Optical flow evaluation results Statistics:     Average   SD   R0.5   R1.0   R2.0   A90   A95   A99  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
R1.0
normalized interpolation
error
avg. Mequon
(Hidden texture)
im0   GT   im1
Schefflera
(Hidden texture)
im0   GT   im1
Urban
(Synthetic)
im0   GT   im1
Teddy
(Stereo)
im0   GT   im1
Backyard
(High-speed camera)
im0   GT   im1
Basketball
(High-speed camera)
im0   GT   im1
Dumptruck
(High-speed camera)
im0   GT   im1
Evergreen
(High-speed camera)
im0   GT   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
MDP-Flow2 [68]11.5 6.71 2 9.78 2 8.39 9 6.36 9 11.5 11 6.23 12 7.12 4 9.73 7 5.42 2 21.2 5 18.8 8 41.2 11 30.1 5 26.5 3 45.6 48 27.7 17 19.5 26 36.0 17 6.48 2 13.8 4 10.3 6 8.19 22 16.4 17 7.11 26
PMMST [114]12.8 6.84 9 9.80 3 8.50 17 6.74 26 11.7 13 6.36 29 7.10 3 9.45 3 5.41 1 21.1 1 18.6 2 41.1 3 30.2 9 26.5 3 45.7 59 27.3 2 18.1 4 36.0 17 6.51 4 13.9 9 10.3 6 8.22 25 16.5 23 7.15 37
NNF-Local [87]14.5 6.74 4 10.1 6 8.30 2 5.97 1 10.3 2 6.14 3 7.09 2 9.63 6 5.44 3 21.7 31 20.5 62 41.2 11 30.3 19 26.6 8 45.4 23 27.9 34 20.6 61 36.0 17 6.51 4 13.8 4 10.3 6 8.04 9 16.1 6 7.10 24
PH-Flow [101]14.5 7.05 23 10.9 17 8.50 17 6.10 3 10.6 5 6.14 3 7.18 5 9.83 8 5.55 6 21.1 1 18.5 1 41.1 3 30.1 5 26.6 8 45.2 14 27.9 34 21.7 85 35.7 9 6.60 15 14.3 19 10.3 6 8.11 13 16.3 14 7.14 34
CombBMOF [113]15.8 6.93 13 9.87 5 8.43 11 6.33 8 11.4 9 6.22 11 7.60 44 10.3 14 6.25 75 21.5 20 19.4 22 41.2 11 30.2 9 26.6 8 45.3 19 27.7 17 19.2 18 36.1 30 6.57 11 14.1 12 10.3 6 7.67 1 15.4 3 6.82 1
NN-field [71]18.8 6.88 10 10.8 13 8.48 14 5.99 2 10.3 2 6.13 2 7.65 53 9.54 4 5.81 32 21.9 46 21.0 82 41.4 24 30.2 9 26.6 8 45.4 23 27.8 27 20.0 42 36.0 17 6.48 2 13.7 3 10.3 6 8.02 8 16.1 6 7.04 17
IROF++ [58]24.1 7.07 26 11.3 28 8.50 17 6.64 21 12.0 18 6.19 7 7.54 36 10.6 27 5.84 37 21.2 5 18.6 2 41.5 28 30.2 9 26.9 20 45.0 9 27.6 14 18.8 10 36.2 37 6.69 32 14.7 30 10.5 57 8.17 19 16.4 17 7.33 72
Layers++ [37]24.5 7.17 36 11.1 23 8.79 57 6.14 6 10.3 2 6.41 33 7.34 16 10.3 14 5.69 19 21.3 10 19.0 14 41.3 17 30.5 36 27.1 40 45.4 23 28.1 52 21.1 73 36.2 37 6.51 4 13.8 4 10.2 1 8.20 23 16.4 17 7.13 32
nLayers [57]26.3 7.15 34 10.4 10 8.81 62 6.44 12 11.4 9 6.42 34 7.23 8 9.30 1 5.65 14 21.4 15 19.1 17 41.5 28 30.7 71 27.4 80 45.6 48 28.0 43 20.8 65 36.2 37 6.54 9 13.6 1 10.3 6 8.07 10 16.3 14 6.91 4
Sparse-NonSparse [56]27.9 7.09 27 11.3 28 8.57 25 6.53 16 11.8 16 6.21 9 7.40 24 10.5 24 5.64 10 21.5 20 19.0 14 41.7 45 30.4 26 27.0 29 45.4 23 28.3 64 21.5 82 36.4 60 6.66 24 14.4 21 10.3 6 8.23 26 16.6 28 7.09 22
2DHMM-SAS [92]28.5 7.27 48 12.0 57 8.59 28 7.82 63 14.2 52 6.36 29 7.25 9 10.5 24 5.74 25 21.4 15 18.7 5 41.4 24 30.3 19 26.9 20 45.2 14 27.9 34 20.3 46 36.0 17 6.64 22 14.4 21 10.3 6 8.41 44 17.0 42 7.08 19
ProbFlowFields [128]28.5 7.03 19 11.8 50 8.66 38 6.41 10 11.7 13 6.31 22 7.18 5 10.3 14 5.58 7 21.7 31 19.6 29 41.8 52 30.7 71 27.1 40 46.0 104 27.9 34 20.5 53 36.2 37 6.51 4 13.8 4 10.3 6 7.80 3 15.6 4 7.14 34
NNF-EAC [103]29.5 7.35 56 11.1 23 8.79 57 6.92 36 12.5 30 6.29 20 7.52 35 10.1 11 5.76 28 21.8 38 19.5 27 42.8 100 30.2 9 26.6 8 45.4 23 27.5 7 18.9 12 36.0 17 6.58 13 14.2 15 10.4 32 8.32 33 16.8 32 7.19 46
AGIF+OF [85]29.6 7.11 29 11.3 28 8.46 12 6.68 25 12.2 22 6.27 16 7.38 21 10.1 11 5.71 23 21.2 5 18.6 2 41.1 3 30.8 83 27.6 100 45.4 23 28.3 64 22.2 103 36.0 17 6.67 25 14.0 11 10.3 6 8.34 36 17.0 42 6.91 4
FlowFields [110]30.0 7.02 18 11.5 36 8.54 23 6.66 24 12.5 30 6.44 38 7.45 28 11.5 53 5.64 10 21.9 46 20.5 62 41.8 52 30.6 52 27.0 29 45.5 38 27.7 17 20.2 44 36.0 17 6.59 14 14.3 19 10.4 32 8.00 7 16.2 11 7.08 19
FMOF [94]30.5 7.36 58 12.0 57 8.73 48 6.42 11 11.3 8 6.30 21 7.63 49 10.4 19 6.02 64 21.8 38 19.9 36 41.2 11 30.5 36 27.0 29 45.4 23 28.0 43 20.5 53 36.1 30 6.51 4 13.8 4 10.2 1 8.30 31 16.7 31 7.12 28
FlowFields+ [130]30.7 6.99 15 11.4 34 8.47 13 6.61 20 12.3 24 6.48 41 7.42 26 11.5 53 5.67 17 21.7 31 20.3 50 41.6 35 30.7 71 27.2 54 45.6 48 27.8 27 20.3 46 36.1 30 6.61 16 14.4 21 10.4 32 7.99 6 16.2 11 7.03 15
S2F-IF [123]31.4 7.01 17 11.5 36 8.48 14 6.57 17 12.2 22 6.42 34 7.40 24 11.2 49 5.64 10 21.6 24 20.1 43 41.3 17 30.7 71 27.3 68 45.7 59 27.8 27 20.4 50 36.1 30 6.71 37 14.9 45 10.4 32 7.98 5 16.1 6 7.04 17
LSM [39]32.8 7.17 36 11.8 50 8.58 27 6.64 21 12.1 21 6.17 6 7.49 31 10.9 39 5.69 19 21.6 24 19.6 29 41.6 35 30.5 36 27.1 40 45.4 23 28.3 64 21.6 83 36.3 51 6.68 30 14.6 26 10.2 1 8.35 39 16.9 40 7.03 15
LME [70]33.3 6.72 3 9.86 4 8.36 5 6.97 38 12.4 27 7.40 85 7.51 33 11.8 61 5.70 22 21.3 10 19.2 20 41.3 17 31.0 104 27.6 100 46.6 116 27.8 27 20.5 53 36.0 17 6.45 1 13.6 1 10.2 1 8.08 12 16.3 14 7.12 28
WLIF-Flow [93]34.5 6.99 15 11.0 20 8.48 14 6.76 27 12.4 27 6.39 32 7.38 21 10.3 14 5.68 18 21.4 15 18.8 8 41.9 65 30.4 26 26.9 20 45.9 93 28.8 97 21.9 92 36.9 93 6.56 10 13.9 9 10.3 6 8.34 36 16.8 32 7.15 37
TV-L1-MCT [64]34.5 7.50 77 12.5 84 8.79 57 7.19 43 13.4 42 6.37 31 7.28 11 10.6 27 5.80 31 21.4 15 18.8 8 41.3 17 30.5 36 27.1 40 45.1 12 27.9 34 18.6 7 36.6 74 6.72 41 15.0 52 10.4 32 7.92 4 15.9 5 7.20 49
ComponentFusion [96]34.5 6.91 11 10.8 13 8.55 24 6.49 15 12.0 18 6.10 1 7.49 31 11.2 49 5.72 24 21.3 10 19.4 22 41.2 11 30.6 52 27.2 54 45.8 75 27.8 27 19.6 29 36.2 37 6.92 73 16.4 91 10.4 32 8.43 48 17.0 42 7.16 40
COFM [59]35.5 7.04 20 10.7 12 8.70 42 6.60 18 11.9 17 6.35 26 7.26 10 9.93 9 5.63 9 21.2 5 18.8 8 41.0 1 30.4 26 27.3 68 44.9 8 27.7 17 22.6 106 35.1 2 6.86 66 14.7 30 11.2 110 8.67 75 17.2 59 7.78 108
MDP-Flow [26]36.3 6.83 7 10.8 13 8.50 17 6.65 23 12.4 27 6.51 45 7.46 29 10.6 27 5.88 45 22.1 64 20.6 67 41.7 45 30.4 26 26.8 15 45.6 48 28.2 61 21.9 92 36.3 51 6.69 32 14.8 39 10.4 32 8.15 15 16.6 28 7.10 24
HAST [109]37.7 6.97 14 10.2 7 8.69 40 6.46 13 11.6 12 6.26 15 7.72 61 11.1 45 5.97 57 21.1 1 18.7 5 41.1 3 30.5 36 27.5 87 44.8 6 28.2 61 22.8 112 35.5 5 6.76 50 15.2 60 10.4 32 8.81 82 18.0 91 6.96 9
RNLOD-Flow [121]37.8 7.12 31 11.5 36 8.64 35 7.38 49 14.0 49 6.35 26 7.55 37 11.2 49 5.83 36 21.3 10 19.0 14 41.1 3 30.5 36 27.2 54 45.4 23 28.3 64 21.6 83 36.2 37 6.62 17 14.2 15 10.4 32 8.70 78 17.7 79 7.02 13
OFLAF [77]38.1 6.81 6 10.2 7 8.40 10 6.10 3 10.7 6 6.21 9 7.36 18 10.6 27 5.54 5 21.1 1 18.8 8 41.0 1 30.8 83 27.4 80 45.7 59 28.1 52 21.9 92 36.0 17 7.02 84 16.1 88 10.4 32 8.90 89 18.1 98 7.16 40
Ramp [62]40.5 7.31 54 12.1 61 8.78 54 6.60 18 12.0 18 6.27 16 7.36 18 10.4 19 5.65 14 21.3 10 18.9 13 41.4 24 30.5 36 27.1 40 45.4 23 28.7 92 22.3 104 36.6 74 6.73 46 14.9 45 10.3 6 8.55 62 17.3 64 7.26 60
Second-order prior [8]41.2 7.30 53 11.3 28 8.90 71 8.52 81 15.6 80 6.74 60 8.32 94 13.6 103 6.42 88 21.8 38 20.0 39 41.5 28 30.1 5 26.5 3 45.5 38 27.5 7 19.0 13 36.0 17 6.67 25 14.6 26 10.3 6 8.25 27 16.8 32 7.11 26
PGM-C [120]42.4 7.19 41 12.1 61 8.72 45 6.82 29 12.9 34 6.62 51 7.67 57 12.2 72 5.78 30 21.9 46 20.9 76 41.8 52 30.6 52 27.1 40 45.8 75 27.7 17 19.5 26 36.2 37 6.65 23 14.7 30 10.3 6 8.20 23 16.6 28 7.31 67
DeepFlow2 [108]43.2 7.28 49 11.3 28 8.88 68 7.68 58 14.4 59 6.94 73 7.58 42 12.3 76 5.88 45 21.9 46 20.2 47 41.7 45 30.5 36 26.8 15 45.9 93 27.5 7 18.0 3 36.4 60 6.67 25 14.6 26 10.4 32 8.18 20 16.4 17 7.31 67
Aniso. Huber-L1 [22]43.4 7.61 84 12.2 69 9.19 83 8.99 91 15.7 83 7.12 79 7.73 63 11.0 42 5.86 42 21.8 38 20.0 39 41.6 35 30.2 9 26.6 8 45.5 38 27.4 4 19.6 29 35.7 9 6.68 30 14.6 26 10.3 6 8.34 36 16.8 32 7.29 66
Classic+NL [31]43.4 7.44 71 12.3 72 8.86 65 6.78 28 12.3 24 6.28 18 7.32 15 10.4 19 5.69 19 21.6 24 19.4 22 41.8 52 30.5 36 27.1 40 45.5 38 28.6 87 21.8 88 36.6 74 6.72 41 14.7 30 10.3 6 8.50 56 17.2 59 7.24 57
FC-2Layers-FF [74]43.5 7.22 44 11.9 55 8.70 42 6.10 3 10.2 1 6.47 40 7.31 14 10.5 24 5.64 10 21.4 15 19.1 17 41.6 35 30.7 71 27.5 87 45.6 48 28.6 87 22.7 109 36.4 60 6.77 52 15.0 52 10.3 6 8.57 64 17.2 59 7.20 49
SRR-TVOF-NL [91]44.5 7.42 69 11.5 36 8.86 65 7.79 60 14.8 67 7.08 77 7.62 46 11.5 53 5.85 40 21.7 31 19.6 29 41.1 3 30.3 19 27.1 40 45.2 14 27.5 7 20.5 53 35.3 4 6.72 41 14.8 39 10.4 32 8.97 96 18.3 104 7.17 43
DF-Auto [115]45.5 7.54 79 11.1 23 9.32 91 8.42 77 14.5 62 8.82 96 7.35 17 10.3 14 5.65 14 22.0 56 20.2 47 41.5 28 30.4 26 26.7 14 45.8 75 27.5 7 18.7 9 36.1 30 6.82 59 15.3 64 10.5 57 8.43 48 17.1 50 7.20 49
FESL [72]45.8 7.36 58 11.7 45 8.65 37 6.82 29 12.6 32 6.33 24 7.51 33 10.7 34 5.89 48 21.6 24 19.6 29 41.3 17 30.9 100 27.5 87 45.7 59 28.4 76 22.1 101 36.2 37 6.70 35 14.8 39 10.2 1 8.59 65 17.4 70 7.08 19
CPM-Flow [116]46.0 7.21 42 12.2 69 8.71 44 6.83 32 12.9 34 6.65 54 7.61 45 11.7 58 5.88 45 22.2 70 21.4 95 41.8 52 30.6 52 27.1 40 45.8 75 27.9 34 19.1 14 36.6 74 6.67 25 14.7 30 10.3 6 8.16 17 16.5 23 7.34 75
Classic+CPF [83]46.4 7.22 44 11.6 41 8.52 22 6.90 35 12.6 32 6.28 18 7.37 20 10.6 27 5.76 28 21.2 5 18.7 5 41.1 3 31.1 108 27.9 108 45.5 38 28.7 92 23.1 116 36.3 51 6.92 73 15.3 64 10.3 6 8.75 80 17.9 87 6.99 10
S2D-Matching [84]48.0 7.37 61 12.3 72 8.80 60 7.62 57 14.2 52 6.43 37 7.28 11 10.4 19 5.74 25 21.6 24 19.1 17 42.2 79 30.6 52 27.3 68 45.4 23 28.6 87 22.5 105 36.4 60 6.76 50 14.5 24 10.3 6 8.46 51 17.0 42 7.32 69
IROF-TV [53]48.3 7.33 55 12.3 72 8.82 63 6.83 32 12.3 24 6.23 12 7.70 60 12.9 92 5.93 52 21.5 20 19.5 27 42.0 70 30.8 83 27.3 68 45.9 93 27.5 7 20.2 44 35.6 6 6.75 48 15.1 58 10.5 57 8.18 20 16.4 17 7.37 79
DeepFlow [86]48.3 7.21 42 11.0 20 8.88 68 7.79 60 14.3 55 7.33 84 7.64 51 12.6 84 5.95 54 22.1 64 20.1 43 42.0 70 30.6 52 26.8 15 46.1 107 28.0 43 17.9 2 37.2 100 6.57 11 14.1 12 10.4 32 8.07 10 16.2 11 7.32 69
EPPM w/o HM [88]48.4 6.77 5 10.4 10 8.32 3 7.00 39 13.4 42 6.16 5 8.19 87 13.6 103 6.26 76 21.7 31 20.3 50 41.5 28 30.5 36 27.2 54 45.5 38 28.5 82 21.8 88 36.5 68 6.84 60 15.6 74 10.6 81 8.41 44 17.1 50 6.95 8
Brox et al. [5]48.9 7.28 49 11.4 34 8.76 50 7.86 64 14.6 64 6.92 72 8.03 80 13.1 94 6.34 82 21.9 46 19.9 36 41.4 24 30.6 52 27.0 29 45.8 75 27.7 17 19.5 26 36.2 37 6.80 55 15.4 69 10.4 32 8.16 17 16.5 23 7.19 46
p-harmonic [29]48.9 7.04 20 11.3 28 8.62 33 8.81 85 15.8 85 6.98 74 7.76 66 13.1 94 6.18 73 22.4 81 20.7 70 41.9 65 30.5 36 27.0 29 45.5 38 27.8 27 19.2 18 36.4 60 6.71 37 15.1 58 10.3 6 8.29 30 16.8 32 7.12 28
Efficient-NL [60]49.4 7.28 49 11.6 41 8.61 32 7.24 46 13.3 41 6.35 26 8.21 89 10.8 36 6.39 87 21.7 31 19.6 29 41.2 11 30.4 26 27.0 29 45.3 19 28.3 64 22.8 112 35.6 6 6.86 66 15.6 74 10.4 32 9.10 102 18.3 104 7.14 34
SepConv-v1 [127]50.0 4.07 1 8.88 1 4.61 1 6.87 34 13.0 38 7.47 86 6.42 1 9.58 5 9.25 124 23.4 105 20.0 39 44.0 109 30.2 9 26.3 2 45.7 59 27.9 34 16.5 1 37.4 106 7.61 112 15.6 74 12.9 129 7.71 2 13.8 1 9.78 128
EpicFlow [102]52.7 7.18 38 12.0 57 8.72 45 7.42 51 14.4 59 6.72 59 7.68 58 12.1 69 5.92 51 22.1 64 21.1 86 42.0 70 30.7 71 27.1 40 45.8 75 27.5 7 19.9 40 35.9 13 6.79 54 15.2 60 10.4 32 8.40 43 17.1 50 7.33 72
DPOF [18]53.0 7.58 83 13.2 102 9.07 74 6.27 7 11.0 7 6.54 47 8.10 85 10.6 27 6.27 78 22.0 56 20.5 62 41.9 65 30.2 9 26.8 15 45.4 23 28.0 43 21.2 75 35.8 12 6.84 60 15.0 52 10.7 91 8.62 69 17.4 70 7.26 60
PMF [73]53.5 6.83 7 10.3 9 8.37 7 6.96 37 13.1 39 6.19 7 7.86 71 13.1 94 6.03 65 21.5 20 19.4 22 41.3 17 31.0 104 27.7 104 45.8 75 28.7 92 20.5 53 37.2 100 6.80 55 15.0 52 10.5 57 8.87 85 18.2 101 7.00 11
ComplOF-FED-GPU [35]54.0 7.23 46 11.8 50 8.72 45 7.20 44 13.9 46 6.62 51 8.43 97 12.6 84 6.45 89 21.9 46 20.8 75 42.3 81 30.4 26 26.9 20 45.4 23 27.7 17 20.1 43 36.1 30 6.86 66 15.4 69 10.5 57 8.55 62 17.3 64 7.28 65
Sparse Occlusion [54]55.3 7.37 61 12.3 72 8.87 67 8.04 68 15.3 74 6.48 41 7.58 42 10.8 36 5.87 43 22.0 56 20.4 56 41.5 28 30.6 52 27.2 54 45.5 38 28.3 64 21.8 88 36.4 60 6.80 55 15.3 64 10.3 6 8.74 79 17.7 79 7.18 45
TC/T-Flow [76]55.6 7.37 61 11.8 50 8.59 28 7.31 47 14.0 49 6.42 34 7.47 30 11.1 45 5.81 32 21.8 38 20.5 62 41.7 45 30.8 83 27.5 87 45.7 59 28.1 52 20.9 66 36.2 37 7.03 86 16.0 86 10.6 81 8.62 69 17.6 76 7.13 32
CLG-TV [48]57.3 7.52 78 12.3 72 9.14 78 8.67 83 15.8 85 7.11 78 7.97 77 12.7 88 6.26 76 22.1 64 20.3 50 42.0 70 30.5 36 26.9 20 45.7 59 27.6 14 19.1 14 36.2 37 6.71 37 14.9 45 10.4 32 8.53 61 17.3 64 7.24 57
SuperFlow [81]57.3 7.43 70 11.5 36 9.30 88 8.55 82 14.8 67 9.15 99 7.91 75 12.0 66 6.31 79 22.1 64 19.9 36 42.0 70 30.7 71 27.2 54 45.9 93 27.3 2 18.4 6 35.9 13 6.86 66 15.8 80 10.6 81 8.15 15 16.5 23 7.16 40
AggregFlow [97]57.3 7.71 90 12.6 86 9.11 76 7.50 54 13.9 46 7.06 75 7.19 7 9.98 10 5.53 4 21.9 46 20.4 56 41.6 35 30.8 83 27.3 68 46.1 107 29.0 101 19.7 36 37.9 113 6.75 48 14.7 30 10.5 57 8.32 33 16.8 32 7.40 83
RFlow [90]57.9 7.24 47 12.1 61 8.90 71 8.42 77 15.6 80 6.49 43 7.72 61 12.2 72 6.01 63 22.0 56 20.6 67 41.7 45 30.4 26 27.1 40 45.7 59 27.4 4 19.8 39 35.6 6 6.84 60 15.9 84 10.5 57 8.91 92 18.0 91 7.47 88
TCOF [69]58.4 7.36 58 12.1 61 8.68 39 9.41 98 16.6 98 7.17 81 7.38 21 10.7 34 5.61 8 21.8 38 20.4 56 41.8 52 30.4 26 27.0 29 45.6 48 28.1 52 21.8 88 35.9 13 6.85 63 15.7 79 10.4 32 9.30 111 19.0 115 7.61 102
SIOF [67]58.5 7.66 87 12.6 86 9.09 75 9.45 99 16.6 98 8.48 93 7.65 53 11.9 64 5.98 58 21.9 46 20.1 43 41.8 52 30.0 3 26.5 3 45.3 19 28.1 52 19.7 36 36.6 74 6.63 20 14.7 30 10.5 57 8.82 83 17.9 87 7.46 85
IAOF [50]58.9 8.70 111 12.9 95 10.3 108 12.4 121 19.2 125 9.77 110 7.74 64 12.0 66 6.21 74 22.8 91 20.2 47 42.0 70 30.2 9 26.5 3 45.5 38 27.7 17 19.6 29 36.1 30 6.67 25 15.0 52 10.3 6 8.41 44 17.1 50 7.12 28
TC-Flow [46]59.0 7.18 38 11.8 50 8.78 54 7.46 53 14.6 64 6.77 64 7.86 71 12.6 84 5.89 48 21.8 38 20.3 50 41.9 65 30.7 71 27.4 80 45.7 59 28.3 64 21.0 69 36.6 74 6.73 46 14.8 39 10.5 57 8.51 57 17.3 64 7.24 57
OAR-Flow [125]60.5 7.45 73 11.7 45 8.98 73 7.57 56 14.4 59 6.91 71 7.62 46 12.4 79 5.82 34 21.6 24 20.3 50 41.6 35 30.9 100 27.5 87 45.8 75 28.0 43 20.5 53 36.4 60 6.97 81 15.6 74 10.5 57 8.46 51 17.1 50 7.34 75
ALD-Flow [66]62.3 7.54 79 12.1 61 9.14 78 7.43 52 14.3 55 6.85 68 7.66 56 12.5 82 5.87 43 21.8 38 20.4 56 42.3 81 30.8 83 27.4 80 45.9 93 28.1 52 19.9 40 36.6 74 6.62 17 14.2 15 10.5 57 8.68 76 17.5 75 7.46 85
OFH [38]62.9 7.39 66 12.1 61 8.88 68 8.07 69 15.0 72 6.66 56 8.03 80 13.8 106 5.96 56 21.9 46 21.1 86 42.1 76 30.5 36 27.3 68 45.4 23 27.8 27 20.4 50 36.2 37 7.11 88 16.4 91 10.5 57 8.61 68 17.6 76 7.19 46
SVFilterOh [111]63.3 7.18 38 10.9 17 8.76 50 6.48 14 11.7 13 6.45 39 7.62 46 10.2 13 5.99 60 21.7 31 19.4 22 42.5 91 31.3 112 28.0 112 46.6 116 28.6 87 22.0 97 36.5 68 6.92 73 14.1 12 11.4 115 8.97 96 17.8 84 8.09 113
MLDP_OF [89]64.6 7.10 28 11.2 27 8.64 35 7.33 48 13.7 44 6.31 22 7.44 27 10.9 39 5.75 27 22.0 56 19.8 35 42.3 81 30.6 52 27.3 68 46.2 113 31.0 125 22.6 106 40.0 125 6.93 76 15.2 60 11.0 104 8.65 73 17.4 70 7.79 110
CostFilter [40]65.0 6.91 11 11.1 23 8.37 7 6.82 29 12.9 34 6.25 14 7.99 78 13.9 107 6.10 67 21.9 46 20.6 67 41.7 45 31.1 108 27.9 108 45.9 93 29.8 113 20.3 46 39.1 121 6.94 78 15.8 80 10.6 81 8.82 83 18.1 98 7.09 22
Modified CLG [34]65.5 7.63 85 11.6 41 9.65 94 10.7 109 17.2 106 10.7 114 8.25 91 14.3 112 6.60 95 22.4 81 21.1 86 41.8 52 30.6 52 26.9 20 45.8 75 27.7 17 19.2 18 36.3 51 6.69 32 14.9 45 10.4 32 8.41 44 17.0 42 7.35 78
Fusion [6]65.8 7.13 33 12.3 72 8.60 31 7.18 42 13.1 39 6.56 48 7.63 49 10.9 39 6.13 71 22.5 87 21.1 86 41.5 28 30.7 71 28.2 115 44.3 2 28.1 52 23.8 120 35.2 3 7.22 97 17.9 104 10.6 81 9.64 118 19.9 122 7.32 69
F-TV-L1 [15]66.7 8.24 101 13.1 99 9.92 101 9.28 94 16.3 93 7.48 87 8.00 79 13.2 99 6.35 84 22.3 76 20.9 76 42.3 81 29.9 2 26.9 20 44.8 6 27.9 34 19.4 23 36.5 68 6.87 70 15.4 69 10.5 57 8.46 51 16.8 32 7.58 98
IIOF-NLDP [131]67.0 7.04 20 10.9 17 8.36 5 7.81 62 14.8 67 6.64 53 8.07 84 11.0 42 6.12 68 22.3 76 20.0 39 42.8 100 30.4 26 27.0 29 45.9 93 29.1 106 23.2 117 36.5 68 8.40 128 24.6 129 11.3 112 8.78 81 17.8 84 6.86 2
FlowNet2 [122]67.5 9.30 114 14.6 115 10.5 111 8.42 77 14.6 64 9.24 103 8.03 80 12.5 82 6.14 72 22.2 70 21.9 104 41.8 52 30.9 100 27.5 87 45.8 75 28.0 43 20.5 53 36.0 17 6.72 41 14.9 45 10.4 32 8.31 32 16.9 40 7.01 12
Complementary OF [21]68.5 7.11 29 12.1 61 8.50 17 7.17 41 14.0 49 6.58 50 8.76 106 12.0 66 6.55 92 22.3 76 21.4 95 42.6 97 30.6 52 27.5 87 45.2 14 28.1 52 20.9 66 36.4 60 7.15 91 16.7 95 10.5 57 9.09 101 18.7 111 7.38 80
SimpleFlow [49]68.8 7.37 61 12.4 81 8.74 49 7.88 65 14.3 55 6.50 44 8.59 102 11.5 53 6.51 90 21.6 24 19.3 21 41.8 52 30.6 52 27.3 68 45.5 38 28.5 82 22.9 114 36.2 37 7.66 115 20.5 123 10.8 99 8.89 88 18.2 101 7.15 37
LDOF [28]69.3 8.08 96 12.3 72 9.79 99 8.94 89 14.9 70 9.18 101 8.23 90 13.5 102 6.52 91 22.3 76 21.1 86 42.4 89 30.6 52 27.0 29 45.8 75 27.9 34 18.8 10 36.6 74 6.77 52 15.3 64 10.4 32 8.44 50 17.1 50 7.38 80
ROF-ND [107]70.3 7.46 74 11.0 20 8.77 52 7.96 66 15.4 75 6.76 63 7.55 37 11.0 42 5.85 40 23.3 102 23.5 121 41.6 35 30.6 52 27.1 40 45.8 75 28.3 64 22.7 109 35.9 13 7.52 109 17.5 101 11.4 115 9.25 109 18.7 111 7.27 62
TF+OM [100]70.5 7.41 67 12.1 61 9.19 83 7.21 45 12.9 34 7.83 88 7.55 37 12.3 76 5.82 34 22.2 70 21.0 82 41.9 65 30.8 83 27.5 87 46.0 104 28.3 64 20.5 53 36.8 88 6.97 81 16.3 89 10.5 57 8.65 73 17.3 64 7.75 106
Local-TV-L1 [65]70.6 8.46 107 12.6 86 10.4 109 9.68 100 16.0 91 8.93 98 7.56 41 11.2 49 5.84 37 23.1 98 20.4 56 46.0 121 30.6 52 27.1 40 45.9 93 30.1 118 19.1 14 39.9 124 6.72 41 14.9 45 10.5 57 8.13 14 16.1 6 7.58 98
TriFlow [95]71.2 7.77 92 13.7 110 9.28 86 8.98 90 15.7 83 9.30 105 7.65 53 12.4 79 5.84 37 22.0 56 20.9 76 41.1 3 30.9 100 27.7 104 45.7 59 28.4 76 21.3 79 36.3 51 6.85 63 15.5 73 10.4 32 8.69 77 17.4 70 7.23 56
Classic++ [32]71.9 7.49 76 12.5 84 9.11 76 8.07 69 15.2 73 6.67 57 7.89 74 12.6 84 6.04 66 22.3 76 20.7 70 42.2 79 30.6 52 27.2 54 45.7 59 29.0 101 21.0 69 37.6 108 6.81 58 15.2 60 10.5 57 8.62 69 17.4 70 7.46 85
Occlusion-TV-L1 [63]72.0 7.44 71 12.3 72 9.14 78 8.91 88 16.5 96 6.85 68 7.83 69 12.8 89 6.32 80 22.6 90 21.5 99 42.5 91 30.5 36 26.9 20 45.8 75 28.4 76 19.6 29 37.1 97 7.15 91 14.8 39 10.7 91 8.51 57 17.1 50 7.34 75
Nguyen [33]72.4 9.74 117 12.6 86 12.4 121 12.3 119 18.6 121 11.1 115 8.27 93 14.8 114 6.69 97 23.4 105 21.7 101 41.8 52 30.3 19 26.8 15 45.3 19 27.4 4 19.6 29 35.7 9 7.24 99 18.3 107 10.5 57 8.37 41 17.0 42 7.22 55
2D-CLG [1]72.4 8.44 105 12.3 72 10.6 113 11.9 117 18.0 116 12.3 121 8.94 109 13.9 107 7.33 112 23.1 98 21.2 93 41.3 17 30.5 36 26.9 20 45.8 75 27.6 14 19.2 18 36.2 37 7.14 89 17.2 99 10.5 57 8.37 41 16.5 23 7.20 49
FlowNetS+ft+v [112]73.4 7.81 94 11.7 45 9.63 93 9.77 102 16.8 100 9.16 100 8.06 83 13.4 101 6.36 85 22.1 64 20.7 70 42.1 76 30.8 83 27.4 80 45.8 75 27.7 17 19.4 23 36.3 51 7.01 83 16.4 91 10.5 57 8.51 57 17.2 59 7.33 72
Aniso-Texture [82]74.2 7.16 35 11.6 41 8.78 54 8.84 86 16.5 96 6.86 70 8.38 96 11.8 61 5.99 60 22.4 81 21.4 95 42.5 91 31.0 104 27.5 87 46.0 104 29.0 101 24.2 123 36.7 83 6.70 35 14.7 30 10.3 6 8.90 89 18.0 91 7.27 62
Shiralkar [42]74.3 7.48 75 12.8 93 8.80 60 9.00 92 15.8 85 6.65 54 8.52 100 16.1 118 6.84 101 23.4 105 22.3 108 41.6 35 30.0 3 27.0 29 44.5 3 28.7 92 21.1 73 37.1 97 7.49 107 18.7 115 10.6 81 8.64 72 17.7 79 6.93 6
Adaptive [20]75.3 7.71 90 13.2 102 9.21 85 9.40 97 16.8 100 7.07 76 7.87 73 12.4 79 6.12 68 22.0 56 20.3 50 41.8 52 30.7 71 27.3 68 45.6 48 28.4 76 20.7 64 36.8 88 6.95 80 16.0 86 10.4 32 8.87 85 17.9 87 7.55 95
CNN-flow-warp+ref [117]77.3 7.35 56 10.8 13 9.30 88 8.87 87 16.2 92 8.14 92 8.60 103 14.1 110 6.62 96 23.7 109 21.9 104 42.7 98 30.8 83 27.3 68 45.9 93 28.0 43 19.1 14 36.7 83 7.37 102 18.5 112 10.6 81 8.33 35 16.8 32 7.27 62
CRTflow [80]77.8 7.69 88 12.6 86 9.28 86 8.45 80 15.5 77 6.81 66 8.55 101 14.0 109 7.29 110 22.4 81 20.7 70 43.8 108 30.7 71 27.2 54 45.7 59 28.1 52 19.6 29 36.7 83 6.87 70 15.8 80 10.6 81 8.59 65 17.2 59 7.65 103
Black & Anandan [4]78.0 8.54 108 12.8 93 10.2 107 10.9 111 17.3 109 9.40 106 9.06 111 13.6 103 6.99 105 22.9 95 21.3 94 41.7 45 30.7 71 27.2 54 45.9 93 28.0 43 18.6 7 36.7 83 6.93 76 15.9 84 10.4 32 8.46 51 17.0 42 7.20 49
HBpMotionGpu [43]78.8 9.39 115 14.6 115 11.3 117 11.7 116 18.9 123 11.5 118 7.55 37 11.1 45 6.00 62 23.3 102 22.3 108 43.5 107 30.3 19 27.2 54 45.2 14 28.7 92 20.9 66 37.1 97 6.62 17 14.2 15 10.5 57 8.99 98 17.8 84 8.04 112
StereoOF-V1MT [119]79.1 7.65 86 13.5 107 8.77 52 8.69 84 15.9 90 6.52 46 9.43 116 15.4 116 7.23 108 24.4 115 22.3 108 43.2 105 30.5 36 27.2 54 45.0 9 28.9 99 21.2 75 37.2 100 7.77 118 19.4 118 11.0 104 8.26 29 16.4 17 6.93 6
GraphCuts [14]79.2 8.65 110 14.1 114 9.83 100 8.28 73 14.2 52 9.28 104 9.89 120 10.6 27 7.38 113 23.0 96 21.1 86 42.5 91 30.3 19 27.3 68 44.7 5 27.2 1 21.4 80 34.7 1 7.42 105 17.8 102 11.0 104 9.32 112 18.9 113 7.66 104
HBM-GC [105]80.5 7.91 95 12.6 86 9.75 97 7.51 55 13.9 46 6.80 65 7.29 13 9.43 2 5.94 53 22.0 56 19.7 34 42.3 81 32.1 122 28.6 119 48.0 123 30.0 115 24.6 126 37.8 110 7.14 89 14.8 39 11.6 118 8.95 95 17.7 79 8.28 115
Steered-L1 [118]80.7 7.06 25 12.2 69 8.59 28 7.40 50 14.3 55 6.83 67 8.48 99 11.7 58 6.69 97 22.8 91 20.9 76 42.7 98 31.2 111 28.1 113 45.8 75 28.3 64 21.2 75 36.7 83 7.25 100 17.8 102 10.9 100 9.00 99 18.3 104 7.58 98
CBF [12]80.9 7.41 67 11.9 55 9.31 90 8.07 69 14.9 70 7.14 80 7.69 59 11.1 45 5.95 54 22.8 91 20.7 70 45.1 117 30.8 83 27.3 68 47.0 120 28.2 61 20.6 61 36.5 68 7.17 94 16.6 94 11.2 110 9.16 106 17.9 87 8.83 122
TriangleFlow [30]80.9 7.79 93 13.0 98 9.16 82 8.36 76 15.5 77 6.69 58 8.20 88 11.9 64 6.59 93 22.5 87 21.0 82 42.5 91 30.1 5 27.0 29 45.0 9 28.9 99 22.6 106 36.5 68 7.42 105 18.3 107 11.0 104 9.49 115 19.3 117 7.47 88
Correlation Flow [75]81.5 7.05 23 11.7 45 8.32 3 8.29 74 15.6 80 6.56 48 7.64 51 10.8 36 5.89 48 22.2 70 20.1 43 42.9 103 31.7 116 27.7 104 49.9 128 29.6 110 23.8 120 37.2 100 7.62 113 19.0 117 11.3 112 9.22 108 18.6 110 7.51 94
IAOF2 [51]83.3 8.43 104 13.6 109 9.76 98 9.86 104 17.4 110 8.67 94 7.74 64 12.2 72 6.33 81 23.1 98 21.7 101 42.3 81 31.0 104 27.9 108 45.6 48 28.5 82 21.0 69 36.6 74 6.71 37 15.0 52 10.3 6 9.14 104 18.4 107 7.49 92
BriefMatch [124]85.2 7.38 65 12.0 57 8.85 64 7.71 59 14.5 62 7.86 89 8.77 107 11.7 58 7.25 109 24.2 113 22.0 106 46.2 122 30.8 83 27.4 80 46.1 107 31.8 128 21.7 85 41.3 128 6.85 63 15.3 64 10.7 91 8.51 57 17.1 50 7.56 97
SegOF [10]86.8 8.16 99 12.4 81 10.1 106 9.10 93 15.5 77 8.83 97 9.48 117 14.1 110 7.46 115 22.8 91 23.0 119 41.6 35 30.8 83 27.4 80 45.8 75 28.3 64 22.0 97 36.3 51 7.83 119 21.5 125 11.0 104 8.46 51 17.1 50 7.17 43
BlockOverlap [61]87.8 8.81 112 12.4 81 11.1 116 10.0 106 15.8 85 10.6 113 7.84 70 10.4 19 6.59 93 23.3 102 20.4 56 46.3 123 31.9 119 27.9 108 48.8 126 30.3 121 19.7 36 39.8 123 7.08 87 14.5 24 11.7 121 8.35 39 16.1 6 8.60 120
TV-L1-improved [17]88.1 7.55 81 12.9 95 9.15 81 9.36 95 16.9 102 7.19 82 8.63 104 12.2 72 6.92 103 22.2 70 21.0 82 42.3 81 30.8 83 27.5 87 45.6 48 28.5 82 21.4 80 36.8 88 7.38 103 18.6 114 10.7 91 8.94 93 18.0 91 7.75 106
Dynamic MRF [7]88.6 7.29 52 13.1 99 8.69 40 8.20 72 16.3 93 6.74 60 9.18 113 16.4 121 7.22 107 24.5 117 23.1 120 44.4 111 30.3 19 27.2 54 45.1 12 29.2 107 23.4 118 37.2 100 7.64 114 19.8 121 10.7 91 9.14 104 18.0 91 7.48 91
AdaConv-v1 [126]89.2 9.81 119 13.9 112 11.6 119 12.1 118 17.6 112 16.0 127 11.4 127 16.1 118 13.1 129 26.5 126 24.4 126 45.3 118 28.4 1 24.4 1 44.6 4 28.4 76 18.1 4 37.7 109 7.74 117 16.3 89 13.1 130 8.25 27 15.1 2 10.1 129
LocallyOriented [52]89.5 8.08 96 13.1 99 9.72 95 9.73 101 17.0 104 7.88 90 8.34 95 12.8 89 6.34 82 23.0 96 22.1 107 43.0 104 30.6 52 27.2 54 45.6 48 30.0 115 21.9 92 38.5 119 7.02 84 15.8 80 10.5 57 9.05 100 18.4 107 7.39 82
SPSA-learn [13]90.2 8.28 102 12.9 95 9.95 103 9.92 105 16.3 93 9.49 107 9.15 112 12.8 89 7.30 111 23.1 98 20.5 62 41.6 35 30.8 83 27.5 87 45.7 59 28.0 43 20.4 50 36.3 51 8.81 130 27.1 131 11.8 122 10.0 123 21.0 126 7.20 49
Rannacher [23]90.8 7.69 88 13.2 102 9.32 91 9.37 96 16.9 102 7.28 83 8.67 105 13.0 93 6.91 102 22.2 70 21.1 86 42.4 89 30.8 83 27.5 87 45.7 59 28.5 82 21.2 75 36.9 93 7.35 101 18.5 112 10.7 91 8.90 89 18.0 91 7.78 108
Ad-TV-NDC [36]91.3 10.8 122 13.9 112 13.4 122 11.6 115 17.6 112 11.2 116 7.77 67 12.3 76 6.12 68 24.0 111 21.6 100 44.4 111 31.1 108 27.6 100 46.1 107 29.0 101 19.3 22 38.0 114 6.87 70 15.4 69 10.5 57 8.59 65 17.0 42 7.71 105
ACK-Prior [27]92.5 7.12 31 11.7 45 8.57 25 7.08 40 13.8 45 6.34 25 8.81 108 11.8 61 6.69 97 22.5 87 21.4 95 42.3 81 32.6 126 29.3 125 48.2 124 30.7 124 25.6 128 38.1 116 7.95 122 18.8 116 12.0 123 10.8 128 21.8 128 8.53 119
Horn & Schunck [3]93.0 8.45 106 13.3 106 10.0 104 11.4 114 18.1 118 9.84 111 9.65 118 16.1 118 7.89 118 24.6 118 22.8 113 42.8 100 30.6 52 27.2 54 45.6 48 28.3 64 19.4 23 36.8 88 7.41 104 18.0 106 10.6 81 8.94 93 17.7 79 7.55 95
UnFlow [129]95.1 9.13 113 15.0 119 10.7 114 10.9 111 18.1 118 9.23 102 9.21 114 16.9 124 7.18 106 22.4 81 21.8 103 41.8 52 30.8 83 27.6 100 45.9 93 28.6 87 22.7 109 36.0 17 6.94 78 15.6 74 10.5 57 10.0 123 19.3 117 7.47 88
TI-DOFE [24]95.7 11.8 124 14.7 117 14.8 125 13.9 126 20.3 127 13.5 125 9.26 115 16.5 123 7.69 117 25.1 120 22.8 113 43.3 106 30.2 9 27.1 40 45.4 23 28.4 76 19.6 29 36.8 88 7.22 97 17.2 99 10.7 91 9.21 107 18.1 98 7.59 101
StereoFlow [44]95.9 13.8 127 20.2 130 14.0 123 14.1 127 21.3 130 12.0 120 7.79 68 13.3 100 5.98 58 22.4 81 20.9 76 42.1 76 33.7 130 32.3 130 46.1 107 30.5 122 31.8 131 36.3 51 6.63 20 14.7 30 10.4 32 9.98 122 21.0 126 7.42 84
Filter Flow [19]100.9 8.30 103 13.2 102 10.0 104 10.8 110 17.1 105 11.7 119 7.96 76 12.1 69 6.38 86 23.7 109 20.9 76 44.5 115 31.5 115 28.2 115 46.7 118 28.8 97 21.0 69 37.3 105 7.15 91 17.0 98 10.7 91 9.54 117 18.9 113 8.47 118
NL-TV-NCC [25]103.1 7.56 82 12.7 92 8.62 33 8.00 67 15.4 75 6.74 60 8.46 98 13.1 94 6.70 100 24.2 113 24.0 124 45.0 116 32.8 127 28.3 118 52.0 131 29.4 108 24.1 122 36.9 93 7.89 121 17.9 104 12.4 127 10.1 125 19.9 122 8.92 123
SILK [79]104.2 9.77 118 15.1 120 11.8 120 12.3 119 18.7 122 11.2 116 10.3 121 16.4 121 8.14 120 25.2 121 22.8 113 45.9 120 30.8 83 27.5 87 45.7 59 30.6 123 20.3 46 40.1 126 7.19 96 16.8 96 10.9 100 8.87 85 17.6 76 7.50 93
Bartels [41]104.4 8.10 98 13.8 111 9.94 102 8.35 75 15.8 85 8.75 95 8.11 86 12.1 69 6.97 104 24.1 112 22.7 111 47.6 125 32.4 123 27.8 107 51.1 130 35.4 130 23.0 115 46.5 130 7.18 95 14.9 45 12.3 126 9.36 114 18.0 91 9.76 127
SLK [47]109.5 11.4 123 15.4 121 14.4 124 12.4 121 18.0 116 12.6 122 10.9 125 17.6 126 8.85 122 27.8 127 25.2 127 46.6 124 30.6 52 28.1 113 43.6 1 29.0 101 21.9 92 37.0 96 8.25 125 22.4 126 11.3 112 9.33 113 18.5 109 7.91 111
GroupFlow [9]110.1 10.1 121 16.9 125 11.3 117 10.4 108 17.8 115 10.0 112 10.8 124 17.5 125 9.21 123 23.6 108 23.9 122 42.5 91 31.9 119 29.3 125 46.2 113 30.1 118 24.5 125 37.8 110 7.55 110 18.4 110 10.6 81 9.52 116 19.8 121 6.89 3
Heeger++ [104]110.3 9.81 119 17.3 126 10.4 109 11.3 113 17.2 106 9.67 108 13.6 129 23.8 130 10.2 127 26.3 124 22.8 113 44.4 111 31.8 118 28.9 124 46.3 115 29.6 110 22.0 97 37.5 107 8.17 124 19.8 121 10.9 100 9.10 102 18.2 101 7.02 13
Learning Flow [11]112.2 8.21 100 14.8 118 9.74 96 9.78 103 17.6 112 8.11 91 9.68 119 15.5 117 7.56 116 25.0 119 24.3 125 45.4 119 31.9 119 28.7 122 47.3 121 29.4 108 22.0 97 37.8 110 7.49 107 18.3 107 10.9 100 10.2 126 20.2 124 8.31 116
2bit-BM-tele [98]113.1 8.61 109 13.5 107 10.5 111 10.0 106 17.5 111 9.73 109 8.26 92 11.5 53 7.40 114 24.4 115 22.7 111 48.1 126 32.5 125 28.6 119 50.2 129 34.7 129 24.3 124 44.9 129 9.35 131 26.2 130 13.8 131 9.25 109 17.3 64 10.2 130
FFV1MT [106]116.8 9.53 116 16.7 124 10.7 114 12.6 124 18.2 120 12.8 123 13.3 128 23.5 129 10.5 128 26.3 124 22.8 113 44.4 111 31.4 114 28.2 115 46.1 107 29.8 113 20.6 61 38.1 116 8.32 127 20.5 123 11.0 104 10.5 127 20.4 125 8.45 117
Adaptive flow [45]119.5 13.2 126 15.9 122 16.2 127 14.2 128 19.9 126 16.4 128 9.02 110 13.1 94 8.03 119 26.0 123 22.8 113 48.6 127 32.4 123 29.4 127 47.9 122 30.1 118 24.6 126 38.0 114 7.55 110 16.9 97 12.2 124 9.85 119 19.5 119 8.97 126
FOLKI [16]119.6 15.0 129 17.4 127 19.4 129 14.3 129 20.9 129 14.4 126 10.7 123 19.2 128 9.99 126 29.8 129 26.8 128 53.1 130 31.3 112 28.6 119 45.8 75 30.0 115 21.7 85 38.9 120 7.85 120 19.4 118 11.6 118 9.85 119 19.2 116 8.80 121
Pyramid LK [2]122.0 16.3 130 16.1 123 21.6 130 16.0 130 20.3 127 18.2 130 16.7 130 15.3 115 14.3 130 35.7 131 36.7 131 56.5 131 32.8 127 31.2 129 45.7 59 29.7 112 22.1 101 38.2 118 8.31 126 23.1 127 11.6 118 11.8 129 25.0 129 8.22 114
PGAM+LK [55]123.0 12.7 125 18.1 128 15.3 126 12.4 121 19.1 124 13.0 124 11.1 126 18.6 127 9.33 125 29.2 128 27.5 129 51.6 129 31.7 116 28.8 123 46.7 118 31.3 126 23.7 119 40.1 126 7.67 116 19.4 118 11.4 115 9.89 121 19.5 119 8.95 124
HCIC-L [99]123.8 18.0 131 18.7 129 23.1 131 12.7 125 17.2 106 17.0 129 10.5 122 14.4 113 8.49 121 25.7 122 23.9 122 44.3 110 33.2 129 29.8 128 49.0 127 31.7 127 26.4 130 39.2 122 7.98 123 18.4 110 12.4 127 12.4 130 25.3 131 8.96 125
Periodicity [78]129.6 14.9 128 20.8 131 18.2 128 20.1 131 22.0 131 21.5 131 17.7 131 26.4 131 16.1 131 29.8 129 34.8 130 49.7 128 35.4 131 34.2 131 48.7 125 37.1 131 25.8 129 47.4 131 8.68 129 23.6 128 12.2 124 13.3 131 25.1 130 11.6 131
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. Submitted to TIP 2016.
[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-invariantdescriptors in variational optical flow estimation. ICIP 2017.
* 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.