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 27 3.36 18 1.62 21 0.99 1 2.16 3 0.57 2
NN-field [71]11.2 2.89 8 8.13 16 2.11 5 2.10 7 7.15 9 1.77 16 2.27 4 5.59 5 1.61 9 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 58 3.70 56 1.95 45 1.01 2 2.25 4 0.53 1
OFLAF [77]14.4 3.04 15 7.80 10 2.40 14 2.14 8 7.02 5 1.72 10 2.25 3 5.32 2 1.56 6 2.62 21 13.7 28 1.37 23 2.35 4 3.13 6 1.62 4 2.98 24 7.73 7 2.57 23 2.08 33 3.27 12 2.05 50 1.33 13 2.43 7 1.40 17
PMMST [114]15.5 3.42 44 7.60 5 2.65 33 2.32 12 6.39 1 2.20 35 2.63 11 6.08 8 2.03 29 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 29 3.53 39 1.63 22 1.27 10 2.29 5 1.02 7
nLayers [57]17.9 2.80 6 7.42 3 2.20 8 2.71 32 7.24 10 2.55 62 2.61 9 6.24 9 2.45 55 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 50 3.46 28 1.89 42 1.38 15 3.06 18 1.29 15
MDP-Flow2 [68]20.8 3.23 32 7.93 13 2.60 25 1.92 2 6.64 2 1.52 1 2.46 7 5.91 7 1.56 6 3.05 48 15.8 61 1.51 42 2.77 25 3.50 18 2.16 29 2.86 20 8.58 18 2.70 34 2.00 27 3.50 36 1.59 19 1.28 11 2.67 12 0.89 4
ComponentFusion [96]21.0 2.78 5 8.20 17 2.05 4 2.04 5 7.31 11 1.66 9 2.55 8 6.78 13 1.61 9 2.24 12 13.1 18 1.01 3 2.71 21 3.56 21 2.10 24 3.55 55 12.4 63 3.22 61 2.19 44 3.60 48 1.54 18 1.32 12 2.91 14 1.13 9
TC/T-Flow [76]23.3 2.69 3 7.75 9 1.87 2 2.76 35 10.2 49 1.73 11 3.33 26 9.01 32 1.49 2 2.86 37 16.7 71 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 3.14 102 2.03 40 4.53 40 1.49 21
FC-2Layers-FF [74]27.4 3.02 14 7.87 12 2.61 26 2.72 33 9.35 37 2.29 43 2.36 5 5.47 4 2.15 36 2.48 14 12.6 14 1.28 14 2.49 7 3.19 7 2.03 17 3.39 43 8.92 20 2.83 45 2.83 82 3.92 72 2.80 81 1.25 8 2.57 11 1.20 12
WLIF-Flow [93]28.6 2.96 10 7.67 6 2.40 14 2.41 17 7.70 15 2.10 29 2.98 18 7.63 20 1.97 28 2.71 29 13.5 24 1.33 17 3.01 46 4.00 52 2.40 48 3.03 27 8.32 12 2.44 17 2.09 35 3.36 18 2.04 49 2.26 48 4.97 50 2.59 57
Layers++ [37]29.0 3.11 18 8.22 20 2.79 45 2.43 20 7.02 5 2.24 38 2.43 6 5.77 6 2.18 39 2.13 9 9.71 10 1.15 7 2.35 4 3.02 3 1.96 11 3.81 63 11.4 47 3.22 61 2.74 77 4.01 77 2.35 64 1.45 16 3.05 17 1.79 32
HAST [109]29.6 2.58 1 7.12 1 1.81 1 2.41 17 7.05 7 2.10 29 1.83 1 4.19 1 1.17 1 2.84 36 15.5 55 1.08 5 2.23 1 2.97 2 1.40 1 3.72 60 10.0 33 3.92 87 3.40 105 4.90 111 5.66 134 1.20 7 2.09 1 1.24 13
AGIF+OF [85]30.7 3.06 16 8.20 17 2.55 23 3.17 61 10.6 56 2.46 56 3.46 31 8.97 31 2.24 42 2.61 19 13.7 28 1.33 17 2.63 15 3.46 15 2.11 25 2.88 22 8.34 14 2.35 13 2.10 37 3.56 43 2.09 52 1.80 30 3.68 30 2.24 43
Efficient-NL [60]30.9 2.99 13 8.23 21 2.28 9 2.72 33 8.95 33 2.25 41 3.81 41 9.87 38 2.07 33 2.77 33 14.3 37 1.46 37 2.61 12 3.48 16 1.96 11 3.31 39 8.33 13 2.59 25 2.60 68 3.75 57 2.54 71 1.60 22 3.02 15 1.66 24
FESL [72]31.0 2.96 10 7.70 7 2.54 21 3.26 72 10.4 53 2.56 63 3.25 24 8.39 24 2.17 37 2.56 16 13.2 19 1.31 16 2.57 9 3.40 11 2.12 27 2.60 9 7.65 5 2.30 11 2.64 73 4.22 86 2.47 68 1.75 28 3.49 27 1.71 27
LME [70]31.0 3.15 23 8.04 15 2.31 11 1.95 3 6.65 3 1.59 4 4.03 47 9.31 33 4.57 100 2.69 27 13.6 26 1.42 28 2.85 32 3.61 24 2.42 50 3.47 51 12.8 70 3.17 57 2.12 39 3.53 39 1.73 24 1.34 14 2.75 13 1.18 11
ALD-Flow [66]31.3 2.82 7 7.86 11 2.16 6 2.84 42 10.1 46 1.86 19 3.73 39 10.4 42 1.67 13 3.10 50 16.8 72 1.28 14 2.69 20 3.60 23 1.85 8 2.79 15 11.3 46 2.32 12 2.07 31 3.25 11 3.10 99 2.03 40 5.11 51 1.94 35
RNLOD-Flow [121]32.3 2.66 2 7.33 2 2.17 7 2.53 28 9.46 39 1.86 19 3.94 45 10.7 48 1.95 25 2.50 15 13.5 24 1.21 10 2.68 18 3.62 26 2.05 19 2.99 25 8.59 19 2.75 38 3.00 92 4.54 98 3.25 107 1.48 18 3.24 21 1.76 31
IROF++ [58]32.8 3.17 26 8.69 31 2.61 26 2.79 37 9.61 40 2.33 44 3.43 28 8.86 28 2.38 49 2.87 40 14.8 42 1.52 44 2.74 23 3.57 22 2.19 31 3.20 35 9.70 30 2.71 35 1.96 25 3.45 27 1.22 9 1.80 30 4.06 32 2.50 53
ProFlow_ROB [146]33.2 3.29 33 9.91 57 2.35 13 2.50 26 10.0 45 1.83 17 4.04 48 11.6 56 1.96 27 2.86 37 15.0 45 1.22 12 2.87 35 3.89 43 1.97 14 2.60 9 10.5 37 2.20 10 1.53 4 3.54 41 1.53 17 2.50 55 6.37 67 2.33 48
NNF-EAC [103]33.3 3.31 36 8.21 19 2.68 35 2.19 10 7.49 13 1.76 14 2.73 13 6.62 12 1.70 15 3.18 55 15.8 61 1.64 54 2.87 35 3.66 29 2.24 33 3.02 26 8.07 10 2.59 25 2.19 44 3.48 32 1.74 25 2.85 66 6.52 68 3.12 69
PH-Flow [101]33.5 3.19 29 8.87 36 2.71 36 2.84 42 9.33 36 2.37 46 2.85 15 7.20 15 2.36 46 2.92 43 15.4 52 1.51 42 2.63 15 3.42 12 2.04 18 3.03 27 8.52 17 2.49 19 2.69 75 3.60 48 3.13 101 1.25 8 2.53 9 1.34 16
Classic+CPF [83]34.0 3.14 21 8.60 28 2.63 30 3.03 58 10.6 56 2.33 44 3.66 35 9.58 34 2.20 40 2.61 19 14.1 33 1.34 20 2.68 18 3.53 19 2.21 32 2.85 19 7.95 9 2.38 14 2.44 60 3.49 34 2.90 92 1.67 26 3.40 24 2.43 51
Sparse-NonSparse [56]35.8 3.14 21 8.75 33 2.76 43 3.02 56 10.6 56 2.43 51 3.45 30 8.96 29 2.36 46 2.66 24 13.7 28 1.42 28 2.85 32 3.75 37 2.33 38 3.28 38 9.40 26 2.73 36 2.42 57 3.31 14 2.69 76 1.47 17 3.07 19 1.66 24
TC-Flow [46]37.3 2.91 9 8.00 14 2.34 12 2.18 9 8.77 28 1.52 1 3.84 43 10.7 48 1.49 2 3.13 51 16.6 70 1.46 37 2.78 26 3.73 36 1.96 11 3.08 30 11.4 47 2.66 29 1.94 23 3.43 24 3.20 106 3.06 71 7.04 73 4.08 95
3DFlow [135]37.7 3.44 45 8.63 30 2.46 17 2.43 20 8.59 27 1.75 13 3.71 37 9.93 40 1.64 11 1.61 3 4.58 1 1.23 13 2.86 34 3.72 34 2.16 29 4.52 88 11.6 53 4.20 95 3.16 100 4.02 78 4.44 126 1.13 5 2.14 2 0.89 4
LSM [39]38.2 3.12 19 8.62 29 2.75 42 3.00 54 10.5 55 2.44 53 3.43 28 8.85 27 2.35 45 2.66 24 13.6 26 1.44 31 2.82 28 3.68 30 2.36 41 3.38 42 9.41 27 2.81 43 2.69 75 3.52 37 2.84 85 1.59 21 3.38 23 1.80 33
SVFilterOh [111]39.0 3.63 51 8.82 34 2.86 47 2.60 30 8.06 18 2.05 28 2.95 16 7.09 14 2.03 29 2.80 35 13.8 31 1.41 27 2.63 15 3.42 12 1.75 7 3.49 52 10.3 35 3.23 64 3.63 114 5.75 133 4.47 127 1.09 4 2.45 8 0.92 6
Ramp [62]39.2 3.18 28 8.83 35 2.73 39 2.89 47 10.1 46 2.44 53 3.27 25 8.43 25 2.38 49 2.74 31 14.2 34 1.46 37 2.82 28 3.69 33 2.29 36 3.37 41 9.31 24 2.93 49 2.62 71 3.38 22 3.19 105 1.54 20 3.21 20 2.24 43
Correlation Flow [75]39.3 3.38 42 8.40 23 2.64 31 2.23 11 7.54 14 1.56 3 5.14 71 13.1 70 1.60 8 2.09 8 8.15 6 1.35 22 3.12 54 4.09 59 2.34 39 4.01 74 11.5 51 4.00 89 2.59 67 3.61 50 3.00 97 1.49 19 3.04 16 1.42 19
ProbFlowFields [128]40.1 4.18 71 12.4 86 3.40 78 2.43 20 8.16 20 2.19 34 3.65 34 9.72 36 2.86 70 2.22 10 9.42 9 1.42 28 3.01 46 3.96 49 2.36 41 2.73 14 10.9 39 2.51 20 1.89 21 3.39 23 1.82 30 2.59 58 6.21 65 2.75 60
PMF [73]40.2 3.61 49 9.07 39 2.62 28 2.40 15 8.05 17 1.83 17 2.61 9 6.27 10 1.65 12 3.35 65 15.4 52 1.58 49 2.54 8 3.27 8 1.71 6 3.59 56 11.1 44 3.46 71 4.07 124 6.18 140 4.02 122 1.06 3 2.38 6 1.25 14
COFM [59]40.4 3.17 26 9.90 56 2.46 17 2.41 17 8.34 24 1.92 22 3.77 40 10.5 43 2.54 58 2.71 29 14.9 44 1.19 9 3.08 51 3.92 47 3.25 94 3.83 66 10.9 39 3.15 56 2.20 47 3.35 16 2.91 94 1.62 25 2.56 10 2.09 39
JOF [140]41.5 3.08 17 8.56 26 2.51 20 3.27 73 10.2 49 2.81 84 3.02 20 7.55 17 2.42 53 2.64 22 14.2 34 1.34 20 2.62 13 3.42 12 2.08 20 3.26 36 8.96 21 2.56 21 3.12 99 4.26 87 4.09 124 2.11 46 4.58 42 2.18 41
FMOF [94]41.7 3.12 19 8.23 21 2.73 39 3.25 69 10.7 63 2.52 60 3.01 19 7.61 18 2.20 40 2.56 16 13.4 22 1.33 17 2.75 24 3.61 24 2.24 33 3.66 58 8.50 16 2.78 41 2.62 71 3.84 65 3.27 109 2.66 63 5.69 56 1.95 37
OAR-Flow [125]42.5 3.37 40 9.87 55 2.67 34 4.22 95 12.8 92 2.87 86 4.95 67 13.4 73 2.66 62 3.23 57 16.4 69 1.37 23 2.83 30 3.82 40 1.97 14 2.49 6 10.9 39 1.87 3 1.52 3 2.82 1 1.86 36 1.85 33 4.35 37 1.68 26
Classic+NL [31]42.9 3.20 31 8.72 32 2.81 46 3.02 56 10.6 56 2.44 53 3.46 31 8.84 26 2.38 49 2.78 34 14.3 37 1.46 37 2.83 30 3.68 30 2.31 37 3.40 44 9.09 23 2.76 40 2.87 84 3.82 64 2.86 89 1.67 26 3.53 28 2.26 47
TV-L1-MCT [64]44.0 3.16 25 8.48 25 2.71 36 3.28 74 10.8 67 2.60 71 3.95 46 10.5 43 2.38 49 2.69 27 13.9 32 1.45 36 2.94 42 3.79 38 2.63 71 3.50 53 9.75 31 3.06 53 2.08 33 3.35 16 2.29 61 1.95 36 3.89 31 2.71 59
HCFN [162]44.2 3.15 23 8.58 27 2.42 16 2.09 6 8.31 23 1.63 7 2.81 14 7.61 18 1.54 5 2.86 37 15.3 49 1.44 31 2.73 22 3.55 20 2.08 20 3.42 46 10.4 36 3.28 65 4.88 139 6.08 138 5.70 135 2.45 53 5.24 54 3.47 78
PWC-Net_ROB [147]46.7 4.86 104 12.4 86 3.56 87 3.14 60 10.3 52 2.60 71 4.38 56 11.6 56 3.18 77 2.56 16 10.6 12 1.52 44 3.25 71 4.18 63 2.46 52 3.10 32 10.6 38 2.75 38 1.44 2 3.56 43 1.01 3 1.60 22 3.41 25 1.14 10
IIOF-NLDP [131]47.8 3.65 52 9.81 54 2.56 24 2.79 37 9.36 38 2.00 24 4.28 54 11.3 54 1.69 14 2.02 6 7.52 5 1.38 26 3.36 78 4.52 93 2.40 48 3.82 64 11.2 45 3.67 80 2.07 31 3.79 61 1.88 40 2.91 68 5.30 55 4.17 96
SimpleFlow [49]48.4 3.35 37 9.20 42 2.98 54 3.18 64 10.7 63 2.71 77 5.06 69 12.6 68 2.70 64 2.95 45 15.1 47 1.58 49 2.91 40 3.79 38 2.47 53 3.59 56 9.49 28 2.99 51 2.39 55 3.46 28 2.24 60 1.60 22 3.56 29 1.57 22
CostFilter [40]48.5 3.84 56 9.64 50 3.06 56 2.55 29 8.09 19 2.03 26 2.69 12 6.47 11 1.88 21 3.66 76 16.8 72 1.88 66 2.62 13 3.34 10 1.99 16 4.05 75 11.0 43 3.65 79 4.16 126 7.18 147 4.66 129 1.16 6 3.36 22 0.87 3
2DHMM-SAS [92]50.9 3.19 29 8.89 37 2.71 36 3.20 67 11.5 77 2.38 47 5.19 72 12.2 64 2.73 66 2.92 43 15.2 48 1.53 46 2.79 27 3.65 28 2.27 35 3.45 49 9.34 25 2.78 41 2.66 74 3.56 43 3.07 98 2.34 51 5.12 52 2.97 67
S2D-Matching [84]51.8 3.36 38 9.66 51 2.86 47 3.19 66 11.1 71 2.46 56 4.86 66 12.9 69 2.47 56 2.67 26 13.2 19 1.44 31 2.87 35 3.72 34 2.38 44 3.45 49 9.76 32 2.95 50 3.05 93 3.79 61 3.30 111 1.95 36 4.16 35 3.00 68
FlowFields+ [130]51.9 4.57 89 13.7 100 3.35 69 2.94 52 10.1 46 2.58 67 4.05 49 10.6 45 3.26 80 2.90 42 13.2 19 1.81 64 3.18 58 4.20 67 2.54 59 2.68 13 11.4 47 2.40 16 1.84 14 3.62 51 1.77 26 2.48 54 5.86 58 2.77 61
MLDP_OF [89]52.0 4.13 67 10.3 64 3.60 88 2.34 13 7.70 15 1.88 21 4.23 53 10.9 51 1.87 20 2.74 31 14.6 41 1.37 23 3.10 52 3.91 46 2.48 57 3.40 44 9.00 22 3.79 84 3.46 107 4.20 84 5.55 133 2.31 49 4.64 45 1.98 38
AggregFlow [97]52.9 4.25 77 11.9 83 3.26 61 4.46 101 13.7 103 3.43 97 4.76 64 12.4 65 3.93 97 3.28 60 15.6 57 1.68 56 2.89 38 3.89 43 2.08 20 2.32 3 7.75 8 2.14 7 2.06 29 3.77 59 1.48 15 2.07 44 4.11 33 2.36 49
MDP-Flow [26]52.9 3.48 47 9.46 47 3.10 58 2.45 23 7.36 12 2.41 48 3.21 23 8.31 23 2.78 68 3.18 55 17.8 80 1.70 59 3.03 48 3.87 41 2.60 66 3.43 47 12.6 67 2.81 43 2.19 44 3.88 69 1.60 20 4.13 92 9.96 100 3.86 90
IROF-TV [53]53.2 3.40 43 9.29 44 2.95 53 2.99 53 11.1 71 2.53 61 3.81 41 9.81 37 2.44 54 3.25 59 16.9 74 1.78 63 3.27 75 4.10 60 2.93 85 4.47 85 16.0 103 3.53 73 1.70 6 3.21 7 1.12 6 1.91 35 4.75 47 2.19 42
S2F-IF [123]54.6 4.51 87 13.6 99 3.31 65 2.90 48 10.4 53 2.48 59 4.07 51 10.8 50 3.15 75 3.31 61 15.7 60 1.90 67 3.17 56 4.19 65 2.55 62 2.81 17 11.6 53 2.60 27 1.86 17 3.67 54 1.87 37 2.11 46 4.64 45 2.54 56
CombBMOF [113]54.6 3.94 60 10.6 68 2.74 41 2.80 39 8.55 26 2.16 32 3.10 22 7.99 22 1.76 16 2.99 46 13.4 22 1.95 70 3.04 49 3.89 43 2.49 58 5.64 113 12.3 61 6.74 128 3.54 110 5.16 119 2.81 82 1.85 33 4.60 43 1.10 8
WRT [150]55.0 3.74 54 9.34 45 2.48 19 3.37 80 10.2 49 2.58 67 6.80 96 15.3 88 2.24 42 1.58 1 5.01 2 1.09 6 2.89 38 3.68 30 2.35 40 5.52 111 12.0 57 4.21 97 2.30 51 3.85 66 2.34 63 3.20 74 4.91 48 4.21 97
FlowFields [110]57.4 4.57 89 13.7 100 3.38 72 3.01 55 10.6 56 2.59 69 4.19 52 11.1 52 3.30 81 3.17 54 15.0 45 1.96 71 3.21 66 4.24 75 2.61 70 2.91 23 12.4 63 2.66 29 1.84 14 3.46 28 1.84 33 2.50 55 6.15 63 2.79 62
Sparse Occlusion [54]58.8 3.62 50 9.12 40 2.90 49 2.92 50 9.08 34 2.56 63 4.49 61 11.8 62 2.11 35 3.14 52 15.8 61 1.57 48 3.26 72 4.22 70 2.36 41 3.52 54 10.9 39 2.66 29 5.10 143 6.32 141 3.15 103 2.02 39 4.92 49 1.71 27
NL-TV-NCC [25]59.2 3.89 58 9.16 41 2.98 54 2.87 46 9.69 41 1.99 23 4.44 60 11.6 56 1.76 16 2.64 22 11.8 13 1.48 41 3.49 90 4.60 100 2.47 53 4.67 95 13.5 75 4.26 101 2.83 82 4.57 100 2.84 85 2.62 61 6.00 62 2.25 45
EPPM w/o HM [88]59.5 4.25 77 11.1 72 3.13 59 2.36 14 8.35 25 1.76 14 3.72 38 10.2 41 1.81 18 3.24 58 14.5 40 1.94 69 3.16 55 3.94 48 2.82 80 4.78 99 12.9 71 4.32 102 3.64 116 4.54 98 5.73 136 1.76 29 4.11 33 1.94 35
PGM-C [120]59.7 4.62 94 14.0 105 3.39 74 3.29 76 12.3 84 2.70 76 4.39 59 11.7 59 3.43 84 4.00 84 19.8 90 2.15 76 3.19 60 4.23 71 2.54 59 2.79 15 11.9 56 2.45 18 1.83 12 3.21 7 1.83 31 2.31 49 5.87 59 1.82 34
OFH [38]59.8 3.90 59 9.77 53 3.62 91 2.84 42 11.0 70 2.04 27 5.52 79 14.4 81 1.89 22 3.52 68 20.5 102 1.60 52 3.18 58 4.06 57 2.82 80 3.86 67 14.1 84 3.59 75 1.77 10 3.62 51 1.81 29 2.64 62 7.08 76 2.15 40
SegFlow [160]60.6 4.62 94 14.1 108 3.39 74 3.35 79 12.6 91 2.73 78 4.38 56 11.7 59 3.45 87 4.06 87 20.2 98 2.15 76 3.20 62 4.23 71 2.60 66 2.83 18 12.0 57 2.56 21 1.86 17 3.36 18 1.84 33 1.96 38 4.63 44 1.60 23
Occlusion-TV-L1 [63]62.0 3.59 48 9.61 48 2.64 31 2.93 51 10.6 56 2.41 48 6.16 87 15.2 86 2.70 64 3.32 63 17.0 75 1.68 56 3.38 80 4.44 86 2.82 80 3.10 32 13.2 74 2.68 32 2.17 40 3.52 37 1.46 13 4.63 106 11.1 115 3.53 79
Complementary OF [21]62.6 4.44 83 11.2 75 4.04 100 2.51 27 9.77 43 1.74 12 3.93 44 10.6 45 2.04 31 3.87 80 18.8 82 2.19 81 3.17 56 4.00 52 2.92 84 4.64 93 13.8 81 3.64 78 2.17 40 3.36 18 2.51 69 3.08 72 7.04 73 3.65 83
Adaptive [20]64.2 3.29 33 9.43 46 2.28 9 3.10 59 11.4 74 2.46 56 6.58 92 15.7 93 2.52 57 3.14 52 15.6 57 1.56 47 3.67 101 4.46 88 3.48 104 3.32 40 13.0 73 2.38 14 2.76 80 4.39 92 1.93 44 3.58 80 8.18 86 2.88 64
Aniso-Texture [82]65.2 2.96 10 7.72 8 2.54 21 2.48 25 8.26 22 2.24 38 6.48 90 15.9 98 2.63 60 1.96 5 10.1 11 0.98 2 3.26 72 4.21 68 2.60 66 5.74 116 16.9 112 5.61 118 4.47 134 5.88 136 3.33 112 3.51 79 7.12 77 3.68 85
ACK-Prior [27]65.6 4.19 73 9.27 43 3.60 88 2.40 15 8.21 21 1.65 8 3.40 27 8.96 29 1.84 19 2.87 40 14.4 39 1.44 31 3.36 78 4.15 61 3.07 89 6.35 124 16.1 105 4.90 112 4.21 129 4.80 105 6.03 138 3.29 76 5.99 61 2.82 63
CPM-Flow [116]66.2 4.63 96 14.1 108 3.39 74 3.33 77 12.5 88 2.73 78 4.37 55 11.7 59 3.43 84 4.00 84 19.9 93 2.14 75 3.19 60 4.23 71 2.54 59 3.08 30 12.0 57 2.88 47 1.87 19 3.44 25 1.84 33 2.91 68 7.48 83 2.91 66
DPOF [18]66.6 4.67 99 12.6 92 3.30 63 3.57 85 10.6 56 3.12 93 3.09 21 7.50 16 2.32 44 3.06 49 14.8 42 1.82 65 3.21 66 4.18 63 2.79 79 4.47 85 12.5 65 3.33 66 4.09 125 3.92 72 6.96 140 2.09 45 4.39 38 1.74 29
EpicFlow [102]66.8 4.61 93 14.0 105 3.39 74 3.33 77 12.5 88 2.74 80 5.37 75 14.8 84 3.46 88 3.94 83 19.2 86 2.13 74 3.20 62 4.23 71 2.58 65 2.87 21 12.2 60 2.64 28 1.83 12 3.28 13 1.83 31 3.21 75 7.12 77 3.61 80
DeepFlow2 [108]68.3 4.04 64 11.2 75 3.38 72 3.80 88 12.4 87 2.86 85 5.12 70 13.4 73 3.00 71 4.17 91 20.1 95 2.18 80 2.96 43 3.97 50 2.08 20 3.06 29 12.6 67 2.69 33 2.17 40 3.24 10 2.71 77 4.74 108 10.4 108 4.38 104
TCOF [69]68.8 4.17 70 10.4 66 3.71 94 3.17 61 10.7 63 2.59 69 6.58 92 15.7 93 3.82 95 3.69 78 16.1 66 2.37 90 3.78 105 4.95 122 2.47 53 2.59 8 8.47 15 2.58 24 3.66 118 4.83 106 2.67 75 1.83 32 4.20 36 1.46 20
ROF-ND [107]68.8 4.12 65 10.0 58 3.37 71 2.78 36 8.82 30 2.12 31 4.61 63 11.9 63 2.09 34 2.23 11 6.56 4 1.69 58 3.60 97 4.75 110 2.85 83 4.92 102 13.6 78 3.75 82 4.59 136 5.18 120 4.10 125 2.67 64 5.19 53 3.46 77
HBM-GC [105]70.2 5.25 107 10.5 67 4.34 107 3.17 61 8.78 29 2.94 89 4.38 56 10.6 45 2.68 63 3.59 72 12.8 16 2.47 93 2.96 43 3.64 27 2.64 72 3.96 73 8.26 11 3.56 74 4.40 132 5.92 137 3.62 116 2.55 57 6.34 66 3.29 72
RFlow [90]70.7 3.82 55 10.0 58 3.44 81 2.61 31 9.73 42 2.02 25 5.66 81 14.5 82 2.05 32 3.93 82 23.1 116 1.90 67 3.24 68 4.19 65 2.66 74 4.12 78 15.2 99 3.34 68 2.61 69 3.56 43 2.65 74 4.48 101 10.5 111 3.93 94
Steered-L1 [118]72.0 3.30 35 8.44 24 2.91 50 1.89 1 7.14 8 1.60 6 3.61 33 9.91 39 1.89 22 3.45 66 19.4 89 1.64 54 3.42 83 4.30 78 3.39 97 5.18 106 14.5 87 4.37 105 5.09 142 5.05 115 10.1 144 5.56 116 10.2 106 6.24 122
SRR-TVOF-NL [91]72.8 4.47 85 10.9 70 3.32 67 4.04 92 13.2 98 2.90 87 4.81 65 12.5 66 3.15 75 3.33 64 15.3 49 1.61 53 3.24 68 4.03 56 2.70 76 3.94 71 11.8 55 3.33 66 4.16 126 5.21 123 3.44 115 2.06 43 3.48 26 2.42 50
DMF_ROB [139]72.9 4.37 80 12.3 85 3.62 91 3.46 83 12.9 94 2.60 71 5.98 84 15.8 95 3.23 79 4.05 86 19.8 90 2.15 76 3.10 52 4.06 57 2.57 64 3.79 62 14.3 85 3.13 55 1.88 20 3.12 5 1.99 48 4.34 95 10.0 101 3.87 91
ComplOF-FED-GPU [35]73.2 4.28 79 11.3 77 3.70 93 3.25 69 13.0 95 2.16 32 4.06 50 11.2 53 1.95 25 3.91 81 19.2 86 2.01 72 3.20 62 4.15 61 2.64 72 4.61 91 16.1 105 3.90 86 2.98 90 3.77 59 3.69 117 2.85 66 7.44 82 2.53 55
FF++_ROB [145]74.7 4.84 103 14.8 116 3.46 82 3.18 64 11.4 74 2.69 75 5.30 74 14.1 78 3.73 94 3.31 61 14.2 34 2.20 82 3.26 72 4.29 77 2.72 77 4.58 90 12.7 69 3.70 81 1.91 22 3.46 28 2.19 59 3.65 82 7.31 79 5.97 119
TF+OM [100]76.4 3.97 61 10.2 61 2.94 52 2.91 49 9.12 35 2.57 66 5.22 73 11.5 55 6.92 109 3.59 72 16.1 66 2.28 87 3.20 62 3.97 50 3.11 90 4.70 97 14.5 87 4.32 102 3.06 95 4.84 108 2.71 77 3.93 87 8.79 91 4.32 102
Aniso. Huber-L1 [22]77.0 3.71 53 10.1 60 3.08 57 4.36 100 13.0 95 3.77 101 6.92 97 15.3 88 3.60 91 3.54 69 15.9 64 2.04 73 3.38 80 4.45 87 2.47 53 3.88 68 12.9 71 2.74 37 3.37 104 4.36 90 2.85 88 3.16 73 7.52 84 2.90 65
DeepFlow [86]78.3 4.49 86 11.7 80 4.14 102 4.26 96 12.8 92 3.36 95 5.96 83 14.2 80 5.10 101 4.89 105 23.1 116 2.67 96 2.98 45 4.00 52 2.11 25 3.26 36 13.5 75 2.84 46 2.09 35 3.10 3 2.77 79 5.83 118 11.4 117 5.45 116
Classic++ [32]79.1 3.37 40 9.67 52 2.91 50 3.28 74 12.1 81 2.61 74 5.46 78 14.1 78 3.00 71 3.63 74 20.2 98 1.70 59 3.24 68 4.34 80 2.60 66 4.65 94 16.0 103 3.60 76 3.09 96 3.94 75 3.28 110 4.64 107 10.4 108 3.71 86
TV-L1-improved [17]80.0 3.36 38 9.63 49 2.62 28 2.82 40 10.7 63 2.23 36 6.50 91 15.8 95 2.73 66 3.80 79 21.3 107 1.76 62 3.34 77 4.38 84 2.39 45 5.97 118 18.1 120 5.67 119 3.57 112 4.92 113 3.43 114 4.01 90 9.84 99 3.44 76
LocallyOriented [52]82.2 4.54 88 12.8 94 3.27 62 4.73 106 14.8 110 3.73 100 7.77 104 18.3 113 3.44 86 3.56 70 15.6 57 2.22 83 3.46 87 4.47 89 2.69 75 3.15 34 10.2 34 3.19 59 2.61 69 4.20 84 2.52 70 4.39 98 8.52 88 5.23 112
SIOF [67]82.5 4.23 75 10.2 61 3.31 65 3.97 90 14.5 108 2.97 90 7.81 106 16.4 101 7.48 111 4.82 101 20.1 95 2.96 99 3.54 93 4.49 90 3.12 91 4.31 80 13.5 75 4.13 93 2.36 54 3.59 47 1.68 23 3.46 78 7.39 80 3.37 74
LiteFlowNet [142]83.9 6.29 114 16.5 122 4.45 109 3.68 86 10.8 67 3.13 94 5.43 76 13.7 76 3.60 91 3.57 71 12.8 16 2.25 86 3.85 112 4.78 112 3.61 107 4.37 82 12.5 65 3.63 77 2.55 64 4.51 97 1.52 16 4.05 91 7.05 75 5.16 108
Brox et al. [5]85.4 4.44 83 12.4 86 4.22 105 3.72 87 13.5 102 3.06 91 4.97 68 13.3 72 3.11 73 4.58 97 22.0 110 2.37 90 3.79 107 4.60 100 4.33 129 3.91 70 17.0 113 3.45 70 2.22 48 3.79 61 1.19 7 4.62 105 10.0 101 3.38 75
TriangleFlow [30]85.7 4.12 65 10.6 68 3.47 83 3.47 84 13.1 97 2.41 48 6.00 85 15.2 86 2.17 37 2.99 46 16.0 65 1.58 49 4.46 133 5.79 138 4.15 125 5.42 110 13.9 83 5.24 113 3.10 98 5.47 129 2.90 92 3.02 70 6.82 70 3.64 82
CRTflow [80]86.0 4.18 71 11.8 82 3.20 60 3.22 68 10.8 67 2.43 51 6.20 88 15.5 91 2.63 60 4.21 92 22.0 110 2.24 84 3.32 76 4.34 80 2.44 51 7.43 131 19.3 126 8.15 134 2.55 64 4.09 80 2.59 73 4.60 104 11.2 116 4.45 105
OFRF [134]87.1 4.77 102 11.6 78 4.03 99 8.72 126 15.3 115 8.51 129 8.49 117 16.7 103 7.32 110 4.55 96 15.3 49 3.16 106 2.92 41 3.87 41 2.13 28 3.76 61 9.69 29 3.22 61 2.98 90 4.50 96 4.04 123 4.59 103 5.76 57 8.61 130
BriefMatch [124]88.0 3.44 45 9.01 38 2.77 44 2.85 45 9.93 44 2.23 36 2.97 17 7.65 21 1.94 24 3.64 75 20.1 95 1.75 61 4.10 123 4.90 120 5.82 139 7.95 133 17.8 116 8.08 133 4.73 138 5.20 121 12.2 146 7.88 135 12.0 121 13.7 141
Rannacher [23]88.4 4.13 67 11.0 71 3.61 90 3.39 81 12.3 84 2.80 83 7.26 99 17.4 109 3.59 90 4.40 94 23.1 116 2.24 84 3.43 85 4.54 96 2.56 63 5.41 109 18.5 121 4.23 98 2.92 87 3.91 71 2.82 83 3.45 77 9.14 92 3.27 71
F-TV-L1 [15]89.3 5.44 110 12.5 91 5.69 119 5.46 111 15.0 113 4.03 104 7.48 101 16.3 100 3.42 83 5.08 108 23.3 119 2.81 98 3.42 83 4.34 80 3.03 87 4.05 75 15.1 96 3.18 58 2.43 58 3.92 72 1.87 37 3.90 86 9.35 96 2.61 58
SuperFlow [81]90.4 4.16 69 11.1 72 3.32 67 4.80 107 12.2 82 4.68 110 7.80 105 16.0 99 10.6 122 5.16 112 22.4 114 3.24 108 3.39 82 4.24 75 3.71 111 3.44 48 13.7 80 2.91 48 3.19 101 4.62 102 1.87 37 4.74 108 10.6 112 4.24 99
TriFlow [95]90.4 4.73 101 12.4 86 3.49 85 4.03 91 12.5 88 3.70 99 8.18 114 17.2 107 10.4 121 3.50 67 15.4 52 2.32 89 3.43 85 4.21 68 3.42 98 3.90 69 12.3 61 3.76 83 7.86 148 5.72 132 16.2 148 2.80 65 5.89 60 2.50 53
DF-Auto [115]90.8 5.04 106 13.7 100 3.30 63 6.51 116 14.1 107 6.09 117 8.14 110 16.5 102 10.2 120 5.06 107 21.3 107 3.10 105 3.74 103 4.91 121 3.25 94 2.67 12 11.4 47 2.14 7 3.36 103 5.23 125 1.45 12 4.45 100 9.18 93 4.28 101
ContinualFlow_ROB [152]90.8 7.36 123 17.7 127 5.46 116 5.94 114 12.2 82 5.98 116 8.16 113 18.3 113 7.89 112 5.11 109 19.3 88 3.18 107 4.15 126 5.04 126 3.68 109 5.65 114 15.1 96 6.17 125 1.72 7 3.34 15 1.11 5 2.34 51 4.48 39 2.25 45
Local-TV-L1 [65]90.8 5.33 108 12.6 92 5.19 114 6.90 119 15.7 118 6.22 118 10.0 122 18.2 112 8.89 114 5.81 118 24.7 124 3.70 116 3.05 50 4.00 52 2.39 45 4.05 75 14.6 89 3.09 54 1.95 24 3.11 4 2.15 55 5.85 119 10.8 113 7.34 125
CLG-TV [48]91.4 4.00 62 10.3 64 3.40 78 4.33 99 12.3 84 4.08 105 6.78 94 15.5 91 3.64 93 4.07 88 17.7 79 2.39 92 3.79 107 4.86 115 3.23 93 4.48 87 16.5 110 3.80 85 3.55 111 4.65 103 2.89 91 4.00 89 10.1 104 3.18 70
CBF [12]92.2 3.88 57 10.2 61 3.50 86 4.60 103 11.3 73 5.06 111 5.43 76 13.1 70 3.39 82 4.09 89 21.2 106 2.16 79 3.80 110 4.72 109 3.52 105 4.33 81 14.4 86 3.01 52 4.97 140 5.51 130 4.93 131 3.99 88 9.27 95 3.91 93
Bartels [41]94.7 4.43 81 11.1 72 4.17 104 2.83 41 8.84 31 2.56 63 4.54 62 12.5 66 2.80 69 4.87 102 22.1 112 3.05 103 3.58 96 4.35 83 4.15 125 5.55 112 17.5 114 5.78 120 3.74 119 5.02 114 5.98 137 5.21 115 11.9 120 5.20 111
Fusion [6]95.2 4.43 81 13.7 100 4.08 101 2.47 24 8.91 32 2.24 38 3.70 36 9.68 35 3.12 74 3.68 77 19.8 90 2.54 95 4.26 130 5.16 131 4.31 128 6.32 121 16.8 111 6.15 124 4.55 135 5.78 134 3.10 99 7.12 129 13.6 130 7.86 129
p-harmonic [29]95.5 4.64 97 13.0 95 4.43 108 3.41 82 11.9 78 2.93 88 7.60 102 18.1 111 3.96 98 4.65 98 21.0 104 2.97 101 3.46 87 4.33 79 3.34 96 4.75 98 17.5 114 4.60 109 3.05 93 4.17 82 2.15 55 5.09 114 10.9 114 3.77 88
CNN-flow-warp+ref [117]96.5 4.93 105 14.5 113 4.29 106 4.18 94 11.9 78 4.24 107 8.23 115 19.7 121 6.35 108 5.13 110 24.4 123 2.96 99 3.55 94 4.40 85 3.85 115 3.82 64 15.0 93 3.39 69 1.96 25 3.44 25 2.14 54 10.0 139 14.8 136 10.8 137
CompactFlow_ROB [159]96.5 8.85 134 18.7 130 5.45 115 5.55 112 12.0 80 5.64 115 8.73 119 17.0 106 11.7 126 5.19 113 17.5 77 3.62 114 4.11 124 4.99 124 3.72 112 4.37 82 14.6 89 4.01 90 1.75 8 3.64 53 0.96 2 4.14 93 7.40 81 5.55 117
EAI-Flow [151]97.0 7.40 124 16.3 120 6.04 122 5.29 110 15.0 113 4.27 108 6.28 89 15.0 85 5.22 104 4.99 106 19.1 85 3.49 111 3.55 94 4.55 97 3.01 86 4.69 96 14.8 91 4.25 100 4.16 126 4.83 106 2.55 72 2.61 60 6.99 72 2.48 52
Dynamic MRF [7]97.3 4.58 91 12.4 86 4.14 102 3.25 69 13.9 104 2.27 42 6.02 86 16.8 104 2.36 46 4.39 93 22.6 115 2.51 94 3.61 98 4.55 97 3.46 100 6.81 126 22.2 136 6.78 130 2.41 56 3.48 32 3.69 117 9.26 137 17.8 140 10.2 134
FlowNetS+ft+v [112]98.7 4.22 74 12.1 84 3.48 84 4.50 102 13.4 100 3.85 102 8.29 116 18.4 115 6.20 107 4.87 102 21.6 109 3.01 102 3.93 117 5.04 126 3.47 103 3.71 59 15.3 100 3.21 60 3.32 102 5.12 117 3.87 119 3.76 84 9.44 97 3.74 87
SegOF [10]98.8 5.85 112 13.5 98 3.98 98 7.40 120 14.9 111 8.13 127 8.55 118 17.3 108 9.01 115 6.50 123 18.1 81 5.14 125 3.90 116 4.53 94 4.81 133 6.57 125 21.7 134 6.81 131 1.65 5 3.49 34 1.08 4 3.71 83 9.23 94 3.63 81
ResPWCR_ROB [144]98.9 7.29 122 16.3 120 6.15 123 4.28 97 11.4 74 3.95 103 5.85 82 13.6 75 5.20 103 4.75 100 17.5 77 3.50 112 3.80 110 4.53 94 4.12 124 4.96 105 15.0 93 4.81 111 3.52 109 5.22 124 2.40 65 3.61 81 6.77 69 4.27 100
LDOF [28]99.2 4.60 92 13.0 95 3.77 95 4.67 104 15.5 117 3.67 98 5.63 80 14.0 77 4.21 99 5.80 117 27.1 133 3.43 110 3.52 92 4.50 92 3.46 100 4.84 101 17.8 116 4.04 91 2.46 62 4.14 81 3.25 107 4.85 111 12.0 121 3.78 89
Second-order prior [8]100.0 4.03 63 11.6 78 3.35 69 3.88 89 14.0 106 3.08 92 7.21 98 17.6 110 3.57 89 4.14 90 19.9 93 2.31 88 3.66 100 4.86 115 2.73 78 7.32 129 21.2 132 6.76 129 4.02 122 4.58 101 4.01 121 4.27 94 10.4 108 5.12 107
WOLF_ROB [148]101.1 5.79 111 16.6 123 4.49 110 7.62 121 21.2 135 5.10 113 9.70 121 21.0 125 5.66 106 5.32 114 19.0 83 3.78 117 3.61 98 4.49 90 3.54 106 4.63 92 13.6 78 4.34 104 2.30 51 3.89 70 2.16 58 4.37 96 7.52 84 6.03 120
AugFNG_ROB [143]101.9 8.29 130 19.2 132 5.66 118 7.67 122 16.0 120 8.01 126 10.1 123 20.5 123 11.0 124 5.13 110 15.5 55 3.64 115 4.11 124 4.97 123 3.93 116 4.45 84 15.1 96 4.20 95 2.27 49 4.37 91 1.23 10 3.80 85 6.87 71 4.34 103
FlowNet2 [122]105.0 8.58 133 18.6 128 6.31 124 9.39 131 17.6 124 9.09 132 8.06 109 15.8 95 9.81 118 5.61 116 16.2 68 4.12 119 4.04 120 4.88 117 3.79 113 4.92 102 16.2 107 4.50 106 4.28 130 6.73 143 2.84 85 2.05 42 4.54 41 1.41 18
StereoFlow [44]105.3 17.1 150 28.1 150 17.9 149 18.7 147 29.7 148 16.5 142 20.1 147 30.9 146 17.5 142 21.2 147 38.3 149 17.9 145 4.60 134 5.05 128 5.52 135 2.38 4 11.5 51 1.77 2 1.25 1 2.92 2 0.71 1 4.49 102 10.3 107 4.23 98
EPMNet [133]106.4 8.37 132 18.8 131 6.44 126 9.35 130 18.4 126 8.78 131 7.42 100 14.7 83 8.61 113 5.98 120 20.4 101 4.27 121 4.04 120 4.88 117 3.79 113 4.92 102 16.2 107 4.50 106 3.65 117 6.14 139 2.42 67 2.60 59 6.15 63 1.74 29
Ad-TV-NDC [36]107.1 8.36 131 14.0 105 11.1 142 12.9 138 19.9 132 12.8 138 14.4 134 23.1 127 12.1 128 7.40 126 20.6 103 6.33 126 3.47 89 4.66 105 2.39 45 3.95 72 13.8 81 3.51 72 2.48 63 3.75 57 2.05 50 9.75 138 12.1 123 16.7 145
LFNet_ROB [149]108.0 7.69 125 19.8 133 5.72 120 4.70 105 13.3 99 4.13 106 8.15 112 20.0 122 5.42 105 4.73 99 17.1 76 3.42 109 4.15 126 5.10 130 4.05 120 5.28 108 18.0 118 4.64 110 2.87 84 4.74 104 1.98 47 4.92 112 11.4 117 5.01 106
Shiralkar [42]109.6 4.64 97 14.1 108 3.94 96 4.29 98 16.9 122 2.77 81 7.75 103 18.8 117 3.19 78 5.54 115 25.0 126 3.56 113 3.51 91 4.55 97 3.04 88 7.41 130 20.1 130 6.41 126 3.76 120 4.35 89 5.28 132 6.56 125 14.4 135 5.30 114
Learning Flow [11]110.2 4.23 75 11.7 80 3.41 80 4.16 93 15.3 115 3.42 96 6.78 94 16.9 105 3.83 96 6.41 122 25.3 127 4.25 120 4.66 136 6.01 143 4.00 119 6.33 123 20.7 131 5.30 114 3.09 96 4.84 108 2.91 94 7.08 128 15.0 137 5.27 113
StereoOF-V1MT [119]110.8 4.71 100 14.1 108 3.95 97 5.10 109 20.3 134 2.78 82 7.98 108 20.7 124 2.57 59 4.48 95 21.1 105 2.79 97 4.20 129 5.29 133 4.10 122 6.85 128 22.3 137 6.42 127 2.45 61 4.17 82 3.15 103 10.5 140 18.4 143 10.5 135
IAOF2 [51]111.9 5.38 109 13.7 100 4.50 111 5.95 115 14.6 109 5.61 114 8.80 120 18.8 117 9.40 116 12.2 136 23.8 122 13.1 140 3.86 113 4.89 119 3.12 91 5.21 107 14.9 92 4.54 108 4.33 131 5.15 118 3.93 120 4.39 98 8.57 89 3.87 91
TVL1_ROB [138]113.2 11.3 139 19.8 133 13.0 144 12.9 138 19.6 131 13.7 140 17.4 141 27.8 140 18.0 143 12.6 138 28.9 135 11.8 138 3.71 102 4.78 112 3.46 100 4.21 79 18.0 118 3.99 88 1.79 11 3.54 41 1.21 8 7.58 133 13.9 133 8.92 132
Modified CLG [34]114.1 7.17 121 17.1 126 6.47 127 6.85 118 14.9 111 7.48 122 14.0 130 24.8 131 15.7 138 8.35 129 27.3 134 6.36 127 3.96 118 4.99 124 4.08 121 4.54 89 19.3 126 4.15 94 2.33 53 3.86 68 2.40 65 6.00 120 13.8 132 5.40 115
GraphCuts [14]114.7 6.25 113 14.3 112 5.53 117 8.60 125 20.1 133 6.61 120 7.91 107 15.4 90 10.9 123 4.88 104 19.0 83 3.05 103 3.78 105 4.71 107 3.94 117 8.74 138 16.4 109 5.39 116 4.04 123 4.87 110 4.85 130 6.35 123 12.2 124 6.05 121
2D-CLG [1]115.1 10.1 136 22.6 141 7.59 132 9.84 133 16.9 122 11.1 137 16.9 140 28.2 141 18.8 146 14.1 140 31.1 139 13.1 140 3.86 113 4.62 103 4.53 130 5.98 119 21.2 132 5.97 122 1.76 9 3.14 6 1.46 13 6.29 122 12.9 129 5.81 118
Filter Flow [19]115.2 6.48 115 14.6 114 4.96 112 5.73 113 15.7 118 5.07 112 10.1 123 18.6 116 14.3 134 9.04 131 23.3 119 7.80 131 3.98 119 4.71 107 4.21 127 5.86 117 15.0 93 5.41 117 4.98 141 6.87 144 2.78 80 4.82 110 8.66 90 3.65 83
SPSA-learn [13]115.9 6.84 120 16.7 124 6.74 128 8.47 124 19.4 129 7.49 123 12.5 126 23.1 127 13.1 132 8.40 130 25.8 130 7.08 129 3.87 115 4.66 105 4.10 122 6.32 121 18.8 122 6.89 132 2.56 66 3.85 66 1.79 27 7.29 130 12.5 126 7.47 127
HBpMotionGpu [43]117.3 6.57 117 15.0 117 5.17 113 8.29 123 18.0 125 8.29 128 14.1 131 26.5 134 13.2 133 6.12 121 25.3 127 3.94 118 3.79 107 4.62 103 3.97 118 4.80 100 15.7 101 4.11 92 4.40 132 5.20 121 2.87 90 6.28 121 11.7 119 7.31 124
IAOF [50]118.2 6.49 116 14.6 114 6.42 125 9.22 129 18.5 127 7.94 125 16.4 139 27.4 138 13.0 131 8.22 127 22.2 113 7.73 130 3.77 104 4.76 111 3.42 98 6.84 127 18.8 122 4.23 98 3.59 113 4.46 94 2.83 84 7.51 132 10.1 104 10.6 136
GroupFlow [9]118.8 8.00 127 18.6 128 8.09 134 11.1 136 23.7 140 10.3 135 12.6 127 25.6 132 12.8 130 5.84 119 20.3 100 4.39 122 4.69 137 5.81 139 3.67 108 9.29 139 22.4 138 10.1 141 2.11 38 3.99 76 2.29 61 5.75 117 10.0 101 7.39 126
Black & Anandan [4]119.3 6.81 119 15.4 118 7.43 130 8.77 127 19.5 130 7.35 121 13.0 128 22.9 126 12.5 129 8.29 128 26.1 131 6.77 128 4.18 128 5.28 132 3.69 110 6.19 120 20.0 129 5.34 115 3.63 114 5.05 115 1.79 27 6.45 124 12.2 124 5.17 110
BlockOverlap [61]122.6 6.67 118 13.1 97 5.87 121 6.62 117 13.9 104 6.53 119 10.6 125 19.5 120 10.1 119 6.97 125 24.9 125 5.13 124 4.38 131 4.61 102 6.37 142 7.47 132 15.7 101 6.05 123 6.23 144 6.41 142 13.0 147 6.92 127 9.60 98 12.2 139
Nguyen [33]123.1 7.88 126 16.8 125 7.02 129 13.4 140 19.0 128 15.3 141 17.6 142 28.9 142 17.2 141 12.0 135 26.9 132 11.6 137 4.38 131 5.07 129 5.58 138 5.69 115 19.7 128 5.93 121 2.75 78 4.02 78 1.91 43 6.59 126 12.5 126 6.52 123
UnFlow [129]123.9 14.6 148 25.8 146 9.09 138 9.40 132 16.8 121 9.89 134 14.2 132 26.9 135 11.2 125 10.0 132 25.4 129 8.67 133 5.43 143 5.90 140 6.72 143 8.64 136 24.0 140 9.41 139 3.51 108 4.90 111 1.37 11 4.37 96 12.6 128 3.33 73
2bit-BM-tele [98]124.6 8.00 127 15.8 119 8.40 136 4.91 108 13.4 100 4.67 109 8.14 110 19.0 119 5.12 102 6.62 124 23.5 121 5.04 123 4.08 122 4.78 112 4.61 132 8.68 137 18.8 122 8.31 135 6.46 146 7.08 146 9.47 143 7.36 131 14.1 134 9.62 133
Horn & Schunck [3]129.6 8.01 129 19.9 135 8.38 135 9.13 128 23.2 139 7.71 124 14.2 132 25.9 133 14.6 136 12.4 137 30.6 137 11.3 136 4.64 135 5.64 135 4.60 131 8.21 135 24.4 141 8.45 136 4.01 121 5.41 126 1.95 45 9.16 136 17.5 138 8.86 131
SILK [79]131.1 9.34 135 20.4 136 10.5 141 10.4 134 21.9 136 10.3 135 16.0 138 27.5 139 14.5 135 10.3 133 29.0 136 8.54 132 4.81 138 5.65 136 5.56 137 9.41 140 25.4 143 8.74 137 2.79 81 3.68 55 4.62 128 10.9 141 17.8 140 12.3 140
Heeger++ [104]132.7 11.9 142 21.8 139 8.08 133 12.5 137 29.7 148 9.42 133 14.8 135 27.1 136 9.68 117 14.3 141 31.0 138 12.7 139 4.98 140 5.74 137 4.97 134 17.5 148 34.1 149 18.4 148 2.75 78 5.44 127 2.15 55 12.3 143 18.8 144 14.8 143
TI-DOFE [24]133.6 13.4 146 23.2 142 16.5 148 16.5 144 24.1 141 18.2 146 20.2 148 31.1 148 20.6 147 19.9 146 32.9 142 20.8 147 4.89 139 5.90 140 5.54 136 8.04 134 23.9 139 8.81 138 2.97 89 4.34 88 1.88 40 10.9 141 17.7 139 11.9 138
H+S_ROB [137]135.0 13.0 144 27.1 149 9.66 139 13.4 140 24.8 143 13.4 139 18.7 146 30.9 146 18.3 145 25.8 150 35.7 146 26.4 149 7.08 148 8.13 148 9.10 147 14.6 146 31.3 148 16.3 146 2.17 40 4.44 93 2.11 53 15.1 147 19.9 146 14.2 142
HCIC-L [99]137.9 15.7 149 22.0 140 10.1 140 31.5 150 26.6 146 41.0 150 14.8 135 23.1 127 16.8 140 18.4 145 34.4 144 18.2 146 5.94 144 6.35 144 6.35 141 10.6 143 19.2 125 11.4 143 18.7 150 17.8 150 19.2 149 4.93 113 8.34 87 5.16 108
SLK [47]137.9 11.6 140 26.0 147 14.6 147 15.3 143 25.0 144 17.5 144 17.8 144 30.1 145 18.1 144 25.4 149 33.6 143 28.0 150 5.25 141 5.90 140 7.03 144 10.3 142 27.4 145 10.6 142 2.89 86 4.47 95 2.94 96 14.9 146 20.7 147 18.8 146
FFV1MT [106]138.9 12.0 143 23.3 143 8.83 137 10.7 135 26.6 146 8.71 130 15.6 137 29.0 143 12.0 127 16.6 144 36.3 148 15.5 143 6.51 147 6.40 145 10.4 148 16.2 147 30.7 147 17.7 147 3.41 106 5.44 127 3.35 113 12.3 143 18.8 144 14.8 143
Adaptive flow [45]140.9 13.2 145 20.8 137 14.0 146 17.1 146 22.0 137 17.9 145 18.1 145 27.1 136 22.8 149 11.8 134 31.1 139 10.5 134 6.35 146 7.13 147 6.25 140 9.87 141 21.8 135 9.44 140 12.6 149 11.4 149 20.0 150 7.75 134 13.6 130 7.73 128
PGAM+LK [55]142.2 11.8 141 25.6 144 13.9 145 14.8 142 24.4 142 16.7 143 13.2 129 24.0 130 15.0 137 16.2 143 41.2 150 15.3 142 5.40 142 5.45 134 8.10 145 12.3 145 26.5 144 12.1 144 7.42 147 8.24 148 7.87 141 13.2 145 18.3 142 19.4 147
Periodicity [78]143.0 11.2 138 27.0 148 7.46 131 16.6 145 29.8 150 18.2 146 25.3 150 31.2 150 24.9 150 12.7 139 35.7 146 11.1 135 31.7 150 41.4 150 25.1 150 23.8 150 41.5 150 23.8 150 2.92 87 5.62 131 6.90 139 18.6 149 33.1 150 22.3 148
FOLKI [16]143.8 10.5 137 25.6 144 11.9 143 20.9 148 26.2 145 26.1 148 17.6 142 31.1 148 16.5 139 15.4 142 32.6 141 16.0 144 6.16 145 6.53 146 9.07 146 12.2 144 29.7 146 13.0 145 4.67 137 5.83 135 9.41 142 18.2 148 22.8 148 25.1 149
Pyramid LK [2]146.8 13.9 147 20.9 138 21.4 150 24.1 149 23.1 138 30.2 149 20.9 149 29.5 144 21.9 148 22.2 148 34.6 145 25.0 148 18.7 149 23.1 149 20.2 149 21.2 149 24.5 142 21.0 149 6.41 145 7.02 145 10.8 145 25.6 150 31.5 149 34.5 150
AdaConv-v1 [126]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
SepConv-v1 [127]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
SuperSlomo [132]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
CtxSyn [136]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
CyclicGen [153]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
TOF-M [154]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
MPRN [155]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
DAIN [156]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
FRUCnet [157]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
OFRI [158]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
PyrWarp [161]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
FGME [163]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
MS-PFT [164]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
MEMC-Net+ [165]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
ADC [166]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
DSepConv [167]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
MAF-net [168]151.4 39.2 151 39.9 151 41.8 151 73.0 151 74.5 151 71.1 151 70.1 151 67.3 151 71.8 151 64.4 151 66.2 151 65.9 151 76.5 152 78.1 152 72.0 152 68.2 152 64.9 152 66.5 152 52.3 152 45.1 152 70.9 152 81.8 151 81.6 151 82.3 151
AVG_FLOW_ROB [141]161.6 62.1 168 56.6 168 61.5 168 99.9 168 96.7 168 99.9 168 81.2 168 81.9 168 80.3 168 65.8 168 68.9 168 67.4 168 68.4 151 75.2 151 67.5 151 62.4 151 55.3 151 59.6 151 31.5 151 28.0 151 29.3 151 86.1 168 96.7 168 87.2 168
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] MPRN 0.32 4 color Anonymous. (Interpolation results only.) Multi-frame pyramid refinement network for video frame interpolation. CVPR 2019 submission 1361.
[156] DAIN 0.13 2 color Anonymous. (Interpolation results only.) DAIN: Depth-aware video frame interpolation. CVPR 2019 submission 1769.
[157] FRUCnet 0.65 2 color V. T. Nguyen, K. Lee, and H.-J. Lee. (Interpolation results only.) A stacked deep MEMC network for frame rate up conversion and its application to HEVC. Submitted to IEEE TCSVT 2019.
[158] OFRI 0.31 2 color Anonymous. (Interpolation results only.) Efficient video frame interpolation via optical flow refinement. CVPR 2019 submission 6743.
[159] CompactFlow_ROB 0.05 2 color Anonymous. CompactFlow: spatially shiftable window revisited. CVPR 2019 submission 1387.
[160] SegFlow 3.2 2 color J. Chen, Z. Cai, J. Lai, and X. Xie. Efficient segmentation-based PatchMatch for large displacement optical flow estimation. IEEE TCSVT 2018.
[161] PyrWarp 0.14 2 color Anonymous. (Interpolation results only.) Video frame interpolation using differentiable forward-warping of feature pyramids. ICCV 2019 submission 741.
[162] HCFN 0.18 2 color Anonymous. Practical coarse-to-fine optical flow with deep networks. ICCV 2019 submission 116.
[163] FGME 0.23 2 color Anonymous. (Interpolation results only.) Fine-grained motion estimation for video frame interpolation. ICCV 2019 submission 4327.
[164] MS-PFT 0.44 2 color X. Cheng and Z. Chen. (Interpolation results only.) A multi-scale position feature transform network for video frame interpolation. Submitted to TCSVT 2019.
[165] MEMC-Net+ 0.12 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.
[166] ADC 0.01 2 color Anonymous. (Interpolation results only.) Learning spatial transform for video frame interpolation. ICCV 2019 submission 5424.
[167] DSepConv 0.3 2 color Anonymous. (Interpolation results only.) Video frame interpolation via deformable separable convolution. AAAI 2020 submission 2271.
[168] MAF-net 0.3 2 color Anonymous. (Interpolation results only.) MAF-net: Motion attention feedback network for video frame interpolation. AAAI 2020 submission 9862.
* 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.