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]5.8 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 26 3.36 18 1.62 21 0.99 1 2.16 3 0.57 2
NN-field [71]11.0 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 57 3.70 55 1.95 44 1.01 2 2.25 4 0.53 1
OFLAF [77]14.1 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 32 3.27 12 2.05 49 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 28 3.53 38 1.63 22 1.27 10 2.29 5 1.02 7
nLayers [57]17.6 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 49 3.46 27 1.89 41 1.38 15 3.06 18 1.29 15
MDP-Flow2 [68]20.3 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 26 3.50 35 1.59 19 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 43 3.60 47 1.54 18 1.32 12 2.91 14 1.13 9
TC/T-Flow [76]23.0 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 14 3.23 9 3.14 101 2.03 39 4.53 40 1.49 21
FC-2Layers-FF [74]27.0 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 81 3.92 71 2.80 80 1.25 8 2.57 11 1.20 12
WLIF-Flow [93]28.0 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 34 3.36 18 2.04 48 2.26 47 4.97 49 2.59 56
Layers++ [37]28.5 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 76 4.01 76 2.35 63 1.45 16 3.05 17 1.79 31
HAST [109]29.2 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 3.40 104 4.90 110 5.66 133 1.20 7 2.09 1 1.24 13
AGIF+OF [85]30.1 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 36 3.56 42 2.09 51 1.80 30 3.68 30 2.24 42
Efficient-NL [60]30.4 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 67 3.75 56 2.54 70 1.60 22 3.02 15 1.66 23
LME [70]30.4 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 38 3.53 38 1.73 24 1.34 14 2.75 13 1.18 11
FESL [72]30.5 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 72 4.22 85 2.47 67 1.75 28 3.49 27 1.71 26
ALD-Flow [66]30.7 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 30 3.25 11 3.10 98 2.03 39 5.11 50 1.94 34
RNLOD-Flow [121]31.8 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 91 4.54 97 3.25 106 1.48 18 3.24 21 1.76 30
IROF++ [58]32.0 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 24 3.45 26 1.22 9 1.80 30 4.06 32 2.50 52
NNF-EAC [103]32.5 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 43 3.48 31 1.74 25 2.85 64 6.52 66 3.12 68
ProFlow_ROB [146]32.5 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 17 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 74 3.60 47 3.13 100 1.25 8 2.53 9 1.34 16
Classic+CPF [83]33.4 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 59 3.49 33 2.90 91 1.67 26 3.40 24 2.43 50
Sparse-NonSparse [56]35.1 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 56 3.31 14 2.69 75 1.47 17 3.07 19 1.66 23
TC-Flow [46]36.4 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 22 3.43 23 3.20 105 3.06 69 7.04 71 4.08 93
3DFlow [135]36.9 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 86 11.6 52 4.20 93 3.16 99 4.02 77 4.44 125 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 74 3.52 36 2.84 84 1.59 21 3.38 23 1.80 32
Ramp [62]38.4 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 70 3.38 21 3.19 104 1.54 20 3.21 20 2.24 42
Correlation Flow [75]38.5 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 66 3.61 49 3.00 96 1.49 19 3.04 16 1.42 19
SVFilterOh [111]38.5 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 3.63 113 5.75 132 4.47 126 1.09 4 2.45 8 0.92 6
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 20 3.39 22 1.82 30 2.59 56 6.21 63 2.75 59
PMF [73]39.4 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 4.07 123 6.18 138 4.02 121 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 46 3.35 16 2.91 93 1.62 25 2.56 10 2.09 38
FMOF [94]40.8 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 70 3.84 64 3.27 108 2.66 61 5.69 54 1.95 36
JOF [140]41.0 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 98 4.26 86 4.09 123 2.11 45 4.58 42 2.18 40
OAR-Flow [125]41.7 3.37 39 9.87 54 2.67 33 4.22 93 12.8 90 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 35 1.85 33 4.35 37 1.68 25
Classic+NL [31]42.1 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 83 3.82 63 2.86 88 1.67 26 3.53 28 2.26 46
TV-L1-MCT [64]43.2 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 32 3.35 16 2.29 60 1.95 36 3.89 31 2.71 58
PWC-Net_ROB [147]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 3 1.60 22 3.41 25 1.14 10
IIOF-NLDP [131]46.6 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 30 3.79 60 1.88 39 2.91 66 5.30 53 4.17 94
SimpleFlow [49]47.5 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 54 3.46 27 2.24 59 1.60 22 3.56 29 1.57 22
CostFilter [40]47.7 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 4.16 125 7.18 145 4.66 128 1.16 6 3.36 22 0.87 3
2DHMM-SAS [92]49.9 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 73 3.56 42 3.07 97 2.34 50 5.12 51 2.97 66
S2D-Matching [84]51.0 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 92 3.79 60 3.30 110 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 14 3.62 50 1.77 26 2.48 52 5.86 56 2.77 60
MLDP_OF [89]51.1 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 3.46 106 4.20 83 5.55 132 2.31 48 4.64 44 1.98 37
MDP-Flow [26]51.8 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 79 1.70 58 3.03 47 3.87 40 2.60 65 3.43 45 12.6 65 2.81 42 2.19 43 3.88 68 1.60 20 4.13 90 9.96 98 3.86 88
AggregFlow [97]51.9 4.25 76 11.9 82 3.26 60 4.46 99 13.7 101 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 28 3.77 58 1.48 15 2.07 43 4.11 33 2.36 48
IROF-TV [53]52.2 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 83 16.0 101 3.53 71 1.70 6 3.21 7 1.12 6 1.91 35 4.75 46 2.19 41
CombBMOF [113]53.7 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 5.64 111 12.3 59 6.74 126 3.54 109 5.16 118 2.81 81 1.85 33 4.60 43 1.10 8
S2F-IF [123]53.7 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 17 3.67 53 1.87 36 2.11 45 4.64 44 2.54 55
WRT [150]53.9 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 5.52 109 12.0 56 4.21 95 2.30 50 3.85 65 2.34 62 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 14 3.46 27 1.84 33 2.50 53 6.15 61 2.79 61
Sparse Occlusion [54]57.6 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 5.10 141 6.32 139 3.15 102 2.02 38 4.92 48 1.71 26
NL-TV-NCC [25]57.9 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 93 13.5 73 4.26 99 2.83 81 4.57 99 2.84 84 2.62 59 6.00 60 2.25 44
EPPM w/o HM [88]58.4 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 97 12.9 69 4.32 100 3.64 115 4.54 97 5.73 134 1.76 29 4.11 33 1.94 34
OFH [38]58.5 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 20.5 100 1.60 51 3.18 57 4.06 56 2.82 78 3.86 65 14.1 82 3.59 73 1.77 10 3.62 50 1.81 29 2.64 60 7.08 74 2.15 39
PGM-C [120]58.8 4.62 93 14.0 104 3.39 73 3.29 75 12.3 83 2.70 75 4.39 57 11.7 58 3.43 83 4.00 83 19.8 89 2.15 75 3.19 59 4.23 70 2.54 58 2.79 15 11.9 55 2.45 18 1.83 12 3.21 7 1.83 31 2.31 48 5.87 57 1.82 33
Occlusion-TV-L1 [63]60.6 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 39 3.52 36 1.46 13 4.63 104 11.1 113 3.53 77
Complementary OF [21]61.2 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 81 2.19 79 3.17 55 4.00 51 2.92 82 4.64 91 13.8 79 3.64 76 2.17 39 3.36 18 2.51 68 3.08 70 7.04 71 3.65 81
Adaptive [20]63.0 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 79 4.39 91 1.93 43 3.58 78 8.18 84 2.88 63
Aniso-Texture [82]64.1 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 5.74 114 16.9 110 5.61 116 4.47 133 5.88 135 3.33 111 3.51 77 7.12 75 3.68 83
ACK-Prior [27]64.3 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 6.35 122 16.1 103 4.90 110 4.21 128 4.80 104 6.03 136 3.29 74 5.99 59 2.82 62
CPM-Flow [116]65.1 4.63 94 14.1 107 3.39 73 3.33 76 12.5 87 2.73 77 4.37 54 11.7 58 3.43 83 4.00 83 19.9 92 2.14 74 3.19 59 4.23 70 2.54 58 3.08 29 12.0 56 2.88 46 1.87 18 3.44 24 1.84 33 2.91 66 7.48 81 2.91 65
DPOF [18]65.3 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 83 12.5 63 3.33 64 4.09 124 3.92 71 6.96 138 2.09 44 4.39 38 1.74 28
EpicFlow [102]65.5 4.61 92 14.0 104 3.39 73 3.33 76 12.5 87 2.74 78 5.37 73 14.8 82 3.46 86 3.94 82 19.2 85 2.13 73 3.20 61 4.23 70 2.58 64 2.87 20 12.2 58 2.64 27 1.83 12 3.28 13 1.83 31 3.21 73 7.12 75 3.61 78
DeepFlow2 [108]67.0 4.04 63 11.2 74 3.38 71 3.80 86 12.4 86 2.86 83 5.12 68 13.4 71 3.00 70 4.17 89 20.1 94 2.18 78 2.96 42 3.97 49 2.08 20 3.06 28 12.6 65 2.69 32 2.17 39 3.24 10 2.71 76 4.74 106 10.4 106 4.38 102
ROF-ND [107]67.5 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 4.92 100 13.6 76 3.75 80 4.59 135 5.18 119 4.10 124 2.67 62 5.19 52 3.46 76
TCOF [69]67.7 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 3.66 117 4.83 105 2.67 74 1.83 32 4.20 36 1.46 20
HBM-GC [105]68.9 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 4.40 131 5.92 136 3.62 115 2.55 55 6.34 64 3.29 71
RFlow [90]69.2 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 23.1 114 1.90 66 3.24 66 4.19 64 2.66 72 4.12 76 15.2 97 3.34 66 2.61 68 3.56 42 2.65 73 4.48 99 10.5 109 3.93 92
Steered-L1 [118]70.7 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 88 1.64 53 3.42 81 4.30 76 3.39 95 5.18 104 14.5 85 4.37 103 5.09 140 5.05 114 10.1 142 5.56 114 10.2 104 6.24 120
SRR-TVOF-NL [91]71.5 4.47 84 10.9 69 3.32 66 4.04 90 13.2 96 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 4.16 125 5.21 122 3.44 114 2.06 42 3.48 26 2.42 49
DMF_ROB [139]71.5 4.37 79 12.3 84 3.62 89 3.46 81 12.9 92 2.60 70 5.98 82 15.8 93 3.23 78 4.05 85 19.8 89 2.15 75 3.10 51 4.06 56 2.57 63 3.79 60 14.3 83 3.13 54 1.88 19 3.12 5 1.99 47 4.34 93 10.0 99 3.87 89
ComplOF-FED-GPU [35]71.9 4.28 78 11.3 76 3.70 91 3.25 68 13.0 93 2.16 31 4.06 49 11.2 52 1.95 24 3.91 80 19.2 85 2.01 71 3.20 61 4.15 60 2.64 70 4.61 89 16.1 103 3.90 84 2.98 89 3.77 58 3.69 116 2.85 64 7.44 80 2.53 54
FF++_ROB [145]73.0 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 88 12.7 67 3.70 79 1.91 21 3.46 27 2.19 58 3.65 80 7.31 77 5.97 117
TF+OM [100]75.0 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 95 14.5 85 4.32 100 3.06 94 4.84 107 2.71 76 3.93 85 8.79 89 4.32 100
Aniso. Huber-L1 [22]75.5 3.71 52 10.1 59 3.08 56 4.36 98 13.0 93 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 3.37 103 4.36 89 2.85 87 3.16 71 7.52 82 2.90 64
DeepFlow [86]76.8 4.49 85 11.7 79 4.14 100 4.26 94 12.8 90 3.36 93 5.96 81 14.2 78 5.10 99 4.89 103 23.1 114 2.67 94 2.98 44 4.00 51 2.11 24 3.26 35 13.5 73 2.84 45 2.09 34 3.10 3 2.77 78 5.83 116 11.4 115 5.45 114
Classic++ [32]77.7 3.37 39 9.67 51 2.91 49 3.28 73 12.1 80 2.61 73 5.46 76 14.1 76 3.00 70 3.63 73 20.2 97 1.70 58 3.24 66 4.34 78 2.60 65 4.65 92 16.0 101 3.60 74 3.09 95 3.94 74 3.28 109 4.64 105 10.4 106 3.71 84
TV-L1-improved [17]78.6 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 21.3 105 1.76 61 3.34 75 4.38 82 2.39 44 5.97 116 18.1 118 5.67 117 3.57 111 4.92 112 3.43 113 4.01 88 9.84 97 3.44 75
LocallyOriented [52]80.7 4.54 87 12.8 93 3.27 61 4.73 104 14.8 108 3.73 98 7.77 102 18.3 111 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 68 4.20 83 2.52 69 4.39 96 8.52 86 5.23 110
SIOF [67]80.9 4.23 74 10.2 60 3.31 64 3.97 88 14.5 106 2.97 88 7.81 104 16.4 99 7.48 109 4.82 99 20.1 94 2.96 97 3.54 91 4.49 88 3.12 89 4.31 78 13.5 73 4.13 91 2.36 53 3.59 46 1.68 23 3.46 76 7.39 78 3.37 73
LiteFlowNet [142]82.2 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 63 4.51 96 1.52 16 4.05 89 7.05 73 5.16 106
Brox et al. [5]83.8 4.44 82 12.4 85 4.22 103 3.72 85 13.5 100 3.06 89 4.97 66 13.3 70 3.11 72 4.58 95 22.0 108 2.37 88 3.79 105 4.60 98 4.33 127 3.91 68 17.0 111 3.45 68 2.22 47 3.79 60 1.19 7 4.62 103 10.0 99 3.38 74
TriangleFlow [30]84.1 4.12 64 10.6 67 3.47 81 3.47 82 13.1 95 2.41 47 6.00 83 15.2 84 2.17 36 2.99 45 16.0 64 1.58 48 4.46 131 5.79 136 4.15 123 5.42 108 13.9 81 5.24 111 3.10 97 5.47 128 2.90 91 3.02 68 6.82 68 3.64 80
CRTflow [80]84.5 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 22.0 108 2.24 82 3.32 74 4.34 78 2.44 50 7.43 129 19.3 124 8.15 132 2.55 63 4.09 79 2.59 72 4.60 102 11.2 114 4.45 103
OFRF [134]85.6 4.77 100 11.6 77 4.03 97 8.72 124 15.3 113 8.51 127 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 89 4.50 95 4.04 122 4.59 101 5.76 55 8.61 128
BriefMatch [124]86.5 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 94 1.75 60 4.10 121 4.90 118 5.82 137 7.95 131 17.8 114 8.08 131 4.73 137 5.20 120 12.2 144 7.88 133 12.0 119 13.7 139
Rannacher [23]86.7 4.13 66 11.0 70 3.61 88 3.39 79 12.3 83 2.80 81 7.26 97 17.4 107 3.59 88 4.40 92 23.1 114 2.24 82 3.43 83 4.54 94 2.56 62 5.41 107 18.5 119 4.23 96 2.92 86 3.91 70 2.82 82 3.45 75 9.14 90 3.27 70
F-TV-L1 [15]87.6 5.44 108 12.5 90 5.69 117 5.46 109 15.0 111 4.03 102 7.48 99 16.3 98 3.42 82 5.08 106 23.3 117 2.81 96 3.42 81 4.34 78 3.03 85 4.05 73 15.1 94 3.18 57 2.43 57 3.92 71 1.87 36 3.90 84 9.35 94 2.61 57
TriFlow [95]88.7 4.73 99 12.4 85 3.49 83 4.03 89 12.5 87 3.70 97 8.18 112 17.2 105 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 7.86 146 5.72 131 16.2 146 2.80 63 5.89 58 2.50 52
SuperFlow [81]88.8 4.16 68 11.1 71 3.32 66 4.80 105 12.2 81 4.68 108 7.80 103 16.0 97 10.6 120 5.16 110 22.4 112 3.24 106 3.39 80 4.24 73 3.71 109 3.44 46 13.7 78 2.91 47 3.19 100 4.62 101 1.87 36 4.74 106 10.6 110 4.24 97
Local-TV-L1 [65]89.2 5.33 106 12.6 91 5.19 112 6.90 117 15.7 116 6.22 116 10.0 120 18.2 110 8.89 112 5.81 116 24.7 122 3.70 114 3.05 49 4.00 51 2.39 44 4.05 73 14.6 87 3.09 53 1.95 23 3.11 4 2.15 54 5.85 117 10.8 111 7.34 123
DF-Auto [115]89.2 5.04 104 13.7 99 3.30 62 6.51 114 14.1 105 6.09 115 8.14 108 16.5 100 10.2 118 5.06 105 21.3 105 3.10 103 3.74 101 4.91 119 3.25 92 2.67 12 11.4 46 2.14 7 3.36 102 5.23 124 1.45 12 4.45 98 9.18 91 4.28 99
ContinualFlow_ROB [152]89.2 7.36 121 17.7 125 5.46 114 5.94 112 12.2 81 5.98 114 8.16 111 18.3 111 7.89 110 5.11 107 19.3 87 3.18 105 4.15 124 5.04 124 3.68 107 5.65 112 15.1 94 6.17 123 1.72 7 3.34 15 1.11 5 2.34 50 4.48 39 2.25 44
CLG-TV [48]89.8 4.00 61 10.3 63 3.40 76 4.33 97 12.3 83 4.08 103 6.78 92 15.5 89 3.64 91 4.07 86 17.7 78 2.39 90 3.79 105 4.86 113 3.23 91 4.48 85 16.5 108 3.80 83 3.55 110 4.65 102 2.89 90 4.00 87 10.1 102 3.18 69
CBF [12]90.5 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 21.2 104 2.16 77 3.80 108 4.72 107 3.52 103 4.33 79 14.4 84 3.01 51 4.97 138 5.51 129 4.93 130 3.99 86 9.27 93 3.91 91
Bartels [41]93.0 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 22.1 110 3.05 101 3.58 94 4.35 81 4.15 123 5.55 110 17.5 112 5.78 118 3.74 118 5.02 113 5.98 135 5.21 113 11.9 118 5.20 109
p-harmonic [29]93.7 4.64 95 13.0 94 4.43 106 3.41 80 11.9 77 2.93 86 7.60 100 18.1 109 3.96 96 4.65 96 21.0 102 2.97 99 3.46 85 4.33 77 3.34 94 4.75 96 17.5 112 4.60 107 3.05 92 4.17 81 2.15 54 5.09 112 10.9 112 3.77 86
Fusion [6]93.7 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 89 2.54 93 4.26 128 5.16 129 4.31 126 6.32 119 16.8 109 6.15 122 4.55 134 5.78 133 3.10 98 7.12 127 13.6 128 7.86 127
CNN-flow-warp+ref [117]94.6 4.93 103 14.5 111 4.29 104 4.18 92 11.9 77 4.24 105 8.23 113 19.7 119 6.35 106 5.13 108 24.4 121 2.96 97 3.55 92 4.40 83 3.85 113 3.82 62 15.0 91 3.39 67 1.96 24 3.44 24 2.14 53 10.0 137 14.8 134 10.8 135
CompactFlow_ROB [162]94.8 8.85 132 18.7 128 5.45 113 5.55 110 12.0 79 5.64 113 8.73 117 17.0 104 11.7 124 5.19 111 17.5 76 3.62 112 4.11 122 4.99 122 3.72 110 4.37 80 14.6 87 4.01 88 1.75 8 3.64 52 0.96 2 4.14 91 7.40 79 5.55 115
EAI-Flow [151]95.2 7.40 122 16.3 118 6.04 120 5.29 108 15.0 111 4.27 106 6.28 87 15.0 83 5.22 102 4.99 104 19.1 84 3.49 109 3.55 92 4.55 95 3.01 84 4.69 94 14.8 89 4.25 98 4.16 125 4.83 105 2.55 71 2.61 58 6.99 70 2.48 51
Dynamic MRF [7]95.6 4.58 90 12.4 85 4.14 100 3.25 68 13.9 102 2.27 41 6.02 84 16.8 102 2.36 45 4.39 91 22.6 113 2.51 92 3.61 96 4.55 95 3.46 98 6.81 124 22.2 134 6.78 128 2.41 55 3.48 31 3.69 116 9.26 135 17.8 138 10.2 132
FlowNetS+ft+v [112]96.9 4.22 73 12.1 83 3.48 82 4.50 100 13.4 98 3.85 100 8.29 114 18.4 113 6.20 105 4.87 100 21.6 107 3.01 100 3.93 115 5.04 124 3.47 101 3.71 57 15.3 98 3.21 59 3.32 101 5.12 116 3.87 118 3.76 82 9.44 95 3.74 85
SegOF [10]97.0 5.85 110 13.5 97 3.98 96 7.40 118 14.9 109 8.13 125 8.55 116 17.3 106 9.01 113 6.50 121 18.1 80 5.14 123 3.90 114 4.53 92 4.81 131 6.57 123 21.7 132 6.81 129 1.65 5 3.49 33 1.08 4 3.71 81 9.23 92 3.63 79
ResPWCR_ROB [144]97.1 7.29 120 16.3 118 6.15 121 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 4.12 122 4.96 103 15.0 91 4.81 109 3.52 108 5.22 123 2.40 64 3.61 79 6.77 67 4.27 98
LDOF [28]97.4 4.60 91 13.0 94 3.77 93 4.67 102 15.5 115 3.67 96 5.63 78 14.0 75 4.21 97 5.80 115 27.1 131 3.43 108 3.52 90 4.50 90 3.46 98 4.84 99 17.8 114 4.04 89 2.46 61 4.14 80 3.25 106 4.85 109 12.0 119 3.78 87
Second-order prior [8]98.3 4.03 62 11.6 77 3.35 68 3.88 87 14.0 104 3.08 90 7.21 96 17.6 108 3.57 87 4.14 88 19.9 92 2.31 86 3.66 98 4.86 113 2.73 76 7.32 127 21.2 130 6.76 127 4.02 121 4.58 100 4.01 120 4.27 92 10.4 106 5.12 105
WOLF_ROB [148]99.2 5.79 109 16.6 121 4.49 108 7.62 119 21.2 133 5.10 111 9.70 119 21.0 123 5.66 104 5.32 112 19.0 82 3.78 115 3.61 96 4.49 88 3.54 104 4.63 90 13.6 76 4.34 102 2.30 50 3.89 69 2.16 57 4.37 94 7.52 82 6.03 118
AugFNG_ROB [143]100.1 8.29 128 19.2 130 5.66 116 7.67 120 16.0 118 8.01 124 10.1 121 20.5 121 11.0 122 5.13 108 15.5 54 3.64 113 4.11 122 4.97 121 3.93 114 4.45 82 15.1 94 4.20 93 2.27 48 4.37 90 1.23 10 3.80 83 6.87 69 4.34 101
FlowNet2 [122]103.4 8.58 131 18.6 126 6.31 122 9.39 129 17.6 122 9.09 130 8.06 107 15.8 93 9.81 116 5.61 114 16.2 67 4.12 117 4.04 118 4.88 115 3.79 111 4.92 100 16.2 105 4.50 104 4.28 129 6.73 141 2.84 84 2.05 41 4.54 41 1.41 18
StereoFlow [44]103.8 17.1 148 28.1 148 17.9 147 18.7 145 29.7 146 16.5 140 20.1 145 30.9 144 17.5 140 21.2 145 38.3 147 17.9 143 4.60 132 5.05 126 5.52 133 2.38 4 11.5 50 1.77 2 1.25 1 2.92 2 0.71 1 4.49 100 10.3 105 4.23 96
EPMNet [133]104.5 8.37 130 18.8 129 6.44 124 9.35 128 18.4 124 8.78 129 7.42 98 14.7 81 8.61 111 5.98 118 20.4 99 4.27 119 4.04 118 4.88 115 3.79 111 4.92 100 16.2 105 4.50 104 3.65 116 6.14 137 2.42 66 2.60 57 6.15 61 1.74 28
Ad-TV-NDC [36]105.3 8.36 129 14.0 104 11.1 140 12.9 136 19.9 130 12.8 136 14.4 132 23.1 125 12.1 126 7.40 124 20.6 101 6.33 124 3.47 87 4.66 103 2.39 44 3.95 70 13.8 79 3.51 70 2.48 62 3.75 56 2.05 49 9.75 136 12.1 121 16.7 143
LFNet_ROB [149]106.2 7.69 123 19.8 131 5.72 118 4.70 103 13.3 97 4.13 104 8.15 110 20.0 120 5.42 103 4.73 97 17.1 75 3.42 107 4.15 124 5.10 128 4.05 118 5.28 106 18.0 116 4.64 108 2.87 83 4.74 103 1.98 46 4.92 110 11.4 115 5.01 104
Shiralkar [42]107.8 4.64 95 14.1 107 3.94 94 4.29 96 16.9 120 2.77 79 7.75 101 18.8 115 3.19 77 5.54 113 25.0 124 3.56 111 3.51 89 4.55 95 3.04 86 7.41 128 20.1 128 6.41 124 3.76 119 4.35 88 5.28 131 6.56 123 14.4 133 5.30 112
Learning Flow [11]108.4 4.23 74 11.7 79 3.41 78 4.16 91 15.3 113 3.42 94 6.78 92 16.9 103 3.83 94 6.41 120 25.3 125 4.25 118 4.66 134 6.01 141 4.00 117 6.33 121 20.7 129 5.30 112 3.09 95 4.84 107 2.91 93 7.08 126 15.0 135 5.27 111
StereoOF-V1MT [119]109.0 4.71 98 14.1 107 3.95 95 5.10 107 20.3 132 2.78 80 7.98 106 20.7 122 2.57 58 4.48 93 21.1 103 2.79 95 4.20 127 5.29 131 4.10 120 6.85 126 22.3 135 6.42 125 2.45 60 4.17 81 3.15 102 10.5 138 18.4 141 10.5 133
IAOF2 [51]110.1 5.38 107 13.7 99 4.50 109 5.95 113 14.6 107 5.61 112 8.80 118 18.8 115 9.40 114 12.2 134 23.8 120 13.1 138 3.86 111 4.89 117 3.12 89 5.21 105 14.9 90 4.54 106 4.33 130 5.15 117 3.93 119 4.39 96 8.57 87 3.87 89
TVL1_ROB [138]111.4 11.3 137 19.8 131 13.0 142 12.9 136 19.6 129 13.7 138 17.4 139 27.8 138 18.0 141 12.6 136 28.9 133 11.8 136 3.71 100 4.78 110 3.46 98 4.21 77 18.0 116 3.99 86 1.79 11 3.54 40 1.21 8 7.58 131 13.9 131 8.92 130
Modified CLG [34]112.2 7.17 119 17.1 124 6.47 125 6.85 116 14.9 109 7.48 120 14.0 128 24.8 129 15.7 136 8.35 127 27.3 132 6.36 125 3.96 116 4.99 122 4.08 119 4.54 87 19.3 124 4.15 92 2.33 52 3.86 67 2.40 64 6.00 118 13.8 130 5.40 113
GraphCuts [14]112.9 6.25 111 14.3 110 5.53 115 8.60 123 20.1 131 6.61 118 7.91 105 15.4 88 10.9 121 4.88 102 19.0 82 3.05 101 3.78 103 4.71 105 3.94 115 8.74 136 16.4 107 5.39 114 4.04 122 4.87 109 4.85 129 6.35 121 12.2 122 6.05 119
Filter Flow [19]113.3 6.48 113 14.6 112 4.96 110 5.73 111 15.7 116 5.07 110 10.1 121 18.6 114 14.3 132 9.04 129 23.3 117 7.80 129 3.98 117 4.71 105 4.21 125 5.86 115 15.0 91 5.41 115 4.98 139 6.87 142 2.78 79 4.82 108 8.66 88 3.65 81
2D-CLG [1]113.4 10.1 134 22.6 139 7.59 130 9.84 131 16.9 120 11.1 135 16.9 138 28.2 139 18.8 144 14.1 138 31.1 137 13.1 138 3.86 111 4.62 101 4.53 128 5.98 117 21.2 130 5.97 120 1.76 9 3.14 6 1.46 13 6.29 120 12.9 127 5.81 116
SPSA-learn [13]114.0 6.84 118 16.7 122 6.74 126 8.47 122 19.4 127 7.49 121 12.5 124 23.1 125 13.1 130 8.40 128 25.8 128 7.08 127 3.87 113 4.66 103 4.10 120 6.32 119 18.8 120 6.89 130 2.56 65 3.85 65 1.79 27 7.29 128 12.5 124 7.47 125
HBpMotionGpu [43]115.4 6.57 115 15.0 115 5.17 111 8.29 121 18.0 123 8.29 126 14.1 129 26.5 132 13.2 131 6.12 119 25.3 125 3.94 116 3.79 105 4.62 101 3.97 116 4.80 98 15.7 99 4.11 90 4.40 131 5.20 120 2.87 89 6.28 119 11.7 117 7.31 122
IAOF [50]116.3 6.49 114 14.6 112 6.42 123 9.22 127 18.5 125 7.94 123 16.4 137 27.4 136 13.0 129 8.22 125 22.2 111 7.73 128 3.77 102 4.76 109 3.42 96 6.84 125 18.8 120 4.23 96 3.59 112 4.46 93 2.83 83 7.51 130 10.1 102 10.6 134
GroupFlow [9]116.9 8.00 125 18.6 126 8.09 132 11.1 134 23.7 138 10.3 133 12.6 125 25.6 130 12.8 128 5.84 117 20.3 98 4.39 120 4.69 135 5.81 137 3.67 106 9.29 137 22.4 136 10.1 139 2.11 37 3.99 75 2.29 60 5.75 115 10.0 99 7.39 124
Black & Anandan [4]117.5 6.81 117 15.4 116 7.43 128 8.77 125 19.5 128 7.35 119 13.0 126 22.9 124 12.5 127 8.29 126 26.1 129 6.77 126 4.18 126 5.28 130 3.69 108 6.19 118 20.0 127 5.34 113 3.63 113 5.05 114 1.79 27 6.45 122 12.2 122 5.17 108
BlockOverlap [61]120.6 6.67 116 13.1 96 5.87 119 6.62 115 13.9 102 6.53 117 10.6 123 19.5 118 10.1 117 6.97 123 24.9 123 5.13 122 4.38 129 4.61 100 6.37 140 7.47 130 15.7 99 6.05 121 6.23 142 6.41 140 13.0 145 6.92 125 9.60 96 12.2 137
Nguyen [33]121.2 7.88 124 16.8 123 7.02 127 13.4 138 19.0 126 15.3 139 17.6 140 28.9 140 17.2 139 12.0 133 26.9 130 11.6 135 4.38 129 5.07 127 5.58 136 5.69 113 19.7 126 5.93 119 2.75 77 4.02 77 1.91 42 6.59 124 12.5 124 6.52 121
UnFlow [129]122.1 14.6 146 25.8 144 9.09 136 9.40 130 16.8 119 9.89 132 14.2 130 26.9 133 11.2 123 10.0 130 25.4 127 8.67 131 5.43 141 5.90 138 6.72 141 8.64 134 24.0 138 9.41 137 3.51 107 4.90 110 1.37 11 4.37 94 12.6 126 3.33 72
2bit-BM-tele [98]122.6 8.00 125 15.8 117 8.40 134 4.91 106 13.4 98 4.67 107 8.14 108 19.0 117 5.12 100 6.62 122 23.5 119 5.04 121 4.08 120 4.78 110 4.61 130 8.68 135 18.8 120 8.31 133 6.46 144 7.08 144 9.47 141 7.36 129 14.1 132 9.62 131
Horn & Schunck [3]127.8 8.01 127 19.9 133 8.38 133 9.13 126 23.2 137 7.71 122 14.2 130 25.9 131 14.6 134 12.4 135 30.6 135 11.3 134 4.64 133 5.64 133 4.60 129 8.21 133 24.4 139 8.45 134 4.01 120 5.41 125 1.95 44 9.16 134 17.5 136 8.86 129
SILK [79]129.2 9.34 133 20.4 134 10.5 139 10.4 132 21.9 134 10.3 133 16.0 136 27.5 137 14.5 133 10.3 131 29.0 134 8.54 130 4.81 136 5.65 134 5.56 135 9.41 138 25.4 141 8.74 135 2.79 80 3.68 54 4.62 127 10.9 139 17.8 138 12.3 138
Heeger++ [104]130.8 11.9 140 21.8 137 8.08 131 12.5 135 29.7 146 9.42 131 14.8 133 27.1 134 9.68 115 14.3 139 31.0 136 12.7 137 4.98 138 5.74 135 4.97 132 17.5 146 34.1 147 18.4 146 2.75 77 5.44 126 2.15 54 12.3 141 18.8 142 14.8 141
TI-DOFE [24]131.7 13.4 144 23.2 140 16.5 146 16.5 142 24.1 139 18.2 144 20.2 146 31.1 146 20.6 145 19.9 144 32.9 140 20.8 145 4.89 137 5.90 138 5.54 134 8.04 132 23.9 137 8.81 136 2.97 88 4.34 87 1.88 39 10.9 139 17.7 137 11.9 136
H+S_ROB [137]133.1 13.0 142 27.1 147 9.66 137 13.4 138 24.8 141 13.4 137 18.7 144 30.9 144 18.3 143 25.8 148 35.7 144 26.4 147 7.08 146 8.13 146 9.10 145 14.6 144 31.3 146 16.3 144 2.17 39 4.44 92 2.11 52 15.1 145 19.9 144 14.2 140
HCIC-L [99]135.9 15.7 147 22.0 138 10.1 138 31.5 148 26.6 144 41.0 148 14.8 133 23.1 125 16.8 138 18.4 143 34.4 142 18.2 144 5.94 142 6.35 142 6.35 139 10.6 141 19.2 123 11.4 141 18.7 148 17.8 148 19.2 147 4.93 111 8.34 85 5.16 106
SLK [47]136.0 11.6 138 26.0 145 14.6 145 15.3 141 25.0 142 17.5 142 17.8 142 30.1 143 18.1 142 25.4 147 33.6 141 28.0 148 5.25 139 5.90 138 7.03 142 10.3 140 27.4 143 10.6 140 2.89 85 4.47 94 2.94 95 14.9 144 20.7 145 18.8 144
FFV1MT [106]137.0 12.0 141 23.3 141 8.83 135 10.7 133 26.6 144 8.71 128 15.6 135 29.0 141 12.0 125 16.6 142 36.3 146 15.5 141 6.51 145 6.40 143 10.4 146 16.2 145 30.7 145 17.7 145 3.41 105 5.44 126 3.35 112 12.3 141 18.8 142 14.8 141
Adaptive flow [45]138.9 13.2 143 20.8 135 14.0 144 17.1 144 22.0 135 17.9 143 18.1 143 27.1 134 22.8 147 11.8 132 31.1 137 10.5 132 6.35 144 7.13 145 6.25 138 9.87 139 21.8 133 9.44 138 12.6 147 11.4 147 20.0 148 7.75 132 13.6 128 7.73 126
PGAM+LK [55]140.2 11.8 139 25.6 142 13.9 143 14.8 140 24.4 140 16.7 141 13.2 127 24.0 128 15.0 135 16.2 141 41.2 148 15.3 140 5.40 140 5.45 132 8.10 143 12.3 143 26.5 142 12.1 142 7.42 145 8.24 146 7.87 139 13.2 143 18.3 140 19.4 145
Periodicity [78]141.1 11.2 136 27.0 146 7.46 129 16.6 143 29.8 148 18.2 144 25.3 148 31.2 148 24.9 148 12.7 137 35.7 144 11.1 133 31.7 148 41.4 148 25.1 148 23.8 148 41.5 148 23.8 148 2.92 86 5.62 130 6.90 137 18.6 147 33.1 148 22.3 146
FOLKI [16]141.9 10.5 135 25.6 142 11.9 141 20.9 146 26.2 143 26.1 146 17.6 140 31.1 146 16.5 137 15.4 140 32.6 139 16.0 142 6.16 143 6.53 144 9.07 144 12.2 142 29.7 144 13.0 143 4.67 136 5.83 134 9.41 140 18.2 146 22.8 146 25.1 147
Pyramid LK [2]144.8 13.9 145 20.9 136 21.4 148 24.1 147 23.1 136 30.2 147 20.9 147 29.5 142 21.9 146 22.2 146 34.6 143 25.0 146 18.7 147 23.1 147 20.2 147 21.2 147 24.5 140 21.0 147 6.41 143 7.02 143 10.8 143 25.6 148 31.5 147 34.5 148
AdaConv-v1 [126]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
SepConv-v1 [127]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
SuperSlomo [132]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
CtxSyn [136]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
CyclicGen [153]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
TOF-M [154]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
MEMC-Net+ [155]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
CFRF [156]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
MPRN [157]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
DAIN [158]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
PyrWarp [159]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
InterpCNN [160]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
OFRI [161]149.4 39.2 149 39.9 149 41.8 149 73.0 149 74.5 149 71.1 149 70.1 149 67.3 149 71.8 149 64.4 149 66.2 149 65.9 149 76.5 150 78.1 150 72.0 150 68.2 150 64.9 150 66.5 150 52.3 150 45.1 150 70.9 150 81.8 149 81.6 149 82.3 149
AVG_FLOW_ROB [141]157.1 62.1 162 56.6 162 61.5 162 99.9 162 96.7 162 99.9 162 81.2 162 81.9 162 80.3 162 65.8 162 68.9 162 67.4 162 68.4 149 75.2 149 67.5 149 62.4 149 55.3 149 59.6 149 31.5 149 28.0 149 29.3 149 86.1 162 96.7 162 87.2 162
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] CtxSyn 0.07 2 color S. Niklaus and F. Liu. (Interpolation results only.) Context-aware synthesis for video frame interpolation. CVPR 2018.
[137] 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.
[138] 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.
[139] 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.
[140] 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.
[141] AVG_FLOW_ROB N/A 2 N/A Average flow field of ROB 2018 training set.
[142] 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.
[143] AugFNG_ROB 0.10 all color Anonymous. FusionNet and AugmentedFlowNet: Selective proxy ground truth for training on unlabeled images. ECCV 2018 submission 2834.
[144] ResPWCR_ROB 0.2 2 color Anonymous. Learning optical flow with residual connections. ROB 2018 submission.
[145] 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.
[146] ProFlow_ROB 76 3 color Anonymous. ProFlow: Learning to predict optical flow. BMVC 2018 submission 277.
[147] 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.
[148] WOLF_ROB 0.02 2 color Anonymous. Reversed deep neural network for optical flow. ROB 2018 submission.
[149] LFNet_ROB 0.068 2 color Anonymous. Learning a flow network. ROB 2018 submission.
[150] 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.
[151] 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.
[152] ContinualFlow_ROB 0.5 all color M Neoral, J. Sochman, and J. Matas. Continual occlusions and optical flow estimation. ACCV 2018.
[153] CyclicGen 0.088 2 color Anonymous. (Interpolation results only.) Deep video frame interpolation using cyclic frame generation. AAAI 2019 submission 323.
[154] TOF-M 0.393 2 color T. Xue, B. Chen, J. Wu, D. Wei, and W. Freeman. Video enhancement with task-oriented flow. arXiv 1711.09078, 2017.
[155] MEMC-Net+ 0.16 2 color W. Bao, W.-S. Lai, X. Zhang, Z. Gao, and M.-H. Yang. (Interpolation results only.) MEMC-Net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. Submitted to TPAMI 2018.
[156] CFRF 0.128 2 color Anonymous. (Interpolation results only.) Coarse-to-fine refinement framework for video frame interpolation. CVPR 2019 submission 1992.
[157] MPRN 0.32 4 color Anonymous. (Interpolation results only.) Multi-frame pyramid refinement network for video frame interpolation. CVPR 2019 submission 1361.
[158] DAIN 0.13 2 color Anonymous. (Interpolation results only.) DAIN: Depth-aware video frame interpolation. CVPR 2019 submission 1769.
[159] PyrWarp 0.14 2 color Anonymous. (Interpolation results only.) Feature pyramid warping for video frame interpolation. CVPR 2019 submission 868.
[160] InterpCNN 0.65 2 color Anonymous. (Interpolation results only.) Video frame interpolation with a stack of synthesis networks and intermediate optical flows. CVPR 2019 submission 6533.
[161] OFRI 0.31 2 color Anonymous. (Interpolation results only.) Efficient video frame interpolation via optical flow refinement. CVPR 2019 submission 6743.
[162] CompactFlow_ROB 0.05 2 color Anonymous. CompactFlow: spatially shiftable window revisited. CVPR 2019 submission 1387.
* 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.