 18.7 139 46.7 149 99.9 173 44.1 153 97.7 194 99.9 138 74.1 155 45.7 141 99.9 96 38.0 144 16.4 150 17.7 139 21.8 152 20.8 123 99.9 166 21.2 130 7.21 93 9.42 49 5.61 67 14.6 134 99.9 158 14.2 128
2bit-BM-tele [96]134.8 20.8 121 59.0 135 16.3 132 16.4 112 68.5 108 15.3 119 43.9 128 99.9 138 15.4 105 14.6 107 99.9 96 11.2 129 15.6 144 17.5 133 16.8 148 34.9 144 99.9 166 31.1 146 18.8 159 20.4 160 30.8 155 23.7 143 99.9 158 61.9 150
Horn & Schunck [3]142.8 28.8 137 57.7 129 25.3 147 41.1 145 99.9 173 28.2 141 80.0 147 99.9 138 75.9 158 75.5 154 99.9 96 66.3 154 15.7 145 18.5 147 13.9 139 41.9 154 99.9 166 41.4 154 11.6 141 13.6 135 6.16 74 45.4 148 99.9 158 39.5 146
SILK [80]143.7 33.3 146 64.6 149 29.2 154 46.3 148 99.9 173 39.4 147 95.0 193 99.9 138 66.5 150 56.7 151 99.9 96 50.2 148 16.1 146 18.7 151 17.2 149 48.7 155 99.9 166 37.3 150 7.26 95 9.22 42 14.5 143 70.9 152 99.9 158 51.7 148
H+S_RVC [176]144.2 50.2 161 65.4 150 33.8 157 46.0 147 99.9 173 42.5 151 76.9 146 99.9 138 71.7 153 96.0 196 99.9 96 91.7 196 17.4 156 16.4 118 30.2 156 74.6 160 99.9 166 80.6 161 5.13 35 9.69 55 5.05 55 99.9 189 99.9 158 96.1 188
Heeger++ [102]144.4 33.0 145 58.3 132 23.6 146 60.7 154 99.9 173 39.4 147 59.1 139 99.9 138 30.1 120 86.5 195 99.9 96 70.5 155 14.9 136 17.1 129 12.9 131 76.5 161 99.9 166 79.8 160 7.49 100 12.9 126 6.59 80 99.9 189 99.9 158 99.9 189
AdaConv-v1 [124]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
SepConv-v1 [125]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
SuperSlomo [130]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
CtxSyn [134]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
CyclicGen [149]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
TOF-M [150]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
MPRN [151]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
DAIN [152]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
FRUCnet [153]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
OFRI [154]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
FGME [158]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
MS-PFT [159]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
MEMC-Net+ [160]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
ADC [161]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
DSepConv [162]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
MAF-net [163]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
STAR-Net [164]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
AdaCoF [165]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
TC-GAN [166]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
FeFlow [167]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
DAI [168]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
SoftSplat [169]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
STSR [170]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
BMBC [171]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
GDCN [172]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
EDSC [173]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
MV_VFI [183]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
DistillNet [184]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
SepConv++ [185]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
EAFI [186]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
FLAVR [188]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
SoftsplatAug [190]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
ProBoost-Net [191]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
IDIAL [192]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
IFRNet [193]146.4 66.7 164 69.5 158 54.6 162 80.0 159 82.0 126 79.5 160 81.7 149 82.0 84 80.9 161 82.3 157 83.1 34 82.6 159 85.8 162 85.9 162 85.2 162 88.2 163 83.0 109 82.9 163 75.7 165 67.1 165 77.8 165 85.1 154 86.4 117 84.8 153
TI-DOFE [24]148.8 40.2 152 61.2 142 38.6 159 60.2 153 99.9 173 53.7 156 90.4 187 99.9 138 78.2 160 83.9 193 99.9 96 82.8 194 16.6 151 19.4 156 16.4 147 38.1 149 99.9 166 38.7 152 8.51 112 10.3 77 7.75 103 56.3 150 99.9 158 49.7 147
HCIC-L [97]148.8 44.4 156 66.7 153 29.1 152 99.9 196 99.9 173 99.9 196 47.8 131 99.9 138 44.6 131 58.5 152 99.9 96 55.7 152 20.3 159 20.6 157 24.0 155 33.9 143 86.5 150 33.6 147 38.0 163 49.5 163 36.0 156 13.7 131 25.8 95 13.7 126
PGAM+LK [55]154.2 43.6 154 67.1 155 43.7 161 73.8 158 99.9 173 77.5 158 61.9 140 82.9 119 63.9 145 76.6 155 99.9 96 72.5 156 17.1 154 17.1 129 31.6 157 66.2 158 99.9 166 64.8 158 18.9 160 20.3 159 22.1 153 99.9 189 99.9 158 99.9 189
Adaptive flow [45]155.5 34.4 150 59.7 137 29.7 155 84.5 194 99.9 173 77.7 159 87.6 184 99.9 138 92.8 197 54.9 149 99.9 96 39.3 145 20.6 160 23.8 161 20.9 151 37.5 147 96.7 159 29.2 144 35.2 162 30.1 162 58.6 161 38.1 146 99.9 158 33.0 144
FFV1MT [104]156.2 36.4 151 71.0 195 25.8 149 50.3 152 99.9 173 39.7 149 65.6 141 99.1 136 41.6 130 99.9 197 99.9 96 97.4 198 20.2 158 19.3 155 33.1 160 62.5 157 99.9 166 71.1 159 9.04 118 14.8 143 11.2 131 99.9 189 99.9 158 99.9 189
SLK [47]158.0 53.1 162 66.3 152 59.5 198 60.9 155 98.1 170 58.6 157 89.7 185 99.9 138 67.7 151 99.9 197 99.9 96 95.1 197 17.0 152 18.8 152 23.5 154 56.6 156 99.9 166 51.2 156 8.43 111 11.9 112 12.2 139 99.9 189 99.9 158 99.9 189
FOLKI [16]165.6 43.8 155 74.4 196 38.0 158 99.9 196 99.9 173 99.9 196 89.7 185 99.9 138 76.2 159 85.0 194 99.9 96 81.0 158 23.2 161 22.5 160 38.8 161 66.2 158 99.9 166 62.7 157 17.0 158 17.6 155 42.6 159 99.9 189 99.9 158 99.9 189
Periodicity [79]170.0 54.4 163 84.3 198 26.5 150 99.9 196 99.9 173 99.9 196 99.9 195 99.9 138 99.9 198 81.4 156 99.9 96 76.3 157 99.9 198 99.9 197 99.9 198 99.9 198 99.9 166 99.9 198 6.06 67 14.8 143 70.6 164 99.9 189 99.9 158 99.9 189
Pyramid LK [2]173.2 47.7 158 60.7 141 56.1 197 88.6 195 99.9 173 91.9 195 99.9 195 99.9 138 89.4 196 83.0 192 99.9 96 83.2 195 98.4 197 99.9 197 87.9 197 87.4 162 99.9 166 81.4 162 16.9 157 16.6 151 57.0 160 99.9 189 99.9 158 99.9 189
AVG_FLOW_ROB [137]181.7 99.9 199 99.9 199 99.9 199 99.9 196 99.9 173 99.9 196 99.9 195 99.9 138 99.9 198 99.9 197 99.9 96 99.9 199 99.9 198 99.9 197 99.9 198 99.9 198 99.9 166 99.9 198 48.6 164 52.0 164 38.2 157 99.9 189 99.9 158 99.9 189
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.
* 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.