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]5.5 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 3 9.14 7 1.06 4 2.28 2 2.94 1 1.57 2 2.39 5 6.78 2 2.15 9 2.00 24 3.36 17 1.62 20 0.99 1 2.16 3 0.57 2
NN-field [71]10.7 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 6 0.79 1 2.35 4 3.05 5 1.60 3 1.89 1 5.20 1 1.37 1 2.43 54 3.70 53 1.95 43 1.01 2 2.25 4 0.53 1
OFLAF [77]13.8 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 20 13.7 27 1.37 22 2.35 4 3.13 6 1.62 4 2.98 23 7.73 7 2.57 22 2.08 30 3.27 12 2.05 48 1.33 13 2.43 7 1.40 17
PMMST [114]14.9 3.42 43 7.60 5 2.65 31 2.32 11 6.39 1 2.20 34 2.63 11 6.08 8 2.03 28 2.06 6 6.07 2 1.44 30 2.60 10 3.27 8 1.91 10 2.56 7 6.78 2 2.09 5 2.06 26 3.53 37 1.63 21 1.27 10 2.29 5 1.02 7
nLayers [57]17.3 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 53 2.30 12 12.7 14 1.16 7 2.30 3 3.02 3 1.70 5 2.62 11 6.95 4 2.09 5 2.29 47 3.46 26 1.89 40 1.38 15 3.06 18 1.29 15
MDP-Flow2 [68]20.0 3.23 31 7.93 13 2.60 23 1.92 2 6.64 2 1.52 1 2.46 7 5.91 7 1.56 5 3.05 46 15.8 60 1.51 40 2.77 24 3.50 18 2.16 28 2.86 19 8.58 18 2.70 33 2.00 24 3.50 34 1.59 18 1.28 11 2.67 12 0.89 4
ComponentFusion [96]20.2 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 11 13.1 17 1.01 3 2.71 21 3.56 20 2.10 23 3.55 53 12.4 60 3.22 60 2.19 41 3.60 46 1.54 17 1.32 12 2.91 14 1.13 9
TC/T-Flow [76]22.7 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 36 16.7 70 1.21 9 2.60 10 3.49 17 1.90 9 2.21 2 7.65 5 2.04 4 1.84 12 3.23 9 3.14 98 2.03 39 4.53 39 1.49 21
FC-2Layers-FF [74]26.5 3.02 14 7.87 12 2.61 24 2.72 32 9.35 36 2.29 42 2.36 5 5.47 4 2.15 35 2.48 13 12.6 13 1.28 13 2.49 7 3.19 7 2.03 17 3.39 42 8.92 20 2.83 44 2.83 79 3.92 68 2.80 77 1.25 8 2.57 11 1.20 12
WLIF-Flow [93]27.5 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 28 13.5 23 1.33 16 3.01 44 4.00 50 2.40 46 3.03 26 8.32 12 2.44 17 2.09 32 3.36 17 2.04 47 2.26 47 4.97 48 2.59 55
Layers++ [37]28.0 3.11 18 8.22 20 2.79 43 2.43 19 7.02 5 2.24 37 2.43 6 5.77 6 2.18 38 2.13 8 9.71 9 1.15 6 2.35 4 3.02 3 1.96 11 3.81 61 11.4 46 3.22 60 2.74 74 4.01 73 2.35 61 1.45 16 3.05 17 1.79 31
HAST [109]28.8 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 35 15.5 54 1.08 5 2.23 1 2.97 2 1.40 1 3.72 58 10.0 33 3.92 85 3.40 102 4.90 107 5.66 130 1.20 7 2.09 1 1.24 13
AGIF+OF [85]29.8 3.06 16 8.20 17 2.55 21 3.17 60 10.6 54 2.46 55 3.46 30 8.97 30 2.24 41 2.61 18 13.7 27 1.33 16 2.63 15 3.46 15 2.11 24 2.88 21 8.34 14 2.35 13 2.10 34 3.56 41 2.09 50 1.80 30 3.68 30 2.24 42
FESL [72]30.0 2.96 10 7.70 7 2.54 19 3.26 71 10.4 51 2.56 62 3.25 23 8.39 23 2.17 36 2.56 15 13.2 18 1.31 15 2.57 9 3.40 11 2.12 26 2.60 9 7.65 5 2.30 11 2.64 70 4.22 82 2.47 65 1.75 28 3.49 27 1.71 26
Efficient-NL [60]30.0 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 32 14.3 36 1.46 35 2.61 12 3.48 16 1.96 11 3.31 38 8.33 13 2.59 24 2.60 65 3.75 54 2.54 68 1.60 22 3.02 15 1.66 23
LME [70]30.0 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 97 2.69 26 13.6 25 1.42 27 2.85 31 3.61 23 2.42 48 3.47 49 12.8 67 3.17 56 2.12 36 3.53 37 1.73 23 1.34 14 2.75 13 1.18 11
ALD-Flow [66]30.4 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 48 16.8 71 1.28 13 2.69 20 3.60 22 1.85 8 2.79 15 11.3 45 2.32 12 2.07 28 3.25 11 3.10 95 2.03 39 5.11 49 1.94 34
RNLOD-Flow [121]31.3 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 14 13.5 23 1.21 9 2.68 18 3.62 25 2.05 19 2.99 24 8.59 19 2.75 37 3.00 89 4.54 94 3.25 103 1.48 18 3.24 21 1.76 30
IROF++ [58]31.5 3.17 25 8.69 30 2.61 24 2.79 36 9.61 39 2.33 43 3.43 27 8.86 27 2.38 47 2.87 38 14.8 41 1.52 42 2.74 22 3.57 21 2.19 30 3.20 34 9.70 30 2.71 34 1.96 22 3.45 25 1.22 7 1.80 30 4.06 32 2.50 51
NNF-EAC [103]32.0 3.31 35 8.21 19 2.68 33 2.19 9 7.49 13 1.76 13 2.73 13 6.62 12 1.70 14 3.18 53 15.8 60 1.64 52 2.87 34 3.66 28 2.24 32 3.02 25 8.07 10 2.59 24 2.19 41 3.48 30 1.74 24 2.85 63 6.52 65 3.12 67
ProFlow_ROB [147]32.0 3.29 32 9.91 55 2.35 13 2.50 25 10.0 44 1.83 16 4.04 47 11.6 55 1.96 26 2.86 36 15.0 44 1.22 11 2.87 34 3.89 41 1.97 14 2.60 9 10.5 36 2.20 10 1.53 4 3.54 39 1.53 16 2.50 52 6.37 64 2.33 46
PH-Flow [101]32.4 3.19 28 8.87 35 2.71 34 2.84 41 9.33 35 2.37 45 2.85 14 7.20 15 2.36 44 2.92 41 15.4 51 1.51 40 2.63 15 3.42 12 2.04 18 3.03 26 8.52 17 2.49 19 2.69 72 3.60 46 3.13 97 1.25 8 2.53 9 1.34 16
Classic+CPF [83]32.9 3.14 21 8.60 27 2.63 28 3.03 57 10.6 54 2.33 43 3.66 34 9.58 33 2.20 39 2.61 18 14.1 32 1.34 19 2.68 18 3.53 19 2.21 31 2.85 18 7.95 9 2.38 14 2.44 56 3.49 32 2.90 88 1.67 26 3.40 24 2.43 50
Sparse-NonSparse [56]34.6 3.14 21 8.75 32 2.76 41 3.02 55 10.6 54 2.43 50 3.45 29 8.96 28 2.36 44 2.66 23 13.7 27 1.42 27 2.85 31 3.75 35 2.33 37 3.28 37 9.40 26 2.73 35 2.42 53 3.31 14 2.69 72 1.47 17 3.07 19 1.66 23
TC-Flow [46]35.9 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 49 16.6 69 1.46 35 2.78 25 3.73 34 1.96 11 3.08 29 11.4 46 2.66 28 1.94 20 3.43 22 3.20 102 3.06 68 7.04 69 4.08 92
3DFlow [135]36.3 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 2 4.58 1 1.23 12 2.86 33 3.72 32 2.16 28 4.52 85 11.6 52 4.20 92 3.16 97 4.02 74 4.44 122 1.13 5 2.14 2 0.89 4
LSM [39]37.0 3.12 19 8.62 28 2.75 40 3.00 53 10.5 53 2.44 52 3.43 27 8.85 26 2.35 43 2.66 23 13.6 25 1.44 30 2.82 27 3.68 29 2.36 39 3.38 41 9.41 27 2.81 42 2.69 72 3.52 35 2.84 81 1.59 21 3.38 23 1.80 32
Ramp [62]37.9 3.18 27 8.83 34 2.73 37 2.89 46 10.1 45 2.44 52 3.27 24 8.43 24 2.38 47 2.74 30 14.2 33 1.46 35 2.82 27 3.69 31 2.29 35 3.37 40 9.31 24 2.93 48 2.62 68 3.38 20 3.19 101 1.54 20 3.21 20 2.24 42
Correlation Flow [75]38.0 3.38 41 8.40 23 2.64 29 2.23 10 7.54 14 1.56 3 5.14 69 13.1 68 1.60 7 2.09 7 8.15 5 1.35 21 3.12 52 4.09 57 2.34 38 4.01 72 11.5 50 4.00 87 2.59 64 3.61 48 3.00 93 1.49 19 3.04 16 1.42 19
SVFilterOh [111]38.0 3.63 50 8.82 33 2.86 45 2.60 29 8.06 18 2.05 27 2.95 15 7.09 14 2.03 28 2.80 34 13.8 30 1.41 26 2.63 15 3.42 12 1.75 7 3.49 50 10.3 35 3.23 63 3.63 111 5.75 129 4.47 123 1.09 4 2.45 8 0.92 6
ProbFlowFields [128]38.5 4.18 69 12.4 84 3.40 75 2.43 19 8.16 20 2.19 33 3.65 33 9.72 35 2.86 68 2.22 9 9.42 8 1.42 27 3.01 44 3.96 47 2.36 39 2.73 14 10.9 38 2.51 20 1.89 18 3.39 21 1.82 29 2.59 55 6.21 62 2.75 58
COFM [59]38.9 3.17 25 9.90 54 2.46 16 2.41 16 8.34 23 1.92 21 3.77 39 10.5 42 2.54 56 2.71 28 14.9 43 1.19 8 3.08 49 3.92 45 3.25 90 3.83 64 10.9 38 3.15 55 2.20 44 3.35 15 2.91 90 1.62 25 2.56 10 2.09 38
PMF [73]39.0 3.61 48 9.07 38 2.62 26 2.40 14 8.05 17 1.83 16 2.61 9 6.27 10 1.65 11 3.35 63 15.4 51 1.58 47 2.54 8 3.27 8 1.71 6 3.59 54 11.1 43 3.46 69 4.07 121 6.18 135 4.02 118 1.06 3 2.38 6 1.25 14
FMOF [94]40.2 3.12 19 8.23 21 2.73 37 3.25 68 10.7 61 2.52 59 3.01 18 7.61 18 2.20 39 2.56 15 13.4 21 1.33 16 2.75 23 3.61 23 2.24 32 3.66 56 8.50 16 2.78 40 2.62 68 3.84 62 3.27 105 2.66 59 5.69 53 1.95 36
JOF [141]40.4 3.08 17 8.56 26 2.51 18 3.27 72 10.2 48 2.81 81 3.02 19 7.55 17 2.42 51 2.64 21 14.2 33 1.34 19 2.62 13 3.42 12 2.08 20 3.26 35 8.96 21 2.56 21 3.12 96 4.26 83 4.09 120 2.11 45 4.58 41 2.18 40
OAR-Flow [125]41.2 3.37 39 9.87 53 2.67 32 4.22 92 12.8 87 2.87 83 4.95 65 13.4 71 2.66 60 3.23 55 16.4 68 1.37 22 2.83 29 3.82 38 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]41.5 3.20 30 8.72 31 2.81 44 3.02 55 10.6 54 2.44 52 3.46 30 8.84 25 2.38 47 2.78 33 14.3 36 1.46 35 2.83 29 3.68 29 2.31 36 3.40 43 9.09 23 2.76 39 2.87 81 3.82 61 2.86 85 1.67 26 3.53 28 2.26 45
TV-L1-MCT [64]42.5 3.16 24 8.48 25 2.71 34 3.28 73 10.8 65 2.60 69 3.95 45 10.5 42 2.38 47 2.69 26 13.9 31 1.45 34 2.94 40 3.79 36 2.63 68 3.50 51 9.75 31 3.06 52 2.08 30 3.35 15 2.29 59 1.95 36 3.89 31 2.71 57
PWC-Net_ROB [148]45.2 4.86 101 12.4 84 3.56 84 3.14 59 10.3 50 2.60 69 4.38 55 11.6 55 3.18 75 2.56 15 10.6 11 1.52 42 3.25 68 4.18 61 2.46 50 3.10 31 10.6 37 2.75 37 1.44 2 3.56 41 1.01 2 1.60 22 3.41 25 1.14 10
IIOF-NLDP [131]46.0 3.65 51 9.81 52 2.56 22 2.79 36 9.36 37 2.00 23 4.28 53 11.3 53 1.69 13 2.02 5 7.52 4 1.38 25 3.36 75 4.52 90 2.40 46 3.82 62 11.2 44 3.67 78 2.07 28 3.79 58 1.88 38 2.91 65 5.30 52 4.17 93
SimpleFlow [49]46.8 3.35 36 9.20 41 2.98 52 3.18 63 10.7 61 2.71 75 5.06 67 12.6 66 2.70 62 2.95 43 15.1 46 1.58 47 2.91 38 3.79 36 2.47 51 3.59 54 9.49 28 2.99 50 2.39 51 3.46 26 2.24 58 1.60 22 3.56 29 1.57 22
CostFilter [40]47.2 3.84 54 9.64 48 3.06 54 2.55 28 8.09 19 2.03 25 2.69 12 6.47 11 1.88 20 3.66 74 16.8 71 1.88 64 2.62 13 3.34 10 1.99 16 4.05 73 11.0 42 3.65 77 4.16 123 7.18 142 4.66 125 1.16 6 3.36 22 0.87 3
2DHMM-SAS [92]49.3 3.19 28 8.89 36 2.71 34 3.20 66 11.5 75 2.38 46 5.19 70 12.2 62 2.73 64 2.92 41 15.2 47 1.53 44 2.79 26 3.65 27 2.27 34 3.45 47 9.34 25 2.78 40 2.66 71 3.56 41 3.07 94 2.34 50 5.12 50 2.97 65
S2D-Matching [84]50.2 3.36 37 9.66 49 2.86 45 3.19 65 11.1 69 2.46 55 4.86 64 12.9 67 2.47 54 2.67 25 13.2 18 1.44 30 2.87 34 3.72 32 2.38 42 3.45 47 9.76 32 2.95 49 3.05 90 3.79 58 3.30 107 1.95 36 4.16 35 3.00 66
FlowFields+ [130]50.3 4.57 87 13.7 98 3.35 67 2.94 51 10.1 45 2.58 66 4.05 48 10.6 44 3.26 78 2.90 40 13.2 18 1.81 62 3.18 56 4.20 65 2.54 57 2.68 13 11.4 46 2.40 16 1.84 12 3.62 49 1.77 25 2.48 51 5.86 55 2.77 59
MLDP_OF [89]50.4 4.13 65 10.3 62 3.60 85 2.34 12 7.70 15 1.88 20 4.23 52 10.9 50 1.87 19 2.74 30 14.6 40 1.37 22 3.10 50 3.91 44 2.48 55 3.40 43 9.00 22 3.79 82 3.46 104 4.20 80 5.55 129 2.31 48 4.64 43 1.98 37
MDP-Flow [26]50.9 3.48 46 9.46 45 3.10 56 2.45 22 7.36 12 2.41 47 3.21 22 8.31 22 2.78 66 3.18 53 17.8 78 1.70 57 3.03 46 3.87 39 2.60 64 3.43 45 12.6 64 2.81 42 2.19 41 3.88 65 1.60 19 4.13 88 9.96 95 3.86 87
AggregFlow [97]51.2 4.25 75 11.9 81 3.26 59 4.46 98 13.7 99 3.43 94 4.76 62 12.4 63 3.93 94 3.28 58 15.6 56 1.68 54 2.89 37 3.89 41 2.08 20 2.32 3 7.75 8 2.14 7 2.06 26 3.77 56 1.48 14 2.07 43 4.11 33 2.36 48
IROF-TV [53]51.5 3.40 42 9.29 43 2.95 51 2.99 52 11.1 69 2.53 60 3.81 40 9.81 36 2.44 52 3.25 57 16.9 73 1.78 61 3.27 72 4.10 58 2.93 82 4.47 82 16.0 97 3.53 71 1.70 6 3.21 7 1.12 4 1.91 35 4.75 45 2.19 41
CombBMOF [113]52.6 3.94 58 10.6 66 2.74 39 2.80 38 8.55 25 2.16 31 3.10 21 7.99 21 1.76 15 2.99 44 13.4 21 1.95 68 3.04 47 3.89 41 2.49 56 5.64 108 12.3 58 6.74 123 3.54 107 5.16 115 2.81 78 1.85 33 4.60 42 1.10 8
S2F-IF [123]53.0 4.51 85 13.6 97 3.31 63 2.90 47 10.4 51 2.48 58 4.07 50 10.8 49 3.15 73 3.31 59 15.7 59 1.90 65 3.17 54 4.19 63 2.55 60 2.81 17 11.6 52 2.60 26 1.86 15 3.67 51 1.87 35 2.11 45 4.64 43 2.54 54
FlowFields [110]55.4 4.57 87 13.7 98 3.38 70 3.01 54 10.6 54 2.59 67 4.19 51 11.1 51 3.30 79 3.17 52 15.0 44 1.96 69 3.21 63 4.24 72 2.61 67 2.91 22 12.4 60 2.66 28 1.84 12 3.46 26 1.84 32 2.50 52 6.15 60 2.79 60
NL-TV-NCC [25]56.9 3.89 56 9.16 40 2.98 52 2.87 45 9.69 40 1.99 22 4.44 58 11.6 55 1.76 15 2.64 21 11.8 12 1.48 39 3.49 87 4.60 96 2.47 51 4.67 92 13.5 72 4.26 96 2.83 79 4.57 96 2.84 81 2.62 57 6.00 59 2.25 44
Sparse Occlusion [54]56.9 3.62 49 9.12 39 2.90 47 2.92 49 9.08 33 2.56 62 4.49 59 11.8 60 2.11 34 3.14 50 15.8 60 1.57 46 3.26 69 4.22 68 2.36 39 3.52 52 10.9 38 2.66 28 5.10 138 6.32 136 3.15 99 2.02 38 4.92 46 1.71 26
EPPM w/o HM [88]57.4 4.25 75 11.1 70 3.13 57 2.36 13 8.35 24 1.76 13 3.72 37 10.2 40 1.81 17 3.24 56 14.5 39 1.94 67 3.16 53 3.94 46 2.82 77 4.78 95 12.9 68 4.32 97 3.64 113 4.54 94 5.73 131 1.76 29 4.11 33 1.94 34
OFH [38]57.6 3.90 57 9.77 51 3.62 88 2.84 41 11.0 68 2.04 26 5.52 77 14.4 79 1.89 21 3.52 66 20.5 97 1.60 50 3.18 56 4.06 55 2.82 77 3.86 65 14.1 81 3.59 73 1.77 8 3.62 49 1.81 28 2.64 58 7.08 72 2.15 39
PGM-C [120]58.0 4.62 92 14.0 103 3.39 72 3.29 75 12.3 80 2.70 74 4.39 57 11.7 58 3.43 82 4.00 82 19.8 86 2.15 74 3.19 58 4.23 69 2.54 57 2.79 15 11.9 55 2.45 18 1.83 10 3.21 7 1.83 30 2.31 48 5.87 56 1.82 33
Occlusion-TV-L1 [63]59.7 3.59 47 9.61 46 2.64 29 2.93 50 10.6 54 2.41 47 6.16 85 15.2 83 2.70 62 3.32 61 17.0 74 1.68 54 3.38 77 4.44 83 2.82 77 3.10 31 13.2 71 2.68 31 2.17 37 3.52 35 1.46 12 4.63 101 11.1 110 3.53 76
Complementary OF [21]60.4 4.44 81 11.2 73 4.04 97 2.51 26 9.77 42 1.74 11 3.93 43 10.6 44 2.04 30 3.87 78 18.8 80 2.19 78 3.17 54 4.00 50 2.92 81 4.64 90 13.8 78 3.64 76 2.17 37 3.36 17 2.51 66 3.08 69 7.04 69 3.65 80
Adaptive [20]61.9 3.29 32 9.43 44 2.28 9 3.10 58 11.4 72 2.46 55 6.58 89 15.7 89 2.52 55 3.14 50 15.6 56 1.56 45 3.67 97 4.46 85 3.48 100 3.32 39 13.0 70 2.38 14 2.76 77 4.39 88 1.93 42 3.58 76 8.18 81 2.88 62
Aniso-Texture [82]62.6 2.96 10 7.72 8 2.54 19 2.48 24 8.26 22 2.24 37 6.48 87 15.9 94 2.63 58 1.96 4 10.1 10 0.98 2 3.26 69 4.21 66 2.60 64 5.74 110 16.9 106 5.61 113 4.47 130 5.88 132 3.33 108 3.51 75 7.12 73 3.68 82
ACK-Prior [27]63.0 4.19 71 9.27 42 3.60 85 2.40 14 8.21 21 1.65 7 3.40 26 8.96 28 1.84 18 2.87 38 14.4 38 1.44 30 3.36 75 4.15 59 3.07 85 6.35 119 16.1 99 4.90 107 4.21 125 4.80 102 6.03 133 3.29 72 5.99 58 2.82 61
CPM-Flow [116]64.1 4.63 93 14.1 106 3.39 72 3.33 76 12.5 84 2.73 76 4.37 54 11.7 58 3.43 82 4.00 82 19.9 89 2.14 73 3.19 58 4.23 69 2.54 57 3.08 29 12.0 56 2.88 46 1.87 16 3.44 23 1.84 32 2.91 65 7.48 78 2.91 64
DPOF [18]64.4 4.67 96 12.6 90 3.30 61 3.57 82 10.6 54 3.12 90 3.09 20 7.50 16 2.32 42 3.06 47 14.8 41 1.82 63 3.21 63 4.18 61 2.79 76 4.47 82 12.5 62 3.33 64 4.09 122 3.92 68 6.96 135 2.09 44 4.39 38 1.74 28
EpicFlow [102]64.5 4.61 91 14.0 103 3.39 72 3.33 76 12.5 84 2.74 77 5.37 73 14.8 82 3.46 85 3.94 81 19.2 83 2.13 72 3.20 60 4.23 69 2.58 63 2.87 20 12.2 57 2.64 27 1.83 10 3.28 13 1.83 30 3.21 71 7.12 73 3.61 77
DeepFlow2 [108]65.8 4.04 62 11.2 73 3.38 70 3.80 85 12.4 83 2.86 82 5.12 68 13.4 71 3.00 69 4.17 88 20.1 91 2.18 77 2.96 41 3.97 48 2.08 20 3.06 28 12.6 64 2.69 32 2.17 37 3.24 10 2.71 73 4.74 103 10.4 103 4.38 100
ROF-ND [107]66.4 4.12 63 10.0 56 3.37 69 2.78 35 8.82 29 2.12 30 4.61 61 11.9 61 2.09 33 2.23 10 6.56 3 1.69 56 3.60 93 4.75 106 2.85 80 4.92 98 13.6 75 3.75 80 4.59 132 5.18 116 4.10 121 2.67 60 5.19 51 3.46 75
TCOF [69]66.8 4.17 68 10.4 64 3.71 91 3.17 60 10.7 61 2.59 67 6.58 89 15.7 89 3.82 92 3.69 76 16.1 65 2.37 87 3.78 101 4.95 118 2.47 51 2.59 8 8.47 15 2.58 23 3.66 115 4.83 103 2.67 71 1.83 32 4.20 36 1.46 20
RFlow [90]68.0 3.82 53 10.0 56 3.44 78 2.61 30 9.73 41 2.02 24 5.66 79 14.5 80 2.05 31 3.93 80 23.1 111 1.90 65 3.24 65 4.19 63 2.66 71 4.12 76 15.2 93 3.34 66 2.61 66 3.56 41 2.65 70 4.48 96 10.5 106 3.93 91
HBM-GC [105]68.0 5.25 104 10.5 65 4.34 104 3.17 60 8.78 28 2.94 86 4.38 55 10.6 44 2.68 61 3.59 70 12.8 15 2.47 90 2.96 41 3.64 26 2.64 69 3.96 71 8.26 11 3.56 72 4.40 128 5.92 133 3.62 112 2.55 54 6.34 63 3.29 70
Steered-L1 [118]69.3 3.30 34 8.44 24 2.91 48 1.89 1 7.14 8 1.60 6 3.61 32 9.91 38 1.89 21 3.45 64 19.4 85 1.64 52 3.42 80 4.30 75 3.39 93 5.18 102 14.5 84 4.37 100 5.09 137 5.05 111 10.1 139 5.56 111 10.2 101 6.24 117
DMF_ROB [140]70.2 4.37 78 12.3 83 3.62 88 3.46 80 12.9 89 2.60 69 5.98 82 15.8 91 3.23 77 4.05 84 19.8 86 2.15 74 3.10 50 4.06 55 2.57 62 3.79 60 14.3 82 3.13 54 1.88 17 3.12 5 1.99 46 4.34 90 10.0 96 3.87 88
SRR-TVOF-NL [91]70.5 4.47 83 10.9 68 3.32 65 4.04 89 13.2 93 2.90 84 4.81 63 12.5 64 3.15 73 3.33 62 15.3 49 1.61 51 3.24 65 4.03 54 2.70 73 3.94 69 11.8 54 3.33 64 4.16 123 5.21 119 3.44 111 2.06 42 3.48 26 2.42 49
ComplOF-FED-GPU [35]70.7 4.28 77 11.3 75 3.70 90 3.25 68 13.0 90 2.16 31 4.06 49 11.2 52 1.95 24 3.91 79 19.2 83 2.01 70 3.20 60 4.15 59 2.64 69 4.61 88 16.1 99 3.90 84 2.98 87 3.77 56 3.69 113 2.85 63 7.44 77 2.53 53
FF++_ROB [146]72.0 4.84 100 14.8 113 3.46 79 3.18 63 11.4 72 2.69 73 5.30 72 14.1 76 3.73 91 3.31 59 14.2 33 2.20 79 3.26 69 4.29 74 2.72 74 4.58 87 12.7 66 3.70 79 1.91 19 3.46 26 2.19 57 3.65 78 7.31 75 5.97 114
TF+OM [100]73.7 3.97 59 10.2 59 2.94 50 2.91 48 9.12 34 2.57 65 5.22 71 11.5 54 6.92 105 3.59 70 16.1 65 2.28 84 3.20 60 3.97 48 3.11 86 4.70 93 14.5 84 4.32 97 3.06 92 4.84 104 2.71 73 3.93 83 8.79 86 4.32 98
Aniso. Huber-L1 [22]74.2 3.71 52 10.1 58 3.08 55 4.36 97 13.0 90 3.77 98 6.92 93 15.3 85 3.60 88 3.54 67 15.9 63 2.04 71 3.38 77 4.45 84 2.47 51 3.88 66 12.9 68 2.74 36 3.37 101 4.36 86 2.85 84 3.16 70 7.52 79 2.90 63
DeepFlow [86]75.5 4.49 84 11.7 78 4.14 99 4.26 93 12.8 87 3.36 92 5.96 81 14.2 78 5.10 98 4.89 102 23.1 111 2.67 93 2.98 43 4.00 50 2.11 24 3.26 35 13.5 72 2.84 45 2.09 32 3.10 3 2.77 75 5.83 113 11.4 112 5.45 112
Classic++ [32]76.2 3.37 39 9.67 50 2.91 48 3.28 73 12.1 78 2.61 72 5.46 76 14.1 76 3.00 69 3.63 72 20.2 94 1.70 57 3.24 65 4.34 77 2.60 64 4.65 91 16.0 97 3.60 74 3.09 93 3.94 71 3.28 106 4.64 102 10.4 103 3.71 83
TV-L1-improved [17]77.0 3.36 37 9.63 47 2.62 26 2.82 39 10.7 61 2.23 35 6.50 88 15.8 91 2.73 64 3.80 77 21.3 102 1.76 60 3.34 74 4.38 81 2.39 43 5.97 112 18.1 115 5.67 114 3.57 109 4.92 109 3.43 110 4.01 86 9.84 94 3.44 74
LocallyOriented [52]79.3 4.54 86 12.8 92 3.27 60 4.73 103 14.8 106 3.73 97 7.77 100 18.3 108 3.44 84 3.56 68 15.6 56 2.22 80 3.46 84 4.47 86 2.69 72 3.15 33 10.2 34 3.19 58 2.61 66 4.20 80 2.52 67 4.39 93 8.52 83 5.23 108
SIOF [67]79.5 4.23 73 10.2 59 3.31 63 3.97 87 14.5 104 2.97 87 7.81 102 16.4 97 7.48 107 4.82 98 20.1 91 2.96 96 3.54 90 4.49 87 3.12 87 4.31 78 13.5 72 4.13 90 2.36 50 3.59 45 1.68 22 3.46 74 7.39 76 3.37 72
LiteFlowNet [143]81.0 6.29 111 16.5 118 4.45 106 3.68 83 10.8 65 3.13 91 5.43 74 13.7 74 3.60 88 3.57 69 12.8 15 2.25 83 3.85 108 4.78 108 3.61 103 4.37 80 12.5 62 3.63 75 2.55 61 4.51 93 1.52 15 4.05 87 7.05 71 5.16 104
Brox et al. [5]82.2 4.44 81 12.4 84 4.22 102 3.72 84 13.5 98 3.06 88 4.97 66 13.3 70 3.11 71 4.58 94 22.0 105 2.37 87 3.79 103 4.60 96 4.33 123 3.91 68 17.0 107 3.45 68 2.22 45 3.79 58 1.19 5 4.62 100 10.0 96 3.38 73
TriangleFlow [30]82.5 4.12 63 10.6 66 3.47 80 3.47 81 13.1 92 2.41 47 6.00 83 15.2 83 2.17 36 2.99 44 16.0 64 1.58 47 4.46 127 5.79 133 4.15 119 5.42 106 13.9 80 5.24 108 3.10 95 5.47 125 2.90 88 3.02 67 6.82 67 3.64 79
CRTflow [80]82.8 4.18 69 11.8 80 3.20 58 3.22 67 10.8 65 2.43 50 6.20 86 15.5 87 2.63 58 4.21 89 22.0 105 2.24 81 3.32 73 4.34 77 2.44 49 7.43 126 19.3 121 8.15 129 2.55 61 4.09 76 2.59 69 4.60 99 11.2 111 4.45 101
OFRF [134]84.0 4.77 99 11.6 76 4.03 96 8.72 121 15.3 110 8.51 124 8.49 112 16.7 99 7.32 106 4.55 93 15.3 49 3.16 103 2.92 39 3.87 39 2.13 27 3.76 59 9.69 29 3.22 60 2.98 87 4.50 92 4.04 119 4.59 98 5.76 54 8.61 125
BriefMatch [124]84.9 3.44 44 9.01 37 2.77 42 2.85 44 9.93 43 2.23 35 2.97 16 7.65 20 1.94 23 3.64 73 20.1 91 1.75 59 4.10 119 4.90 116 5.82 134 7.95 128 17.8 111 8.08 128 4.73 134 5.20 117 12.2 141 7.88 130 12.0 116 13.7 136
Rannacher [23]84.9 4.13 65 11.0 69 3.61 87 3.39 78 12.3 80 2.80 80 7.26 95 17.4 104 3.59 87 4.40 91 23.1 111 2.24 81 3.43 82 4.54 93 2.56 61 5.41 105 18.5 116 4.23 94 2.92 84 3.91 67 2.82 79 3.45 73 9.14 87 3.27 69
F-TV-L1 [15]85.9 5.44 107 12.5 89 5.69 114 5.46 107 15.0 109 4.03 101 7.48 97 16.3 96 3.42 81 5.08 104 23.3 114 2.81 95 3.42 80 4.34 77 3.03 83 4.05 73 15.1 91 3.18 57 2.43 54 3.92 68 1.87 35 3.90 82 9.35 91 2.61 56
SuperFlow [81]87.0 4.16 67 11.1 70 3.32 65 4.80 104 12.2 79 4.68 106 7.80 101 16.0 95 10.6 118 5.16 107 22.4 109 3.24 104 3.39 79 4.24 72 3.71 106 3.44 46 13.7 77 2.91 47 3.19 98 4.62 98 1.87 35 4.74 103 10.6 107 4.24 95
Local-TV-L1 [65]87.2 5.33 105 12.6 90 5.19 111 6.90 114 15.7 113 6.22 113 10.0 117 18.2 107 8.89 109 5.81 113 24.7 119 3.70 110 3.05 48 4.00 50 2.39 43 4.05 73 14.6 86 3.09 53 1.95 21 3.11 4 2.15 53 5.85 114 10.8 108 7.34 120
TriFlow [95]87.2 4.73 98 12.4 84 3.49 82 4.03 88 12.5 84 3.70 96 8.18 109 17.2 102 10.4 117 3.50 65 15.4 51 2.32 86 3.43 82 4.21 66 3.42 94 3.90 67 12.3 58 3.76 81 7.86 143 5.72 128 16.2 143 2.80 62 5.89 57 2.50 51
DF-Auto [115]87.4 5.04 103 13.7 98 3.30 61 6.51 111 14.1 103 6.09 111 8.14 106 16.5 98 10.2 116 5.06 103 21.3 102 3.10 102 3.74 99 4.91 117 3.25 90 2.67 12 11.4 46 2.14 7 3.36 100 5.23 121 1.45 11 4.45 95 9.18 88 4.28 97
CLG-TV [48]88.1 4.00 60 10.3 62 3.40 75 4.33 96 12.3 80 4.08 102 6.78 91 15.5 87 3.64 90 4.07 85 17.7 77 2.39 89 3.79 103 4.86 111 3.23 89 4.48 84 16.5 104 3.80 83 3.55 108 4.65 99 2.89 87 4.00 85 10.1 99 3.18 68
CBF [12]89.0 3.88 55 10.2 59 3.50 83 4.60 100 11.3 71 5.06 107 5.43 74 13.1 68 3.39 80 4.09 86 21.2 101 2.16 76 3.80 106 4.72 105 3.52 101 4.33 79 14.4 83 3.01 51 4.97 135 5.51 126 4.93 127 3.99 84 9.27 90 3.91 90
Bartels [41]91.3 4.43 79 11.1 70 4.17 101 2.83 40 8.84 30 2.56 62 4.54 60 12.5 64 2.80 67 4.87 99 22.1 107 3.05 100 3.58 92 4.35 80 4.15 119 5.55 107 17.5 109 5.78 115 3.74 116 5.02 110 5.98 132 5.21 110 11.9 115 5.20 107
Fusion [6]91.7 4.43 79 13.7 98 4.08 98 2.47 23 8.91 31 2.24 37 3.70 35 9.68 34 3.12 72 3.68 75 19.8 86 2.54 92 4.26 124 5.16 125 4.31 122 6.32 116 16.8 105 6.15 119 4.55 131 5.78 130 3.10 95 7.12 124 13.6 125 7.86 124
p-harmonic [29]91.9 4.64 94 13.0 93 4.43 105 3.41 79 11.9 76 2.93 85 7.60 98 18.1 106 3.96 95 4.65 95 21.0 99 2.97 98 3.46 84 4.33 76 3.34 92 4.75 94 17.5 109 4.60 104 3.05 90 4.17 78 2.15 53 5.09 109 10.9 109 3.77 85
CNN-flow-warp+ref [117]92.8 4.93 102 14.5 110 4.29 103 4.18 91 11.9 76 4.24 104 8.23 110 19.7 116 6.35 104 5.13 105 24.4 118 2.96 96 3.55 91 4.40 82 3.85 109 3.82 62 15.0 88 3.39 67 1.96 22 3.44 23 2.14 52 10.0 134 14.8 131 10.8 132
Dynamic MRF [7]93.8 4.58 89 12.4 84 4.14 99 3.25 68 13.9 100 2.27 41 6.02 84 16.8 100 2.36 44 4.39 90 22.6 110 2.51 91 3.61 94 4.55 94 3.46 96 6.81 121 22.2 131 6.78 125 2.41 52 3.48 30 3.69 113 9.26 132 17.8 135 10.2 129
SegOF [10]95.0 5.85 109 13.5 96 3.98 95 7.40 115 14.9 107 8.13 122 8.55 113 17.3 103 9.01 110 6.50 118 18.1 79 5.14 120 3.90 112 4.53 91 4.81 128 6.57 120 21.7 129 6.81 126 1.65 5 3.49 32 1.08 3 3.71 79 9.23 89 3.63 78
FlowNetS+ft+v [112]95.0 4.22 72 12.1 82 3.48 81 4.50 99 13.4 95 3.85 99 8.29 111 18.4 109 6.20 103 4.87 99 21.6 104 3.01 99 3.93 113 5.04 121 3.47 99 3.71 57 15.3 94 3.21 59 3.32 99 5.12 113 3.87 115 3.76 80 9.44 92 3.74 84
ResPWCR_ROB [145]95.4 7.29 119 16.3 117 6.15 117 4.28 94 11.4 72 3.95 100 5.85 80 13.6 73 5.20 100 4.75 97 17.5 76 3.50 107 3.80 106 4.53 91 4.12 118 4.96 101 15.0 88 4.81 106 3.52 106 5.22 120 2.40 62 3.61 77 6.77 66 4.27 96
LDOF [28]95.6 4.60 90 13.0 93 3.77 92 4.67 101 15.5 112 3.67 95 5.63 78 14.0 75 4.21 96 5.80 112 27.1 128 3.43 106 3.52 89 4.50 89 3.46 96 4.84 97 17.8 111 4.04 88 2.46 58 4.14 77 3.25 103 4.85 106 12.0 116 3.78 86
Second-order prior [8]96.3 4.03 61 11.6 76 3.35 67 3.88 86 14.0 102 3.08 89 7.21 94 17.6 105 3.57 86 4.14 87 19.9 89 2.31 85 3.66 96 4.86 111 2.73 75 7.32 124 21.2 127 6.76 124 4.02 119 4.58 97 4.01 117 4.27 89 10.4 103 5.12 103
WOLF_ROB [149]97.1 5.79 108 16.6 119 4.49 107 7.62 116 21.2 130 5.10 109 9.70 116 21.0 120 5.66 102 5.32 109 19.0 81 3.78 112 3.61 94 4.49 87 3.54 102 4.63 89 13.6 75 4.34 99 2.30 48 3.89 66 2.16 56 4.37 91 7.52 79 6.03 115
AugFNG_ROB [144]97.6 8.29 125 19.2 126 5.66 113 7.67 117 16.0 115 8.01 121 10.1 118 20.5 118 11.0 120 5.13 105 15.5 54 3.64 109 4.11 120 4.97 119 3.93 110 4.45 81 15.1 91 4.20 92 2.27 46 4.37 87 1.23 8 3.80 81 6.87 68 4.34 99
ContFlow_ROB [150]98.1 9.09 129 19.9 129 6.50 122 6.19 110 13.4 95 6.15 112 9.17 115 18.8 111 9.23 111 5.18 108 15.2 47 3.74 111 4.69 131 5.58 129 4.78 127 6.30 115 17.2 108 6.52 122 2.47 59 4.66 100 1.34 9 2.67 60 4.94 47 2.35 47
FlowNet2 [122]101.0 8.58 128 18.6 123 6.31 118 9.39 126 17.6 119 9.09 127 8.06 105 15.8 91 9.81 114 5.61 111 16.2 67 4.12 114 4.04 116 4.88 113 3.79 107 4.92 98 16.2 101 4.50 101 4.28 126 6.73 138 2.84 81 2.05 41 4.54 40 1.41 18
StereoFlow [44]101.5 17.1 145 28.1 145 17.9 144 18.7 142 29.7 143 16.5 137 20.1 142 30.9 141 17.5 137 21.2 142 38.3 144 17.9 140 4.60 128 5.05 122 5.52 130 2.38 4 11.5 50 1.77 2 1.25 1 2.92 2 0.71 1 4.49 97 10.3 102 4.23 94
EPMNet [133]102.0 8.37 127 18.8 125 6.44 120 9.35 125 18.4 121 8.78 126 7.42 96 14.7 81 8.61 108 5.98 115 20.4 96 4.27 116 4.04 116 4.88 113 3.79 107 4.92 98 16.2 101 4.50 101 3.65 114 6.14 134 2.42 64 2.60 56 6.15 60 1.74 28
Ad-TV-NDC [36]103.1 8.36 126 14.0 103 11.1 137 12.9 133 19.9 127 12.8 133 14.4 129 23.1 122 12.1 123 7.40 121 20.6 98 6.33 121 3.47 86 4.66 101 2.39 43 3.95 70 13.8 78 3.51 70 2.48 60 3.75 54 2.05 48 9.75 133 12.1 118 16.7 140
LFNet_ROB [151]103.8 7.69 120 19.8 127 5.72 115 4.70 102 13.3 94 4.13 103 8.15 108 20.0 117 5.42 101 4.73 96 17.1 75 3.42 105 4.15 121 5.10 124 4.05 114 5.28 104 18.0 113 4.64 105 2.87 81 4.74 101 1.98 45 4.92 107 11.4 112 5.01 102
Shiralkar [42]105.6 4.64 94 14.1 106 3.94 93 4.29 95 16.9 117 2.77 78 7.75 99 18.8 111 3.19 76 5.54 110 25.0 121 3.56 108 3.51 88 4.55 94 3.04 84 7.41 125 20.1 125 6.41 120 3.76 117 4.35 85 5.28 128 6.56 120 14.4 130 5.30 110
Learning Flow [11]106.0 4.23 73 11.7 78 3.41 77 4.16 90 15.3 110 3.42 93 6.78 91 16.9 101 3.83 93 6.41 117 25.3 122 4.25 115 4.66 130 6.01 138 4.00 113 6.33 118 20.7 126 5.30 109 3.09 93 4.84 104 2.91 90 7.08 123 15.0 132 5.27 109
StereoOF-V1MT [119]106.5 4.71 97 14.1 106 3.95 94 5.10 106 20.3 129 2.78 79 7.98 104 20.7 119 2.57 57 4.48 92 21.1 100 2.79 94 4.20 123 5.29 127 4.10 116 6.85 123 22.3 132 6.42 121 2.45 57 4.17 78 3.15 99 10.5 135 18.4 138 10.5 130
IAOF2 [51]107.6 5.38 106 13.7 98 4.50 108 5.95 109 14.6 105 5.61 110 8.80 114 18.8 111 9.40 112 12.2 131 23.8 117 13.1 135 3.86 109 4.89 115 3.12 87 5.21 103 14.9 87 4.54 103 4.33 127 5.15 114 3.93 116 4.39 93 8.57 84 3.87 88
TVL1_ROB [139]108.9 11.3 134 19.8 127 13.0 139 12.9 133 19.6 126 13.7 135 17.4 136 27.8 135 18.0 138 12.6 133 28.9 130 11.8 133 3.71 98 4.78 108 3.46 96 4.21 77 18.0 113 3.99 86 1.79 9 3.54 39 1.21 6 7.58 128 13.9 128 8.92 127
Modified CLG [34]109.7 7.17 118 17.1 122 6.47 121 6.85 113 14.9 107 7.48 117 14.0 125 24.8 126 15.7 133 8.35 124 27.3 129 6.36 122 3.96 114 4.99 120 4.08 115 4.54 86 19.3 121 4.15 91 2.33 49 3.86 64 2.40 62 6.00 115 13.8 127 5.40 111
GraphCuts [14]110.5 6.25 110 14.3 109 5.53 112 8.60 120 20.1 128 6.61 115 7.91 103 15.4 86 10.9 119 4.88 101 19.0 81 3.05 100 3.78 101 4.71 103 3.94 111 8.74 133 16.4 103 5.39 111 4.04 120 4.87 106 4.85 126 6.35 118 12.2 119 6.05 116
2D-CLG [1]110.6 10.1 131 22.6 136 7.59 127 9.84 128 16.9 117 11.1 132 16.9 135 28.2 136 18.8 141 14.1 135 31.1 134 13.1 135 3.86 109 4.62 99 4.53 124 5.98 113 21.2 127 5.97 117 1.76 7 3.14 6 1.46 12 6.29 117 12.9 124 5.81 113
Filter Flow [19]110.6 6.48 112 14.6 111 4.96 109 5.73 108 15.7 113 5.07 108 10.1 118 18.6 110 14.3 129 9.04 126 23.3 114 7.80 126 3.98 115 4.71 103 4.21 121 5.86 111 15.0 88 5.41 112 4.98 136 6.87 139 2.78 76 4.82 105 8.66 85 3.65 80
SPSA-learn [13]111.4 6.84 117 16.7 120 6.74 123 8.47 119 19.4 124 7.49 118 12.5 121 23.1 122 13.1 127 8.40 125 25.8 125 7.08 124 3.87 111 4.66 101 4.10 116 6.32 116 18.8 117 6.89 127 2.56 63 3.85 63 1.79 26 7.29 125 12.5 121 7.47 122
HBpMotionGpu [43]112.8 6.57 114 15.0 114 5.17 110 8.29 118 18.0 120 8.29 123 14.1 126 26.5 129 13.2 128 6.12 116 25.3 122 3.94 113 3.79 103 4.62 99 3.97 112 4.80 96 15.7 95 4.11 89 4.40 128 5.20 117 2.87 86 6.28 116 11.7 114 7.31 119
IAOF [50]113.7 6.49 113 14.6 111 6.42 119 9.22 124 18.5 122 7.94 120 16.4 134 27.4 133 13.0 126 8.22 122 22.2 108 7.73 125 3.77 100 4.76 107 3.42 94 6.84 122 18.8 117 4.23 94 3.59 110 4.46 90 2.83 80 7.51 127 10.1 99 10.6 131
GroupFlow [9]114.0 8.00 122 18.6 123 8.09 129 11.1 131 23.7 135 10.3 130 12.6 122 25.6 127 12.8 125 5.84 114 20.3 95 4.39 117 4.69 131 5.81 134 3.67 104 9.29 134 22.4 133 10.1 136 2.11 35 3.99 72 2.29 59 5.75 112 10.0 96 7.39 121
Black & Anandan [4]114.7 6.81 116 15.4 115 7.43 125 8.77 122 19.5 125 7.35 116 13.0 123 22.9 121 12.5 124 8.29 123 26.1 126 6.77 123 4.18 122 5.28 126 3.69 105 6.19 114 20.0 124 5.34 110 3.63 111 5.05 111 1.79 26 6.45 119 12.2 119 5.17 106
BlockOverlap [61]117.8 6.67 115 13.1 95 5.87 116 6.62 112 13.9 100 6.53 114 10.6 120 19.5 115 10.1 115 6.97 120 24.9 120 5.13 119 4.38 125 4.61 98 6.37 137 7.47 127 15.7 95 6.05 118 6.23 139 6.41 137 13.0 142 6.92 122 9.60 93 12.2 134
Nguyen [33]118.2 7.88 121 16.8 121 7.02 124 13.4 135 19.0 123 15.3 136 17.6 137 28.9 137 17.2 136 12.0 130 26.9 127 11.6 132 4.38 125 5.07 123 5.58 133 5.69 109 19.7 123 5.93 116 2.75 75 4.02 74 1.91 41 6.59 121 12.5 121 6.52 118
UnFlow [129]119.3 14.6 143 25.8 141 9.09 133 9.40 127 16.8 116 9.89 129 14.2 127 26.9 130 11.2 121 10.0 127 25.4 124 8.67 128 5.43 138 5.90 135 6.72 138 8.64 131 24.0 135 9.41 134 3.51 105 4.90 107 1.37 10 4.37 91 12.6 123 3.33 71
2bit-BM-tele [98]120.0 8.00 122 15.8 116 8.40 131 4.91 105 13.4 95 4.67 105 8.14 106 19.0 114 5.12 99 6.62 119 23.5 116 5.04 118 4.08 118 4.78 108 4.61 126 8.68 132 18.8 117 8.31 130 6.46 141 7.08 141 9.47 138 7.36 126 14.1 129 9.62 128
Horn & Schunck [3]124.8 8.01 124 19.9 129 8.38 130 9.13 123 23.2 134 7.71 119 14.2 127 25.9 128 14.6 131 12.4 132 30.6 132 11.3 131 4.64 129 5.64 130 4.60 125 8.21 130 24.4 136 8.45 131 4.01 118 5.41 122 1.95 43 9.16 131 17.5 133 8.86 126
SILK [79]126.3 9.34 130 20.4 131 10.5 136 10.4 129 21.9 131 10.3 130 16.0 133 27.5 134 14.5 130 10.3 128 29.0 131 8.54 127 4.81 133 5.65 131 5.56 132 9.41 135 25.4 138 8.74 132 2.79 78 3.68 52 4.62 124 10.9 136 17.8 135 12.3 135
Heeger++ [104]128.0 11.9 137 21.8 134 8.08 128 12.5 132 29.7 143 9.42 128 14.8 130 27.1 131 9.68 113 14.3 136 31.0 133 12.7 134 4.98 135 5.74 132 4.97 129 17.5 143 34.1 144 18.4 143 2.75 75 5.44 123 2.15 53 12.3 138 18.8 139 14.8 138
TI-DOFE [24]128.8 13.4 141 23.2 137 16.5 143 16.5 139 24.1 136 18.2 141 20.2 143 31.1 143 20.6 142 19.9 141 32.9 137 20.8 142 4.89 134 5.90 135 5.54 131 8.04 129 23.9 134 8.81 133 2.97 86 4.34 84 1.88 38 10.9 136 17.7 134 11.9 133
H+S_ROB [138]130.2 13.0 139 27.1 144 9.66 134 13.4 135 24.8 138 13.4 134 18.7 141 30.9 141 18.3 140 25.8 145 35.7 141 26.4 144 7.08 143 8.13 143 9.10 142 14.6 141 31.3 143 16.3 141 2.17 37 4.44 89 2.11 51 15.1 142 19.9 141 14.2 137
HCIC-L [99]132.9 15.7 144 22.0 135 10.1 135 31.5 145 26.6 141 41.0 145 14.8 130 23.1 122 16.8 135 18.4 140 34.4 139 18.2 141 5.94 139 6.35 139 6.35 136 10.6 138 19.2 120 11.4 138 18.7 145 17.8 145 19.2 144 4.93 108 8.34 82 5.16 104
SLK [47]133.1 11.6 135 26.0 142 14.6 142 15.3 138 25.0 139 17.5 139 17.8 139 30.1 140 18.1 139 25.4 144 33.6 138 28.0 145 5.25 136 5.90 135 7.03 139 10.3 137 27.4 140 10.6 137 2.89 83 4.47 91 2.94 92 14.9 141 20.7 142 18.8 141
FFV1MT [106]134.0 12.0 138 23.3 138 8.83 132 10.7 130 26.6 141 8.71 125 15.6 132 29.0 138 12.0 122 16.6 139 36.3 143 15.5 138 6.51 142 6.40 140 10.4 143 16.2 142 30.7 142 17.7 142 3.41 103 5.44 123 3.35 109 12.3 138 18.8 139 14.8 138
Adaptive flow [45]135.9 13.2 140 20.8 132 14.0 141 17.1 141 22.0 132 17.9 140 18.1 140 27.1 131 22.8 144 11.8 129 31.1 134 10.5 129 6.35 141 7.13 142 6.25 135 9.87 136 21.8 130 9.44 135 12.6 144 11.4 144 20.0 145 7.75 129 13.6 125 7.73 123
PGAM+LK [55]137.1 11.8 136 25.6 139 13.9 140 14.8 137 24.4 137 16.7 138 13.2 124 24.0 125 15.0 132 16.2 138 41.2 145 15.3 137 5.40 137 5.45 128 8.10 140 12.3 140 26.5 139 12.1 139 7.42 142 8.24 143 7.87 136 13.2 140 18.3 137 19.4 142
Periodicity [78]138.1 11.2 133 27.0 143 7.46 126 16.6 140 29.8 145 18.2 141 25.3 145 31.2 145 24.9 145 12.7 134 35.7 141 11.1 130 31.7 145 41.4 145 25.1 145 23.8 145 41.5 145 23.8 145 2.92 84 5.62 127 6.90 134 18.6 144 33.1 145 22.3 143
FOLKI [16]138.9 10.5 132 25.6 139 11.9 138 20.9 143 26.2 140 26.1 143 17.6 137 31.1 143 16.5 134 15.4 137 32.6 136 16.0 139 6.16 140 6.53 141 9.07 141 12.2 139 29.7 141 13.0 140 4.67 133 5.83 131 9.41 137 18.2 143 22.8 143 25.1 144
Pyramid LK [2]141.8 13.9 142 20.9 133 21.4 145 24.1 144 23.1 133 30.2 144 20.9 144 29.5 139 21.9 143 22.2 143 34.6 140 25.0 143 18.7 144 23.1 144 20.2 144 21.2 144 24.5 137 21.0 144 6.41 140 7.02 140 10.8 140 25.6 145 31.5 144 34.5 145
AdaConv-v1 [126]146.4 39.2 146 39.9 146 41.8 146 73.0 146 74.5 146 71.1 146 70.1 146 67.3 146 71.8 146 64.4 146 66.2 146 65.9 146 76.5 147 78.1 147 72.0 147 68.2 147 64.9 147 66.5 147 52.3 147 45.1 147 70.9 147 81.8 146 81.6 146 82.3 146
SepConv-v1 [127]146.4 39.2 146 39.9 146 41.8 146 73.0 146 74.5 146 71.1 146 70.1 146 67.3 146 71.8 146 64.4 146 66.2 146 65.9 146 76.5 147 78.1 147 72.0 147 68.2 147 64.9 147 66.5 147 52.3 147 45.1 147 70.9 147 81.8 146 81.6 146 82.3 146
SuperSlomo [132]146.4 39.2 146 39.9 146 41.8 146 73.0 146 74.5 146 71.1 146 70.1 146 67.3 146 71.8 146 64.4 146 66.2 146 65.9 146 76.5 147 78.1 147 72.0 147 68.2 147 64.9 147 66.5 147 52.3 147 45.1 147 70.9 147 81.8 146 81.6 146 82.3 146
FGIK [136]146.4 39.2 146 39.9 146 41.8 146 73.0 146 74.5 146 71.1 146 70.1 146 67.3 146 71.8 146 64.4 146 66.2 146 65.9 146 76.5 147 78.1 147 72.0 147 68.2 147 64.9 147 66.5 147 52.3 147 45.1 147 70.9 147 81.8 146 81.6 146 82.3 146
CtxSyn [137]146.4 39.2 146 39.9 146 41.8 146 73.0 146 74.5 146 71.1 146 70.1 146 67.3 146 71.8 146 64.4 146 66.2 146 65.9 146 76.5 147 78.1 147 72.0 147 68.2 147 64.9 147 66.5 147 52.3 147 45.1 147 70.9 147 81.8 146 81.6 146 82.3 146
AVG_FLOW_ROB [142]149.1 62.1 151 56.6 151 61.5 151 99.9 151 96.7 151 99.9 151 81.2 151 81.9 151 80.3 151 65.8 151 68.9 151 67.4 151 68.4 146 75.2 146 67.5 146 62.4 146 55.3 146 59.6 146 31.5 146 28.0 146 29.3 146 86.1 151 96.7 151 87.2 151
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] ContFlow_ROB 0.45 all color Anonymous. Continual Flow. ROB 2018 submission.
[151] LFNet_ROB 0.068 2 color Anonymous. Learning a flow network. ROB 2018 submission.
* 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.