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        
Average
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
NNF-Local [87]5.7 2.69 3 7.56 4 1.98 3 1.97 4 7.01 4 1.59 4 2.18 2 5.36 3 1.53 4 1.87 4 9.14 8 1.06 4 2.28 2 2.94 1 1.57 2 2.39 5 6.78 2 2.15 9 2.00 25 3.36 18 1.62 20 0.99 1 2.16 3 0.57 2
NN-field [71]10.8 2.89 8 8.13 16 2.11 5 2.10 6 7.15 9 1.77 15 2.27 4 5.59 5 1.61 8 1.58 1 8.52 7 0.79 1 2.35 4 3.05 5 1.60 3 1.89 1 5.20 1 1.37 1 2.43 56 3.70 54 1.95 43 1.01 2 2.25 4 0.53 1
OFLAF [77]14.0 3.04 15 7.80 10 2.40 14 2.14 7 7.02 5 1.72 9 2.25 3 5.32 2 1.56 5 2.62 21 13.7 28 1.37 23 2.35 4 3.13 6 1.62 4 2.98 23 7.73 7 2.57 22 2.08 31 3.27 12 2.05 48 1.33 13 2.43 7 1.40 17
PMMST [114]15.2 3.42 43 7.60 5 2.65 32 2.32 11 6.39 1 2.20 34 2.63 11 6.08 8 2.03 28 2.06 7 6.07 3 1.44 31 2.60 10 3.27 8 1.91 10 2.56 7 6.78 2 2.09 5 2.06 27 3.53 38 1.63 21 1.27 10 2.29 5 1.02 7
nLayers [57]17.5 2.80 6 7.42 3 2.20 8 2.71 31 7.24 10 2.55 61 2.61 9 6.24 9 2.45 54 2.30 13 12.7 15 1.16 8 2.30 3 3.02 3 1.70 5 2.62 11 6.95 4 2.09 5 2.29 48 3.46 27 1.89 40 1.38 15 3.06 18 1.29 15
MDP-Flow2 [68]20.2 3.23 31 7.93 13 2.60 24 1.92 2 6.64 2 1.52 1 2.46 7 5.91 7 1.56 5 3.05 47 15.8 60 1.51 41 2.77 24 3.50 18 2.16 28 2.86 19 8.58 18 2.70 33 2.00 25 3.50 35 1.59 18 1.28 11 2.67 12 0.89 4
ComponentFusion [96]20.5 2.78 5 8.20 17 2.05 4 2.04 5 7.31 11 1.66 8 2.55 8 6.78 13 1.61 8 2.24 12 13.1 18 1.01 3 2.71 21 3.56 20 2.10 23 3.55 53 12.4 61 3.22 60 2.19 42 3.60 47 1.54 17 1.32 12 2.91 14 1.13 9
TC/T-Flow [76]22.9 2.69 3 7.75 9 1.87 2 2.76 34 10.2 48 1.73 10 3.33 25 9.01 31 1.49 2 2.86 37 16.7 70 1.21 10 2.60 10 3.49 17 1.90 9 2.21 2 7.65 5 2.04 4 1.84 13 3.23 9 3.14 100 2.03 39 4.53 40 1.49 21
FC-2Layers-FF [74]26.8 3.02 14 7.87 12 2.61 25 2.72 32 9.35 36 2.29 42 2.36 5 5.47 4 2.15 35 2.48 14 12.6 14 1.28 14 2.49 7 3.19 7 2.03 17 3.39 42 8.92 20 2.83 44 2.83 80 3.92 70 2.80 79 1.25 8 2.57 11 1.20 12
WLIF-Flow [93]27.9 2.96 10 7.67 6 2.40 14 2.41 16 7.70 15 2.10 28 2.98 17 7.63 19 1.97 27 2.71 29 13.5 24 1.33 17 3.01 45 4.00 51 2.40 47 3.03 26 8.32 12 2.44 17 2.09 33 3.36 18 2.04 47 2.26 47 4.97 49 2.59 56
Layers++ [37]28.3 3.11 18 8.22 20 2.79 44 2.43 19 7.02 5 2.24 37 2.43 6 5.77 6 2.18 38 2.13 9 9.71 10 1.15 7 2.35 4 3.02 3 1.96 11 3.81 61 11.4 46 3.22 60 2.74 75 4.01 75 2.35 62 1.45 16 3.05 17 1.79 31
HAST [109]29.0 2.58 1 7.12 1 1.81 1 2.41 16 7.05 7 2.10 28 1.83 1 4.19 1 1.17 1 2.84 36 15.5 54 1.08 5 2.23 1 2.97 2 1.40 1 3.72 58 10.0 33 3.92 85 3.40 103 4.90 109 5.66 132 1.20 7 2.09 1 1.24 13
AGIF+OF [85]30.0 3.06 16 8.20 17 2.55 22 3.17 60 10.6 55 2.46 55 3.46 30 8.97 30 2.24 41 2.61 19 13.7 28 1.33 17 2.63 15 3.46 15 2.11 24 2.88 21 8.34 14 2.35 13 2.10 35 3.56 42 2.09 50 1.80 30 3.68 30 2.24 42
Efficient-NL [60]30.2 2.99 13 8.23 21 2.28 9 2.72 32 8.95 32 2.25 40 3.81 40 9.87 37 2.07 32 2.77 33 14.3 37 1.46 36 2.61 12 3.48 16 1.96 11 3.31 38 8.33 13 2.59 24 2.60 66 3.75 55 2.54 69 1.60 22 3.02 15 1.66 23
LME [70]30.3 3.15 23 8.04 15 2.31 11 1.95 3 6.65 3 1.59 4 4.03 46 9.31 32 4.57 98 2.69 27 13.6 26 1.42 28 2.85 31 3.61 23 2.42 49 3.47 49 12.8 68 3.17 56 2.12 37 3.53 38 1.73 23 1.34 14 2.75 13 1.18 11
FESL [72]30.3 2.96 10 7.70 7 2.54 20 3.26 71 10.4 52 2.56 62 3.25 23 8.39 23 2.17 36 2.56 16 13.2 19 1.31 16 2.57 9 3.40 11 2.12 26 2.60 9 7.65 5 2.30 11 2.64 71 4.22 84 2.47 66 1.75 28 3.49 27 1.71 26
ALD-Flow [66]30.6 2.82 7 7.86 11 2.16 6 2.84 41 10.1 45 1.86 18 3.73 38 10.4 41 1.67 12 3.10 49 16.8 71 1.28 14 2.69 20 3.60 22 1.85 8 2.79 15 11.3 45 2.32 12 2.07 29 3.25 11 3.10 97 2.03 39 5.11 50 1.94 34
RNLOD-Flow [121]31.7 2.66 2 7.33 2 2.17 7 2.53 27 9.46 38 1.86 18 3.94 44 10.7 47 1.95 24 2.50 15 13.5 24 1.21 10 2.68 18 3.62 25 2.05 19 2.99 24 8.59 19 2.75 37 3.00 90 4.54 96 3.25 105 1.48 18 3.24 21 1.76 30
IROF++ [58]31.9 3.17 25 8.69 30 2.61 25 2.79 36 9.61 39 2.33 43 3.43 27 8.86 27 2.38 48 2.87 39 14.8 42 1.52 43 2.74 22 3.57 21 2.19 30 3.20 34 9.70 30 2.71 34 1.96 23 3.45 26 1.22 8 1.80 30 4.06 32 2.50 52
NNF-EAC [103]32.4 3.31 35 8.21 19 2.68 34 2.19 9 7.49 13 1.76 13 2.73 13 6.62 12 1.70 14 3.18 54 15.8 60 1.64 53 2.87 34 3.66 28 2.24 32 3.02 25 8.07 10 2.59 24 2.19 42 3.48 31 1.74 24 2.85 64 6.52 66 3.12 68
ProFlow_ROB [147]32.4 3.29 32 9.91 56 2.35 13 2.50 25 10.0 44 1.83 16 4.04 47 11.6 55 1.96 26 2.86 37 15.0 45 1.22 12 2.87 34 3.89 42 1.97 14 2.60 9 10.5 36 2.20 10 1.53 4 3.54 40 1.53 16 2.50 53 6.37 65 2.33 47
PH-Flow [101]32.8 3.19 28 8.87 35 2.71 35 2.84 41 9.33 35 2.37 45 2.85 14 7.20 15 2.36 45 2.92 42 15.4 51 1.51 41 2.63 15 3.42 12 2.04 18 3.03 26 8.52 17 2.49 19 2.69 73 3.60 47 3.13 99 1.25 8 2.53 9 1.34 16
Classic+CPF [83]33.3 3.14 21 8.60 27 2.63 29 3.03 57 10.6 55 2.33 43 3.66 34 9.58 33 2.20 39 2.61 19 14.1 33 1.34 20 2.68 18 3.53 19 2.21 31 2.85 18 7.95 9 2.38 14 2.44 58 3.49 33 2.90 90 1.67 26 3.40 24 2.43 50
Sparse-NonSparse [56]35.0 3.14 21 8.75 32 2.76 42 3.02 55 10.6 55 2.43 50 3.45 29 8.96 28 2.36 45 2.66 24 13.7 28 1.42 28 2.85 31 3.75 36 2.33 37 3.28 37 9.40 26 2.73 35 2.42 55 3.31 14 2.69 74 1.47 17 3.07 19 1.66 23
TC-Flow [46]36.3 2.91 9 8.00 14 2.34 12 2.18 8 8.77 27 1.52 1 3.84 42 10.7 47 1.49 2 3.13 50 16.6 69 1.46 36 2.78 25 3.73 35 1.96 11 3.08 29 11.4 46 2.66 28 1.94 21 3.43 23 3.20 104 3.06 69 7.04 71 4.08 93
3DFlow [135]36.7 3.44 44 8.63 29 2.46 16 2.43 19 8.59 26 1.75 12 3.71 36 9.93 39 1.64 10 1.61 3 4.58 1 1.23 13 2.86 33 3.72 33 2.16 28 4.52 85 11.6 52 4.20 92 3.16 98 4.02 76 4.44 124 1.13 5 2.14 2 0.89 4
LSM [39]37.5 3.12 19 8.62 28 2.75 41 3.00 53 10.5 54 2.44 52 3.43 27 8.85 26 2.35 44 2.66 24 13.6 26 1.44 31 2.82 27 3.68 29 2.36 40 3.38 41 9.41 27 2.81 42 2.69 73 3.52 36 2.84 83 1.59 21 3.38 23 1.80 32
Ramp [62]38.3 3.18 27 8.83 34 2.73 38 2.89 46 10.1 45 2.44 52 3.27 24 8.43 24 2.38 48 2.74 31 14.2 34 1.46 36 2.82 27 3.69 32 2.29 35 3.37 40 9.31 24 2.93 48 2.62 69 3.38 21 3.19 103 1.54 20 3.21 20 2.24 42
SVFilterOh [111]38.4 3.63 50 8.82 33 2.86 46 2.60 29 8.06 18 2.05 27 2.95 15 7.09 14 2.03 28 2.80 35 13.8 31 1.41 27 2.63 15 3.42 12 1.75 7 3.49 50 10.3 35 3.23 63 3.63 112 5.75 131 4.47 125 1.09 4 2.45 8 0.92 6
Correlation Flow [75]38.4 3.38 41 8.40 23 2.64 30 2.23 10 7.54 14 1.56 3 5.14 69 13.1 68 1.60 7 2.09 8 8.15 6 1.35 22 3.12 53 4.09 58 2.34 38 4.01 72 11.5 50 4.00 87 2.59 65 3.61 49 3.00 95 1.49 19 3.04 16 1.42 19
ProbFlowFields [128]39.2 4.18 70 12.4 85 3.40 76 2.43 19 8.16 20 2.19 33 3.65 33 9.72 35 2.86 69 2.22 10 9.42 9 1.42 28 3.01 45 3.96 48 2.36 40 2.73 14 10.9 38 2.51 20 1.89 19 3.39 22 1.82 29 2.59 56 6.21 63 2.75 59
PMF [73]39.3 3.61 48 9.07 38 2.62 27 2.40 14 8.05 17 1.83 16 2.61 9 6.27 10 1.65 11 3.35 64 15.4 51 1.58 48 2.54 8 3.27 8 1.71 6 3.59 54 11.1 43 3.46 69 4.07 122 6.18 137 4.02 120 1.06 3 2.38 6 1.25 14
COFM [59]39.5 3.17 25 9.90 55 2.46 16 2.41 16 8.34 23 1.92 21 3.77 39 10.5 42 2.54 57 2.71 29 14.9 44 1.19 9 3.08 50 3.92 46 3.25 92 3.83 64 10.9 38 3.15 55 2.20 45 3.35 16 2.91 92 1.62 25 2.56 10 2.09 38
FMOF [94]40.7 3.12 19 8.23 21 2.73 38 3.25 68 10.7 62 2.52 59 3.01 18 7.61 18 2.20 39 2.56 16 13.4 22 1.33 17 2.75 23 3.61 23 2.24 32 3.66 56 8.50 16 2.78 40 2.62 69 3.84 63 3.27 107 2.66 61 5.69 54 1.95 36
JOF [141]40.9 3.08 17 8.56 26 2.51 19 3.27 72 10.2 48 2.81 82 3.02 19 7.55 17 2.42 52 2.64 22 14.2 34 1.34 20 2.62 13 3.42 12 2.08 20 3.26 35 8.96 21 2.56 21 3.12 97 4.26 85 4.09 122 2.11 45 4.58 42 2.18 40
OAR-Flow [125]41.6 3.37 39 9.87 54 2.67 33 4.22 93 12.8 89 2.87 84 4.95 65 13.4 71 2.66 61 3.23 56 16.4 68 1.37 23 2.83 29 3.82 39 1.97 14 2.49 6 10.9 38 1.87 3 1.52 3 2.82 1 1.86 34 1.85 33 4.35 37 1.68 25
Classic+NL [31]42.0 3.20 30 8.72 31 2.81 45 3.02 55 10.6 55 2.44 52 3.46 30 8.84 25 2.38 48 2.78 34 14.3 37 1.46 36 2.83 29 3.68 29 2.31 36 3.40 43 9.09 23 2.76 39 2.87 82 3.82 62 2.86 87 1.67 26 3.53 28 2.26 46
TV-L1-MCT [64]43.1 3.16 24 8.48 25 2.71 35 3.28 73 10.8 66 2.60 70 3.95 45 10.5 42 2.38 48 2.69 27 13.9 32 1.45 35 2.94 41 3.79 37 2.63 69 3.50 51 9.75 31 3.06 52 2.08 31 3.35 16 2.29 59 1.95 36 3.89 31 2.71 58
PWC-Net_ROB [148]45.8 4.86 102 12.4 85 3.56 85 3.14 59 10.3 51 2.60 70 4.38 55 11.6 55 3.18 76 2.56 16 10.6 12 1.52 43 3.25 69 4.18 62 2.46 51 3.10 31 10.6 37 2.75 37 1.44 2 3.56 42 1.01 2 1.60 22 3.41 25 1.14 10
IIOF-NLDP [131]46.5 3.65 51 9.81 53 2.56 23 2.79 36 9.36 37 2.00 23 4.28 53 11.3 53 1.69 13 2.02 6 7.52 5 1.38 26 3.36 76 4.52 91 2.40 47 3.82 62 11.2 44 3.67 78 2.07 29 3.79 59 1.88 38 2.91 66 5.30 53 4.17 94
SimpleFlow [49]47.4 3.35 36 9.20 41 2.98 53 3.18 63 10.7 62 2.71 76 5.06 67 12.6 66 2.70 63 2.95 44 15.1 47 1.58 48 2.91 39 3.79 37 2.47 52 3.59 54 9.49 28 2.99 50 2.39 53 3.46 27 2.24 58 1.60 22 3.56 29 1.57 22
CostFilter [40]47.6 3.84 55 9.64 49 3.06 55 2.55 28 8.09 19 2.03 25 2.69 12 6.47 11 1.88 20 3.66 75 16.8 71 1.88 65 2.62 13 3.34 10 1.99 16 4.05 73 11.0 42 3.65 77 4.16 124 7.18 144 4.66 127 1.16 6 3.36 22 0.87 3
2DHMM-SAS [92]49.8 3.19 28 8.89 36 2.71 35 3.20 66 11.5 76 2.38 46 5.19 70 12.2 62 2.73 65 2.92 42 15.2 48 1.53 45 2.79 26 3.65 27 2.27 34 3.45 47 9.34 25 2.78 40 2.66 72 3.56 42 3.07 96 2.34 50 5.12 51 2.97 66
S2D-Matching [84]50.8 3.36 37 9.66 50 2.86 46 3.19 65 11.1 70 2.46 55 4.86 64 12.9 67 2.47 55 2.67 26 13.2 19 1.44 31 2.87 34 3.72 33 2.38 43 3.45 47 9.76 32 2.95 49 3.05 91 3.79 59 3.30 109 1.95 36 4.16 35 3.00 67
FlowFields+ [130]51.0 4.57 88 13.7 99 3.35 68 2.94 51 10.1 45 2.58 66 4.05 48 10.6 44 3.26 79 2.90 41 13.2 19 1.81 63 3.18 57 4.20 66 2.54 58 2.68 13 11.4 46 2.40 16 1.84 13 3.62 50 1.77 25 2.48 52 5.86 56 2.77 60
MLDP_OF [89]51.0 4.13 66 10.3 63 3.60 86 2.34 12 7.70 15 1.88 20 4.23 52 10.9 50 1.87 19 2.74 31 14.6 41 1.37 23 3.10 51 3.91 45 2.48 56 3.40 43 9.00 22 3.79 82 3.46 105 4.20 82 5.55 131 2.31 48 4.64 44 1.98 37
MDP-Flow [26]51.6 3.48 46 9.46 46 3.10 57 2.45 22 7.36 12 2.41 47 3.21 22 8.31 22 2.78 67 3.18 54 17.8 78 1.70 58 3.03 47 3.87 40 2.60 65 3.43 45 12.6 65 2.81 42 2.19 42 3.88 67 1.60 19 4.13 90 9.96 97 3.86 88
AggregFlow [97]51.7 4.25 76 11.9 82 3.26 60 4.46 99 13.7 100 3.43 95 4.76 62 12.4 63 3.93 95 3.28 59 15.6 56 1.68 55 2.89 37 3.89 42 2.08 20 2.32 3 7.75 8 2.14 7 2.06 27 3.77 57 1.48 14 2.07 43 4.11 33 2.36 48
IROF-TV [53]52.0 3.40 42 9.29 43 2.95 52 2.99 52 11.1 70 2.53 60 3.81 40 9.81 36 2.44 53 3.25 58 16.9 73 1.78 62 3.27 73 4.10 59 2.93 83 4.47 82 16.0 100 3.53 71 1.70 6 3.21 7 1.12 5 1.91 35 4.75 46 2.19 41
CombBMOF [113]53.5 3.94 59 10.6 67 2.74 40 2.80 38 8.55 25 2.16 31 3.10 21 7.99 21 1.76 15 2.99 45 13.4 22 1.95 69 3.04 48 3.89 42 2.49 57 5.64 110 12.3 59 6.74 125 3.54 108 5.16 117 2.81 80 1.85 33 4.60 43 1.10 8
S2F-IF [123]53.5 4.51 86 13.6 98 3.31 64 2.90 47 10.4 52 2.48 58 4.07 50 10.8 49 3.15 74 3.31 60 15.7 59 1.90 66 3.17 55 4.19 64 2.55 61 2.81 17 11.6 52 2.60 26 1.86 16 3.67 52 1.87 35 2.11 45 4.64 44 2.54 55
WRT [151]53.7 3.74 53 9.34 44 2.48 18 3.37 78 10.2 48 2.58 66 6.80 94 15.3 86 2.24 41 1.58 1 5.01 2 1.09 6 2.89 37 3.68 29 2.35 39 5.52 108 12.0 56 4.21 94 2.30 49 3.85 64 2.34 61 3.20 72 4.91 47 4.21 95
FlowFields [110]56.2 4.57 88 13.7 99 3.38 71 3.01 54 10.6 55 2.59 68 4.19 51 11.1 51 3.30 80 3.17 53 15.0 45 1.96 70 3.21 64 4.24 73 2.61 68 2.91 22 12.4 61 2.66 28 1.84 13 3.46 27 1.84 32 2.50 53 6.15 61 2.79 61
Sparse Occlusion [54]57.5 3.62 49 9.12 39 2.90 48 2.92 49 9.08 33 2.56 62 4.49 59 11.8 60 2.11 34 3.14 51 15.8 60 1.57 47 3.26 70 4.22 69 2.36 40 3.52 52 10.9 38 2.66 28 5.10 140 6.32 138 3.15 101 2.02 38 4.92 48 1.71 26
NL-TV-NCC [25]57.7 3.89 57 9.16 40 2.98 53 2.87 45 9.69 40 1.99 22 4.44 58 11.6 55 1.76 15 2.64 22 11.8 13 1.48 40 3.49 88 4.60 98 2.47 52 4.67 92 13.5 73 4.26 98 2.83 80 4.57 98 2.84 83 2.62 59 6.00 60 2.25 44
EPPM w/o HM [88]58.2 4.25 76 11.1 71 3.13 58 2.36 13 8.35 24 1.76 13 3.72 37 10.2 40 1.81 17 3.24 57 14.5 40 1.94 68 3.16 54 3.94 47 2.82 78 4.78 96 12.9 69 4.32 99 3.64 114 4.54 96 5.73 133 1.76 29 4.11 33 1.94 34
OFH [38]58.3 3.90 58 9.77 52 3.62 89 2.84 41 11.0 69 2.04 26 5.52 77 14.4 79 1.89 21 3.52 67 20.5 99 1.60 51 3.18 57 4.06 56 2.82 78 3.86 65 14.1 82 3.59 73 1.77 9 3.62 50 1.81 28 2.64 60 7.08 74 2.15 39
PGM-C [120]58.6 4.62 93 14.0 104 3.39 73 3.29 75 12.3 82 2.70 75 4.39 57 11.7 58 3.43 83 4.00 83 19.8 88 2.15 75 3.19 59 4.23 70 2.54 58 2.79 15 11.9 55 2.45 18 1.83 11 3.21 7 1.83 30 2.31 48 5.87 57 1.82 33
Occlusion-TV-L1 [63]60.5 3.59 47 9.61 47 2.64 30 2.93 50 10.6 55 2.41 47 6.16 85 15.2 84 2.70 63 3.32 62 17.0 74 1.68 55 3.38 78 4.44 84 2.82 78 3.10 31 13.2 72 2.68 31 2.17 38 3.52 36 1.46 12 4.63 103 11.1 112 3.53 77
Complementary OF [21]61.1 4.44 82 11.2 74 4.04 98 2.51 26 9.77 42 1.74 11 3.93 43 10.6 44 2.04 30 3.87 79 18.8 80 2.19 79 3.17 55 4.00 51 2.92 82 4.64 90 13.8 79 3.64 76 2.17 38 3.36 18 2.51 67 3.08 70 7.04 71 3.65 81
Adaptive [20]62.8 3.29 32 9.43 45 2.28 9 3.10 58 11.4 73 2.46 55 6.58 90 15.7 91 2.52 56 3.14 51 15.6 56 1.56 46 3.67 99 4.46 86 3.48 102 3.32 39 13.0 71 2.38 14 2.76 78 4.39 90 1.93 42 3.58 78 8.18 83 2.88 63
Aniso-Texture [82]63.8 2.96 10 7.72 8 2.54 20 2.48 24 8.26 22 2.24 37 6.48 88 15.9 96 2.63 59 1.96 5 10.1 11 0.98 2 3.26 70 4.21 67 2.60 65 5.74 113 16.9 109 5.61 115 4.47 132 5.88 134 3.33 110 3.51 77 7.12 75 3.68 83
ACK-Prior [27]64.1 4.19 72 9.27 42 3.60 86 2.40 14 8.21 21 1.65 7 3.40 26 8.96 28 1.84 18 2.87 39 14.4 39 1.44 31 3.36 76 4.15 60 3.07 87 6.35 121 16.1 102 4.90 109 4.21 127 4.80 103 6.03 135 3.29 74 5.99 59 2.82 62
CPM-Flow [116]64.9 4.63 94 14.1 107 3.39 73 3.33 76 12.5 86 2.73 77 4.37 54 11.7 58 3.43 83 4.00 83 19.9 91 2.14 74 3.19 59 4.23 70 2.54 58 3.08 29 12.0 56 2.88 46 1.87 17 3.44 24 1.84 32 2.91 66 7.48 80 2.91 65
DPOF [18]65.2 4.67 97 12.6 91 3.30 62 3.57 83 10.6 55 3.12 91 3.09 20 7.50 16 2.32 43 3.06 48 14.8 42 1.82 64 3.21 64 4.18 62 2.79 77 4.47 82 12.5 63 3.33 64 4.09 123 3.92 70 6.96 137 2.09 44 4.39 38 1.74 28
EpicFlow [102]65.4 4.61 92 14.0 104 3.39 73 3.33 76 12.5 86 2.74 78 5.37 73 14.8 82 3.46 86 3.94 82 19.2 84 2.13 73 3.20 61 4.23 70 2.58 64 2.87 20 12.2 58 2.64 27 1.83 11 3.28 13 1.83 30 3.21 73 7.12 75 3.61 78
DeepFlow2 [108]66.8 4.04 63 11.2 74 3.38 71 3.80 86 12.4 85 2.86 83 5.12 68 13.4 71 3.00 70 4.17 89 20.1 93 2.18 78 2.96 42 3.97 49 2.08 20 3.06 28 12.6 65 2.69 32 2.17 38 3.24 10 2.71 75 4.74 105 10.4 105 4.38 102
ROF-ND [107]67.3 4.12 64 10.0 57 3.37 70 2.78 35 8.82 29 2.12 30 4.61 61 11.9 61 2.09 33 2.23 11 6.56 4 1.69 57 3.60 95 4.75 108 2.85 81 4.92 99 13.6 76 3.75 80 4.59 134 5.18 118 4.10 123 2.67 62 5.19 52 3.46 76
TCOF [69]67.6 4.17 69 10.4 65 3.71 92 3.17 60 10.7 62 2.59 68 6.58 90 15.7 91 3.82 93 3.69 77 16.1 65 2.37 88 3.78 103 4.95 120 2.47 52 2.59 8 8.47 15 2.58 23 3.66 116 4.83 104 2.67 73 1.83 32 4.20 36 1.46 20
HBM-GC [105]68.8 5.25 105 10.5 66 4.34 105 3.17 60 8.78 28 2.94 87 4.38 55 10.6 44 2.68 62 3.59 71 12.8 16 2.47 91 2.96 42 3.64 26 2.64 70 3.96 71 8.26 11 3.56 72 4.40 130 5.92 135 3.62 114 2.55 55 6.34 64 3.29 71
RFlow [90]68.9 3.82 54 10.0 57 3.44 79 2.61 30 9.73 41 2.02 24 5.66 79 14.5 80 2.05 31 3.93 81 23.1 113 1.90 66 3.24 66 4.19 64 2.66 72 4.12 76 15.2 96 3.34 66 2.61 67 3.56 42 2.65 72 4.48 98 10.5 108 3.93 92
Steered-L1 [118]70.3 3.30 34 8.44 24 2.91 49 1.89 1 7.14 8 1.60 6 3.61 32 9.91 38 1.89 21 3.45 65 19.4 87 1.64 53 3.42 81 4.30 76 3.39 95 5.18 103 14.5 85 4.37 102 5.09 139 5.05 113 10.1 141 5.56 113 10.2 103 6.24 119
DMF_ROB [140]71.2 4.37 79 12.3 84 3.62 89 3.46 81 12.9 91 2.60 70 5.98 82 15.8 93 3.23 78 4.05 85 19.8 88 2.15 75 3.10 51 4.06 56 2.57 63 3.79 60 14.3 83 3.13 54 1.88 18 3.12 5 1.99 46 4.34 92 10.0 98 3.87 89
SRR-TVOF-NL [91]71.3 4.47 84 10.9 69 3.32 66 4.04 90 13.2 95 2.90 85 4.81 63 12.5 64 3.15 74 3.33 63 15.3 49 1.61 52 3.24 66 4.03 55 2.70 74 3.94 69 11.8 54 3.33 64 4.16 124 5.21 121 3.44 113 2.06 42 3.48 26 2.42 49
ComplOF-FED-GPU [35]71.6 4.28 78 11.3 76 3.70 91 3.25 68 13.0 92 2.16 31 4.06 49 11.2 52 1.95 24 3.91 80 19.2 84 2.01 71 3.20 61 4.15 60 2.64 70 4.61 88 16.1 102 3.90 84 2.98 88 3.77 57 3.69 115 2.85 64 7.44 79 2.53 54
FF++_ROB [146]72.9 4.84 101 14.8 114 3.46 80 3.18 63 11.4 73 2.69 74 5.30 72 14.1 76 3.73 92 3.31 60 14.2 34 2.20 80 3.26 70 4.29 75 2.72 75 4.58 87 12.7 67 3.70 79 1.91 20 3.46 27 2.19 57 3.65 80 7.31 77 5.97 116
TF+OM [100]74.8 3.97 60 10.2 60 2.94 51 2.91 48 9.12 34 2.57 65 5.22 71 11.5 54 6.92 107 3.59 71 16.1 65 2.28 85 3.20 61 3.97 49 3.11 88 4.70 94 14.5 85 4.32 99 3.06 93 4.84 106 2.71 75 3.93 85 8.79 88 4.32 100
Aniso. Huber-L1 [22]75.3 3.71 52 10.1 59 3.08 56 4.36 98 13.0 92 3.77 99 6.92 95 15.3 86 3.60 89 3.54 68 15.9 63 2.04 72 3.38 78 4.45 85 2.47 52 3.88 66 12.9 69 2.74 36 3.37 102 4.36 88 2.85 86 3.16 71 7.52 81 2.90 64
DeepFlow [86]76.5 4.49 85 11.7 79 4.14 100 4.26 94 12.8 89 3.36 93 5.96 81 14.2 78 5.10 99 4.89 103 23.1 113 2.67 94 2.98 44 4.00 51 2.11 24 3.26 35 13.5 73 2.84 45 2.09 33 3.10 3 2.77 77 5.83 115 11.4 114 5.45 114
Classic++ [32]77.3 3.37 39 9.67 51 2.91 49 3.28 73 12.1 79 2.61 73 5.46 76 14.1 76 3.00 70 3.63 73 20.2 96 1.70 58 3.24 66 4.34 78 2.60 65 4.65 91 16.0 100 3.60 74 3.09 94 3.94 73 3.28 108 4.64 104 10.4 105 3.71 84
TV-L1-improved [17]78.3 3.36 37 9.63 48 2.62 27 2.82 39 10.7 62 2.23 35 6.50 89 15.8 93 2.73 65 3.80 78 21.3 104 1.76 61 3.34 75 4.38 82 2.39 44 5.97 115 18.1 117 5.67 116 3.57 110 4.92 111 3.43 112 4.01 88 9.84 96 3.44 75
LocallyOriented [52]80.4 4.54 87 12.8 93 3.27 61 4.73 104 14.8 107 3.73 98 7.77 102 18.3 110 3.44 85 3.56 69 15.6 56 2.22 81 3.46 85 4.47 87 2.69 73 3.15 33 10.2 34 3.19 58 2.61 67 4.20 82 2.52 68 4.39 95 8.52 85 5.23 110
SIOF [67]80.7 4.23 74 10.2 60 3.31 64 3.97 88 14.5 105 2.97 88 7.81 104 16.4 99 7.48 109 4.82 99 20.1 93 2.96 97 3.54 91 4.49 88 3.12 89 4.31 78 13.5 73 4.13 90 2.36 52 3.59 46 1.68 22 3.46 76 7.39 78 3.37 73
LiteFlowNet [143]82.1 6.29 112 16.5 120 4.45 107 3.68 84 10.8 66 3.13 92 5.43 74 13.7 74 3.60 89 3.57 70 12.8 16 2.25 84 3.85 110 4.78 110 3.61 105 4.37 80 12.5 63 3.63 75 2.55 62 4.51 95 1.52 15 4.05 89 7.05 73 5.16 106
Brox et al. [5]83.4 4.44 82 12.4 85 4.22 103 3.72 85 13.5 99 3.06 89 4.97 66 13.3 70 3.11 72 4.58 95 22.0 107 2.37 88 3.79 105 4.60 98 4.33 126 3.91 68 17.0 110 3.45 68 2.22 46 3.79 59 1.19 6 4.62 102 10.0 98 3.38 74
TriangleFlow [30]83.7 4.12 64 10.6 67 3.47 81 3.47 82 13.1 94 2.41 47 6.00 83 15.2 84 2.17 36 2.99 45 16.0 64 1.58 48 4.46 130 5.79 135 4.15 122 5.42 107 13.9 81 5.24 110 3.10 96 5.47 127 2.90 90 3.02 68 6.82 68 3.64 80
CRTflow [80]84.1 4.18 70 11.8 81 3.20 59 3.22 67 10.8 66 2.43 50 6.20 86 15.5 89 2.63 59 4.21 90 22.0 107 2.24 82 3.32 74 4.34 78 2.44 50 7.43 128 19.3 123 8.15 131 2.55 62 4.09 78 2.59 71 4.60 101 11.2 113 4.45 103
OFRF [134]85.2 4.77 100 11.6 77 4.03 97 8.72 123 15.3 112 8.51 126 8.49 115 16.7 101 7.32 108 4.55 94 15.3 49 3.16 104 2.92 40 3.87 40 2.13 27 3.76 59 9.69 29 3.22 60 2.98 88 4.50 94 4.04 121 4.59 100 5.76 55 8.61 127
BriefMatch [124]86.1 3.44 44 9.01 37 2.77 43 2.85 44 9.93 43 2.23 35 2.97 16 7.65 20 1.94 23 3.64 74 20.1 93 1.75 60 4.10 121 4.90 118 5.82 136 7.95 130 17.8 113 8.08 130 4.73 136 5.20 119 12.2 143 7.88 132 12.0 118 13.7 138
Rannacher [23]86.3 4.13 66 11.0 70 3.61 88 3.39 79 12.3 82 2.80 81 7.26 97 17.4 106 3.59 88 4.40 92 23.1 113 2.24 82 3.43 83 4.54 94 2.56 62 5.41 106 18.5 118 4.23 95 2.92 85 3.91 69 2.82 81 3.45 75 9.14 89 3.27 70
F-TV-L1 [15]87.2 5.44 108 12.5 90 5.69 116 5.46 109 15.0 110 4.03 102 7.48 99 16.3 98 3.42 82 5.08 106 23.3 116 2.81 96 3.42 81 4.34 78 3.03 85 4.05 73 15.1 93 3.18 57 2.43 56 3.92 70 1.87 35 3.90 84 9.35 93 2.61 57
SuperFlow [81]88.5 4.16 68 11.1 71 3.32 66 4.80 105 12.2 80 4.68 108 7.80 103 16.0 97 10.6 120 5.16 110 22.4 111 3.24 106 3.39 80 4.24 73 3.71 109 3.44 46 13.7 78 2.91 47 3.19 99 4.62 100 1.87 35 4.74 105 10.6 109 4.24 97
TriFlow [95]88.5 4.73 99 12.4 85 3.49 83 4.03 89 12.5 86 3.70 97 8.18 112 17.2 104 10.4 119 3.50 66 15.4 51 2.32 87 3.43 83 4.21 67 3.42 96 3.90 67 12.3 59 3.76 81 7.86 145 5.72 130 16.2 145 2.80 63 5.89 58 2.50 52
Local-TV-L1 [65]88.7 5.33 106 12.6 91 5.19 112 6.90 116 15.7 115 6.22 115 10.0 119 18.2 109 8.89 112 5.81 115 24.7 121 3.70 113 3.05 49 4.00 51 2.39 44 4.05 73 14.6 87 3.09 53 1.95 22 3.11 4 2.15 53 5.85 116 10.8 110 7.34 122
ContinualFlow_ROB [153]88.8 7.36 121 17.7 125 5.46 113 5.94 111 12.2 80 5.98 113 8.16 111 18.3 110 7.89 110 5.11 107 19.3 86 3.18 105 4.15 123 5.04 123 3.68 107 5.65 111 15.1 93 6.17 122 1.72 7 3.34 15 1.11 4 2.34 50 4.48 39 2.25 44
DF-Auto [115]88.8 5.04 104 13.7 99 3.30 62 6.51 113 14.1 104 6.09 114 8.14 108 16.5 100 10.2 118 5.06 105 21.3 104 3.10 103 3.74 101 4.91 119 3.25 92 2.67 12 11.4 46 2.14 7 3.36 101 5.23 123 1.45 11 4.45 97 9.18 90 4.28 99
CLG-TV [48]89.4 4.00 61 10.3 63 3.40 76 4.33 97 12.3 82 4.08 103 6.78 92 15.5 89 3.64 91 4.07 86 17.7 77 2.39 90 3.79 105 4.86 113 3.23 91 4.48 84 16.5 107 3.80 83 3.55 109 4.65 101 2.89 89 4.00 87 10.1 101 3.18 69
CBF [12]90.3 3.88 56 10.2 60 3.50 84 4.60 101 11.3 72 5.06 109 5.43 74 13.1 68 3.39 81 4.09 87 21.2 103 2.16 77 3.80 108 4.72 107 3.52 103 4.33 79 14.4 84 3.01 51 4.97 137 5.51 128 4.93 129 3.99 86 9.27 92 3.91 91
Bartels [41]92.6 4.43 80 11.1 71 4.17 102 2.83 40 8.84 30 2.56 62 4.54 60 12.5 64 2.80 68 4.87 100 22.1 109 3.05 101 3.58 94 4.35 81 4.15 122 5.55 109 17.5 111 5.78 117 3.74 117 5.02 112 5.98 134 5.21 112 11.9 117 5.20 109
Fusion [6]93.2 4.43 80 13.7 99 4.08 99 2.47 23 8.91 31 2.24 37 3.70 35 9.68 34 3.12 73 3.68 76 19.8 88 2.54 93 4.26 127 5.16 128 4.31 125 6.32 118 16.8 108 6.15 121 4.55 133 5.78 132 3.10 97 7.12 126 13.6 127 7.86 126
p-harmonic [29]93.2 4.64 95 13.0 94 4.43 106 3.41 80 11.9 77 2.93 86 7.60 100 18.1 108 3.96 96 4.65 96 21.0 101 2.97 99 3.46 85 4.33 77 3.34 94 4.75 95 17.5 111 4.60 106 3.05 91 4.17 80 2.15 53 5.09 111 10.9 111 3.77 86
CNN-flow-warp+ref [117]94.2 4.93 103 14.5 111 4.29 104 4.18 92 11.9 77 4.24 105 8.23 113 19.7 118 6.35 106 5.13 108 24.4 120 2.96 97 3.55 92 4.40 83 3.85 112 3.82 62 15.0 90 3.39 67 1.96 23 3.44 24 2.14 52 10.0 136 14.8 133 10.8 134
EAI-Flow [152]94.9 7.40 122 16.3 118 6.04 119 5.29 108 15.0 110 4.27 106 6.28 87 15.0 83 5.22 102 4.99 104 19.1 83 3.49 109 3.55 92 4.55 95 3.01 84 4.69 93 14.8 88 4.25 97 4.16 124 4.83 104 2.55 70 2.61 58 6.99 70 2.48 51
Dynamic MRF [7]95.2 4.58 90 12.4 85 4.14 100 3.25 68 13.9 101 2.27 41 6.02 84 16.8 102 2.36 45 4.39 91 22.6 112 2.51 92 3.61 96 4.55 95 3.46 98 6.81 123 22.2 133 6.78 127 2.41 54 3.48 31 3.69 115 9.26 134 17.8 137 10.2 131
SegOF [10]96.5 5.85 110 13.5 97 3.98 96 7.40 117 14.9 108 8.13 124 8.55 116 17.3 105 9.01 113 6.50 120 18.1 79 5.14 122 3.90 114 4.53 92 4.81 130 6.57 122 21.7 131 6.81 128 1.65 5 3.49 33 1.08 3 3.71 81 9.23 91 3.63 79
FlowNetS+ft+v [112]96.5 4.22 73 12.1 83 3.48 82 4.50 100 13.4 97 3.85 100 8.29 114 18.4 112 6.20 105 4.87 100 21.6 106 3.01 100 3.93 115 5.04 123 3.47 101 3.71 57 15.3 97 3.21 59 3.32 100 5.12 115 3.87 117 3.76 82 9.44 94 3.74 85
ResPWCR_ROB [145]96.8 7.29 120 16.3 118 6.15 120 4.28 95 11.4 73 3.95 101 5.85 80 13.6 73 5.20 101 4.75 98 17.5 76 3.50 110 3.80 108 4.53 92 4.12 121 4.96 102 15.0 90 4.81 108 3.52 107 5.22 122 2.40 63 3.61 79 6.77 67 4.27 98
LDOF [28]96.9 4.60 91 13.0 94 3.77 93 4.67 102 15.5 114 3.67 96 5.63 78 14.0 75 4.21 97 5.80 114 27.1 130 3.43 108 3.52 90 4.50 90 3.46 98 4.84 98 17.8 113 4.04 88 2.46 60 4.14 79 3.25 105 4.85 108 12.0 118 3.78 87
Second-order prior [8]97.9 4.03 62 11.6 77 3.35 68 3.88 87 14.0 103 3.08 90 7.21 96 17.6 107 3.57 87 4.14 88 19.9 91 2.31 86 3.66 98 4.86 113 2.73 76 7.32 126 21.2 129 6.76 126 4.02 120 4.58 99 4.01 119 4.27 91 10.4 105 5.12 105
WOLF_ROB [149]98.6 5.79 109 16.6 121 4.49 108 7.62 118 21.2 132 5.10 111 9.70 118 21.0 122 5.66 104 5.32 111 19.0 81 3.78 114 3.61 96 4.49 88 3.54 104 4.63 89 13.6 76 4.34 101 2.30 49 3.89 68 2.16 56 4.37 93 7.52 81 6.03 117
AugFNG_ROB [144]99.5 8.29 128 19.2 129 5.66 115 7.67 119 16.0 117 8.01 123 10.1 120 20.5 120 11.0 122 5.13 108 15.5 54 3.64 112 4.11 122 4.97 121 3.93 113 4.45 81 15.1 93 4.20 92 2.27 47 4.37 89 1.23 9 3.80 83 6.87 69 4.34 101
FlowNet2 [122]102.8 8.58 131 18.6 126 6.31 121 9.39 128 17.6 121 9.09 129 8.06 107 15.8 93 9.81 116 5.61 113 16.2 67 4.12 116 4.04 118 4.88 115 3.79 110 4.92 99 16.2 104 4.50 103 4.28 128 6.73 140 2.84 83 2.05 41 4.54 41 1.41 18
StereoFlow [44]103.0 17.1 147 28.1 147 17.9 146 18.7 144 29.7 145 16.5 139 20.1 144 30.9 143 17.5 139 21.2 144 38.3 146 17.9 142 4.60 131 5.05 125 5.52 132 2.38 4 11.5 50 1.77 2 1.25 1 2.92 2 0.71 1 4.49 99 10.3 104 4.23 96
EPMNet [133]103.9 8.37 130 18.8 128 6.44 123 9.35 127 18.4 123 8.78 128 7.42 98 14.7 81 8.61 111 5.98 117 20.4 98 4.27 118 4.04 118 4.88 115 3.79 110 4.92 99 16.2 104 4.50 103 3.65 115 6.14 136 2.42 65 2.60 57 6.15 61 1.74 28
Ad-TV-NDC [36]104.6 8.36 129 14.0 104 11.1 139 12.9 135 19.9 129 12.8 135 14.4 131 23.1 124 12.1 125 7.40 123 20.6 100 6.33 123 3.47 87 4.66 103 2.39 44 3.95 70 13.8 79 3.51 70 2.48 61 3.75 55 2.05 48 9.75 135 12.1 120 16.7 142
LFNet_ROB [150]105.6 7.69 123 19.8 130 5.72 117 4.70 103 13.3 96 4.13 104 8.15 110 20.0 119 5.42 103 4.73 97 17.1 75 3.42 107 4.15 123 5.10 127 4.05 117 5.28 105 18.0 115 4.64 107 2.87 82 4.74 102 1.98 45 4.92 109 11.4 114 5.01 104
Shiralkar [42]107.3 4.64 95 14.1 107 3.94 94 4.29 96 16.9 119 2.77 79 7.75 101 18.8 114 3.19 77 5.54 112 25.0 123 3.56 111 3.51 89 4.55 95 3.04 86 7.41 127 20.1 127 6.41 123 3.76 118 4.35 87 5.28 130 6.56 122 14.4 132 5.30 112
Learning Flow [11]107.8 4.23 74 11.7 79 3.41 78 4.16 91 15.3 112 3.42 94 6.78 92 16.9 103 3.83 94 6.41 119 25.3 124 4.25 117 4.66 133 6.01 140 4.00 116 6.33 120 20.7 128 5.30 111 3.09 94 4.84 106 2.91 92 7.08 125 15.0 134 5.27 111
StereoOF-V1MT [119]108.3 4.71 98 14.1 107 3.95 95 5.10 107 20.3 131 2.78 80 7.98 106 20.7 121 2.57 58 4.48 93 21.1 102 2.79 95 4.20 126 5.29 130 4.10 119 6.85 125 22.3 134 6.42 124 2.45 59 4.17 80 3.15 101 10.5 137 18.4 140 10.5 132
IAOF2 [51]109.5 5.38 107 13.7 99 4.50 109 5.95 112 14.6 106 5.61 112 8.80 117 18.8 114 9.40 114 12.2 133 23.8 119 13.1 137 3.86 111 4.89 117 3.12 89 5.21 104 14.9 89 4.54 105 4.33 129 5.15 116 3.93 118 4.39 95 8.57 86 3.87 89
TVL1_ROB [139]110.7 11.3 136 19.8 130 13.0 141 12.9 135 19.6 128 13.7 137 17.4 138 27.8 137 18.0 140 12.6 135 28.9 132 11.8 135 3.71 100 4.78 110 3.46 98 4.21 77 18.0 115 3.99 86 1.79 10 3.54 40 1.21 7 7.58 130 13.9 130 8.92 129
Modified CLG [34]111.5 7.17 119 17.1 124 6.47 124 6.85 115 14.9 108 7.48 119 14.0 127 24.8 128 15.7 135 8.35 126 27.3 131 6.36 124 3.96 116 4.99 122 4.08 118 4.54 86 19.3 123 4.15 91 2.33 51 3.86 66 2.40 63 6.00 117 13.8 129 5.40 113
GraphCuts [14]112.2 6.25 111 14.3 110 5.53 114 8.60 122 20.1 130 6.61 117 7.91 105 15.4 88 10.9 121 4.88 102 19.0 81 3.05 101 3.78 103 4.71 105 3.94 114 8.74 135 16.4 106 5.39 113 4.04 121 4.87 108 4.85 128 6.35 120 12.2 121 6.05 118
2D-CLG [1]112.5 10.1 133 22.6 138 7.59 129 9.84 130 16.9 119 11.1 134 16.9 137 28.2 138 18.8 143 14.1 137 31.1 136 13.1 137 3.86 111 4.62 101 4.53 127 5.98 116 21.2 129 5.97 119 1.76 8 3.14 6 1.46 12 6.29 119 12.9 126 5.81 115
Filter Flow [19]112.6 6.48 113 14.6 112 4.96 110 5.73 110 15.7 115 5.07 110 10.1 120 18.6 113 14.3 131 9.04 128 23.3 116 7.80 128 3.98 117 4.71 105 4.21 124 5.86 114 15.0 90 5.41 114 4.98 138 6.87 141 2.78 78 4.82 107 8.66 87 3.65 81
SPSA-learn [13]113.2 6.84 118 16.7 122 6.74 125 8.47 121 19.4 126 7.49 120 12.5 123 23.1 124 13.1 129 8.40 127 25.8 127 7.08 126 3.87 113 4.66 103 4.10 119 6.32 118 18.8 119 6.89 129 2.56 64 3.85 64 1.79 26 7.29 127 12.5 123 7.47 124
HBpMotionGpu [43]114.6 6.57 115 15.0 115 5.17 111 8.29 120 18.0 122 8.29 125 14.1 128 26.5 131 13.2 130 6.12 118 25.3 124 3.94 115 3.79 105 4.62 101 3.97 115 4.80 97 15.7 98 4.11 89 4.40 130 5.20 119 2.87 88 6.28 118 11.7 116 7.31 121
IAOF [50]115.5 6.49 114 14.6 112 6.42 122 9.22 126 18.5 124 7.94 122 16.4 136 27.4 135 13.0 128 8.22 124 22.2 110 7.73 127 3.77 102 4.76 109 3.42 96 6.84 124 18.8 119 4.23 95 3.59 111 4.46 92 2.83 82 7.51 129 10.1 101 10.6 133
GroupFlow [9]116.0 8.00 125 18.6 126 8.09 131 11.1 133 23.7 137 10.3 132 12.6 124 25.6 129 12.8 127 5.84 116 20.3 97 4.39 119 4.69 134 5.81 136 3.67 106 9.29 136 22.4 135 10.1 138 2.11 36 3.99 74 2.29 59 5.75 114 10.0 98 7.39 123
Black & Anandan [4]116.6 6.81 117 15.4 116 7.43 127 8.77 124 19.5 127 7.35 118 13.0 125 22.9 123 12.5 126 8.29 125 26.1 128 6.77 125 4.18 125 5.28 129 3.69 108 6.19 117 20.0 126 5.34 112 3.63 112 5.05 113 1.79 26 6.45 121 12.2 121 5.17 108
BlockOverlap [61]119.8 6.67 116 13.1 96 5.87 118 6.62 114 13.9 101 6.53 116 10.6 122 19.5 117 10.1 117 6.97 122 24.9 122 5.13 121 4.38 128 4.61 100 6.37 139 7.47 129 15.7 98 6.05 120 6.23 141 6.41 139 13.0 144 6.92 124 9.60 95 12.2 136
Nguyen [33]120.3 7.88 124 16.8 123 7.02 126 13.4 137 19.0 125 15.3 138 17.6 139 28.9 139 17.2 138 12.0 132 26.9 129 11.6 134 4.38 128 5.07 126 5.58 135 5.69 112 19.7 125 5.93 118 2.75 76 4.02 76 1.91 41 6.59 123 12.5 123 6.52 120
UnFlow [129]121.2 14.6 145 25.8 143 9.09 135 9.40 129 16.8 118 9.89 131 14.2 129 26.9 132 11.2 123 10.0 129 25.4 126 8.67 130 5.43 140 5.90 137 6.72 140 8.64 133 24.0 137 9.41 136 3.51 106 4.90 109 1.37 10 4.37 93 12.6 125 3.33 72
2bit-BM-tele [98]122.0 8.00 125 15.8 117 8.40 133 4.91 106 13.4 97 4.67 107 8.14 108 19.0 116 5.12 100 6.62 121 23.5 118 5.04 120 4.08 120 4.78 110 4.61 129 8.68 134 18.8 119 8.31 132 6.46 143 7.08 143 9.47 140 7.36 128 14.1 131 9.62 130
Horn & Schunck [3]126.8 8.01 127 19.9 132 8.38 132 9.13 125 23.2 136 7.71 121 14.2 129 25.9 130 14.6 133 12.4 134 30.6 134 11.3 133 4.64 132 5.64 132 4.60 128 8.21 132 24.4 138 8.45 133 4.01 119 5.41 124 1.95 43 9.16 133 17.5 135 8.86 128
SILK [79]128.2 9.34 132 20.4 133 10.5 138 10.4 131 21.9 133 10.3 132 16.0 135 27.5 136 14.5 132 10.3 130 29.0 133 8.54 129 4.81 135 5.65 133 5.56 134 9.41 137 25.4 140 8.74 134 2.79 79 3.68 53 4.62 126 10.9 138 17.8 137 12.3 137
Heeger++ [104]129.8 11.9 139 21.8 136 8.08 130 12.5 134 29.7 145 9.42 130 14.8 132 27.1 133 9.68 115 14.3 138 31.0 135 12.7 136 4.98 137 5.74 134 4.97 131 17.5 145 34.1 146 18.4 145 2.75 76 5.44 125 2.15 53 12.3 140 18.8 141 14.8 140
TI-DOFE [24]130.7 13.4 143 23.2 139 16.5 145 16.5 141 24.1 138 18.2 143 20.2 145 31.1 145 20.6 144 19.9 143 32.9 139 20.8 144 4.89 136 5.90 137 5.54 133 8.04 131 23.9 136 8.81 135 2.97 87 4.34 86 1.88 38 10.9 138 17.7 136 11.9 135
H+S_ROB [138]132.1 13.0 141 27.1 146 9.66 136 13.4 137 24.8 140 13.4 136 18.7 143 30.9 143 18.3 142 25.8 147 35.7 143 26.4 146 7.08 145 8.13 145 9.10 144 14.6 143 31.3 145 16.3 143 2.17 38 4.44 91 2.11 51 15.1 144 19.9 143 14.2 139
HCIC-L [99]134.9 15.7 146 22.0 137 10.1 137 31.5 147 26.6 143 41.0 147 14.8 132 23.1 124 16.8 137 18.4 142 34.4 141 18.2 143 5.94 141 6.35 141 6.35 138 10.6 140 19.2 122 11.4 140 18.7 147 17.8 147 19.2 146 4.93 110 8.34 84 5.16 106
SLK [47]135.0 11.6 137 26.0 144 14.6 144 15.3 140 25.0 141 17.5 141 17.8 141 30.1 142 18.1 141 25.4 146 33.6 140 28.0 147 5.25 138 5.90 137 7.03 141 10.3 139 27.4 142 10.6 139 2.89 84 4.47 93 2.94 94 14.9 143 20.7 144 18.8 143
FFV1MT [106]136.0 12.0 140 23.3 140 8.83 134 10.7 132 26.6 143 8.71 127 15.6 134 29.0 140 12.0 124 16.6 141 36.3 145 15.5 140 6.51 144 6.40 142 10.4 145 16.2 144 30.7 144 17.7 144 3.41 104 5.44 125 3.35 111 12.3 140 18.8 141 14.8 140
Adaptive flow [45]137.9 13.2 142 20.8 134 14.0 143 17.1 143 22.0 134 17.9 142 18.1 142 27.1 133 22.8 146 11.8 131 31.1 136 10.5 131 6.35 143 7.13 144 6.25 137 9.87 138 21.8 132 9.44 137 12.6 146 11.4 146 20.0 147 7.75 131 13.6 127 7.73 125
PGAM+LK [55]139.2 11.8 138 25.6 141 13.9 142 14.8 139 24.4 139 16.7 140 13.2 126 24.0 127 15.0 134 16.2 140 41.2 147 15.3 139 5.40 139 5.45 131 8.10 142 12.3 142 26.5 141 12.1 141 7.42 144 8.24 145 7.87 138 13.2 142 18.3 139 19.4 144
Periodicity [78]140.1 11.2 135 27.0 145 7.46 128 16.6 142 29.8 147 18.2 143 25.3 147 31.2 147 24.9 147 12.7 136 35.7 143 11.1 132 31.7 147 41.4 147 25.1 147 23.8 147 41.5 147 23.8 147 2.92 85 5.62 129 6.90 136 18.6 146 33.1 147 22.3 145
FOLKI [16]140.9 10.5 134 25.6 141 11.9 140 20.9 145 26.2 142 26.1 145 17.6 139 31.1 145 16.5 136 15.4 139 32.6 138 16.0 141 6.16 142 6.53 143 9.07 143 12.2 141 29.7 143 13.0 142 4.67 135 5.83 133 9.41 139 18.2 145 22.8 145 25.1 146
Pyramid LK [2]143.8 13.9 144 20.9 135 21.4 147 24.1 146 23.1 135 30.2 146 20.9 146 29.5 141 21.9 145 22.2 145 34.6 142 25.0 145 18.7 146 23.1 146 20.2 146 21.2 146 24.5 139 21.0 146 6.41 142 7.02 142 10.8 142 25.6 147 31.5 146 34.5 147
AdaConv-v1 [126]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
SepConv-v1 [127]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
SuperSlomo [132]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
FGIK [136]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
CtxSyn [137]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
CyclicGen [154]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
CFRF [155]148.4 39.2 148 39.9 148 41.8 148 73.0 148 74.5 148 71.1 148 70.1 148 67.3 148 71.8 148 64.4 148 66.2 148 65.9 148 76.5 149 78.1 149 72.0 149 68.2 149 64.9 149 66.5 149 52.3 149 45.1 149 70.9 149 81.8 148 81.6 148 82.3 148
AVG_FLOW_ROB [142]152.4 62.1 155 56.6 155 61.5 155 99.9 155 96.7 155 99.9 155 81.2 155 81.9 155 80.3 155 65.8 155 68.9 155 67.4 155 68.4 148 75.2 148 67.5 148 62.4 148 55.3 148 59.6 148 31.5 148 28.0 148 29.3 148 86.1 155 96.7 155 87.2 155
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 T. Arici. Energy minimization based motion estimation using adaptive smoothness priors. Submitted to IEEE TIP 2011.
[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 D. Nguyen. Enhancing the sharpness of flow field using image-driven functions with occlusion-aware filter. Submitted to TIP 2011.
[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 A. Ayvaci, M. Raptis, and S. Soatto. Sparse occlusion detection with optical flow. Submitted to IJCV 2011.
[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 L. Chen, J. Wang, and Y. Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. Submitted to PAMI 2013.
[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 M. Santoro, G. AlRegib, and Y. Altunbasak. Motion estimation using block overlap minimization. Submitted to 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 W. Dong, G. Shi, X. Hu, and Y. Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. Submitted to IEEE TIP 2013.
[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] Correlation Flow 290 2 color M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code.
[76] TC/T-Flow 341 5 color M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013.
[77] OFLAF 1530 2 color T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013.
[78] Periodicity 8000 4 color G. Khachaturov, S. Gonzalez-Brambila, and J. Gonzalez-Trejo. Periodicity-based computation of optical flow. Submitted to Computacion y Sistemas (CyS) 2013.
[79] SILK 572 2 gray P. Zille, C. Xu, T. Corpetti, L. Shao. Observation models based on scale interactions for optical flow estimation. Submitted to IEEE TIP.
[80] 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.
[81] SuperFlow 178 2 color Anonymous. Superpixel based optical flow estimation. ICCV 2013 submission 507.
[82] Aniso-Texture 300 2 color Anonymous. Texture information-based optical flow estimation using an incremental multi-resolution approach. ITC-CSCC 2013 submission 267.
[83] Classic+CPF 640 2 gray Z. Tu, R. Veltkamp, and N. van der Aa. A combined post-filtering method to improve accuracy of variational optical flow estimation. Submitted to Pattern Recognition 2013.
[84] S2D-Matching 1200 2 color Anonymous. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013 submission 1479.
[85] AGIF+OF 438 2 gray Z. Tu, R. Poppe, and R. Veltkamp. Adaptive guided image filter to warped interpolation image for variational optical flow computation. Submitted to Signal Processing 2015.
[86] DeepFlow 13 2 color P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[87] NNF-Local 673 2 color Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow with nearest neighbor field. Submitted to PAMI 2014.
[88] 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.
[89] 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.
[90] 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.
[91] 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.
[92] 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.
[93] 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.
[94] FMOF 215 2 color N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014.
[95] TriFlow 150 2 color TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914.
[96] ComponentFusion 6.5 2 color Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941.
[97] 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.
[98] 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.
[99] HCIC-L 330 2 color Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114.
[100] TF+OM 600 2 color R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015.
[101] PH-Flow 800 2 color J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015.
[102] EpicFlow 16 2 color J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015.
[103] NNF-EAC 380 2 color Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336.
[104] Heeger++ 6600 5 gray Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238.
[105] HBM-GC 330 2 color A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015.
[106] 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.
[107] 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.
[108] DeepFlow2 16 2 color J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015.
[109] HAST 2667 2 color Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221.
[110] 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.
[111] SVFilterOh 1.56 2 color Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788.
[112] FlowNetS+ft+v 0.5 2 color Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235.
[113] 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.)
[114] 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.
[115] DF-Auto 70 2 color N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015.
[116] CPM-Flow 3 2 color Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241.
[117] 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.
[118] Steered-L1 804 2 color Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016.
[119] 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.
[120] PGM-C 5 2 color Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016.
[121] 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.
[122] FlowNet2 0.091 2 color Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900.
[123] S2F-IF 20 2 color Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765.
[124] 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.
[125] OAR-Flow 60 2 color Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20.
[126] AdaConv-v1 2.8 2 color S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017.
[127] SepConv-v1 0.2 2 color S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017.
[128] ProbFlowFields 37 2 color A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017.
[129] UnFlow 0.12 2 color Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018.
[130] 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.
[131] 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.
[132] 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.
[133] EPMNet 0.061 2 color Anonymous. EPM-convolution multilayer-network for optical flow estimation. ICME 2018 submission 1119.
[134] OFRF 90 2 color T. Mai, M. Gouiffes, and S. Bouchafa. Optical Flow refinement using iterative propagation under color, proximity and flow reliability constraints. Submitted to Signal, Image and Video Processing 2017.
[135] 3DFlow 328 2 color J. Chen, Z. Cai, J. Lai, and X. Xie. A filtering based framework for optical flow estimation. IEEE TCSVT 2018.
[136] FGIK 0.18 2 color Anonymous. (Interpolation results only.) Learning flow-guided interpolation kernels for video frame synthesis. ECCV 2018 submission 433.
[137] CtxSyn 0.07 2 color S. Niklaus and F. Liu. (Interpolation results only.) Context-aware synthesis for video frame interpolation. CVPR 2018.
[138] H+S_ROB 5 2 color ROB 2018 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann. Horn-Schunck optical flow with a multi-scale strategy. Image Processing On Line 3:151–172, 2013.
[139] TVL1_ROB 1 2 color ROB 2018 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo. TV-L1 optical flow estimation. Image Processing On Line 3:137-150, 2013.
[140] 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.
[141] 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.
[142] AVG_FLOW_ROB N/A 2 N/A Average flow field of ROB 2018 training set.
[143] 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.
[144] AugFNG_ROB 0.10 all color Anonymous. FusionNet and AugmentedFlowNet: Selective proxy ground truth for training on unlabeled images. ECCV 2018 submission 2834.
[145] ResPWCR_ROB 0.2 2 color Anonymous. Learning optical flow with residual connections. ROB 2018 submission.
[146] 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.
[147] ProFlow_ROB 76 3 color Anonymous. ProFlow: Learning to predict optical flow. BMVC 2018 submission 277.
[148] PWC-Net_ROB 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.
[149] WOLF_ROB 0.02 2 color Anonymous. Reversed deep neural network for optical flow. ROB 2018 submission.
[150] LFNet_ROB 0.068 2 color Anonymous. Learning a flow network. ROB 2018 submission.
[151] 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.
[152] 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.
[153] ContinualFlow_ROB 0.5 all color Anonymous. Continual Flow. ACCV 2018 submission 273.
[154] CyclicGen 0.088 2 color Anonymous. (Interpolation results only.) Deep video frame interpolation using cyclic frame generation. AAAI 2019 submission 323.
[155] CFRF 0.128 2 color Anonymous. (Interpolation results only.) Coarse-to-fine refinement framework for video frame interpolation. AAAI 2019 submission 4489.
* 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.