Optical flow evaluation results Statistics:     Average   SD   R2.5   R5.0   R10.0   A90   A95   A99  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
A99
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
PMMST [114]10.2 11.2 10 21.1 6 2.71 2 13.8 8 19.7 7 3.65 22 10.3 1 19.2 7 2.71 1 16.8 4 30.8 22 7.53 30 41.1 5 51.1 7 10.0 17 24.6 2 43.0 6 4.93 12 34.2 10 70.9 12 4.04 8 28.8 14 45.4 24 3.42 9
MDP-Flow2 [68]10.7 11.0 3 20.7 5 2.71 2 13.9 10 19.9 9 3.46 2 10.3 1 20.3 14 3.00 2 16.7 3 30.0 14 7.35 7 41.0 4 50.7 4 10.1 29 27.1 35 44.9 19 4.97 19 33.6 3 70.1 7 3.92 3 29.2 18 47.0 35 3.42 9
NNF-Local [87]19.9 11.4 16 21.6 9 2.71 2 12.8 1 18.4 2 3.56 4 10.4 3 20.0 12 3.00 2 19.8 58 37.3 88 7.35 7 41.5 11 51.4 9 10.0 17 28.2 61 47.3 37 5.07 45 34.5 13 71.9 23 4.04 8 29.1 17 46.1 30 3.37 2
SepConv-v1 [127]20.5 9.68 1 19.1 1 2.52 1 15.4 35 20.1 11 5.26 101 11.0 10 16.7 1 3.87 95 20.4 72 26.8 1 9.59 117 41.9 12 52.5 16 9.00 2 24.7 3 42.4 3 4.69 1 30.7 1 67.4 1 3.92 3 24.7 1 35.8 1 3.32 1
NN-field [71]22.8 11.5 24 22.9 23 2.71 2 13.0 3 18.6 3 3.42 1 12.3 77 19.7 8 3.00 2 21.1 79 39.8 102 7.44 17 41.4 9 51.4 9 10.0 17 27.5 43 46.4 28 4.97 19 33.8 6 71.0 14 4.04 8 29.3 20 46.2 31 3.37 2
NNF-EAC [103]26.7 11.5 24 21.7 10 3.11 73 14.5 22 21.0 24 3.70 24 12.3 77 22.6 35 3.00 2 17.7 16 32.4 41 7.55 37 43.2 38 55.1 42 10.1 29 25.1 6 43.8 9 4.90 6 34.0 8 70.5 9 4.08 38 29.4 22 47.5 40 3.42 9
DeepFlow2 [108]28.5 11.4 16 23.5 27 3.00 60 16.7 57 23.0 59 4.04 55 11.0 10 20.3 14 3.00 2 19.0 47 29.8 12 7.53 30 42.7 25 54.0 25 10.3 52 25.0 4 43.0 6 4.93 12 35.2 27 73.8 34 4.04 8 28.9 15 44.9 21 3.56 67
DeepFlow [86]28.7 11.3 14 24.2 40 3.00 60 16.6 56 23.0 59 4.32 69 11.0 10 20.3 14 3.00 2 19.3 49 28.1 6 7.59 45 42.7 25 54.5 30 10.2 47 25.2 8 44.1 10 5.00 39 32.9 2 68.2 2 4.04 8 28.4 10 44.6 17 3.56 67
SuperFlow [81]29.8 11.0 3 22.1 17 3.11 73 17.1 62 22.7 51 4.69 82 11.7 46 18.7 2 3.37 67 18.7 40 27.4 3 7.70 65 41.3 7 51.2 8 9.98 13 26.3 22 46.9 31 4.80 3 34.7 16 76.0 45 4.08 38 28.1 8 41.5 5 3.42 9
PH-Flow [101]30.4 11.9 56 25.7 69 2.83 18 13.3 5 19.7 7 3.56 4 10.7 5 22.7 36 3.00 2 16.5 2 30.2 15 7.33 3 42.3 18 52.1 13 10.1 29 28.7 77 50.9 91 5.20 69 35.6 34 77.0 55 4.04 8 29.6 28 47.0 35 3.51 51
DF-Auto [115]30.7 10.9 2 19.2 2 3.11 73 17.2 65 23.4 67 4.43 74 10.4 3 20.6 21 3.00 2 18.1 27 29.7 9 7.55 37 41.4 9 52.1 13 10.0 17 26.2 17 47.2 34 4.97 19 35.2 27 79.3 67 4.08 38 29.6 28 44.7 18 3.56 67
CombBMOF [113]31.1 12.0 61 24.3 41 2.83 18 14.3 16 20.6 16 3.56 4 11.3 27 25.7 61 3.00 2 20.3 71 34.9 66 7.55 37 43.2 38 54.0 25 10.1 29 26.4 23 47.7 44 4.90 6 36.2 52 71.4 18 4.08 38 29.5 25 45.7 27 3.37 2
CBF [12]32.6 11.0 3 19.8 3 3.00 60 17.1 62 22.9 56 4.24 66 12.0 61 19.0 4 3.00 2 17.8 21 28.0 5 7.85 79 40.6 3 49.9 2 9.97 9 26.2 17 44.6 14 4.97 19 36.3 54 76.3 50 4.12 81 27.9 4 41.2 4 3.70 104
Aniso. Huber-L1 [22]33.6 11.4 16 21.7 10 3.11 73 19.7 101 24.7 100 4.55 78 12.0 61 19.7 8 3.11 62 18.4 32 29.8 12 7.55 37 42.5 21 54.4 29 9.98 13 25.2 8 42.2 2 4.83 4 35.6 34 71.5 19 4.04 8 27.9 4 42.0 7 3.56 67
LME [70]33.8 11.4 16 22.0 15 2.71 2 15.1 30 21.8 35 3.87 48 11.3 27 36.0 119 3.00 2 17.4 8 32.0 38 7.48 21 44.5 61 57.0 59 11.4 123 27.6 45 47.2 34 4.97 19 33.6 3 69.7 4 4.04 8 30.0 34 48.6 51 3.42 9
WLIF-Flow [93]35.1 11.5 24 22.1 17 2.83 18 15.2 31 21.6 32 3.79 39 11.3 27 26.4 69 3.00 2 17.4 8 30.3 17 7.59 45 42.5 21 53.5 21 10.4 61 29.0 83 51.1 94 5.29 86 34.8 19 69.7 4 4.04 8 30.0 34 48.4 47 3.46 36
CLG-TV [48]35.3 11.1 8 21.8 13 3.11 73 18.8 85 24.0 81 4.43 74 11.3 27 20.0 12 3.70 82 18.6 38 28.9 7 7.72 70 42.8 28 55.0 41 10.0 17 25.0 4 42.9 5 4.93 12 36.0 46 71.6 20 4.04 8 29.0 16 44.0 13 3.56 67
IROF++ [58]35.4 11.9 56 24.1 37 2.83 18 14.7 25 21.3 26 3.56 4 12.1 74 29.0 89 3.00 2 16.3 1 27.9 4 7.35 7 43.9 50 56.0 50 11.1 93 26.4 23 47.0 33 4.93 12 34.5 13 72.3 24 4.08 38 30.3 44 49.3 59 3.56 67
FMOF [94]36.7 12.2 81 24.5 50 2.94 48 14.0 11 20.0 10 3.56 4 12.3 77 27.7 77 3.00 2 19.8 58 35.4 71 7.70 65 42.4 19 52.1 13 10.1 29 28.1 57 49.1 60 4.93 12 34.6 15 72.7 28 3.87 1 30.2 40 47.6 43 3.42 9
Brox et al. [5]37.3 11.4 16 24.9 62 2.94 48 15.9 42 22.2 43 4.04 55 11.3 27 21.0 22 3.37 67 18.4 32 27.0 2 7.59 45 42.2 16 53.3 19 10.0 17 28.2 61 51.5 97 5.00 39 36.8 57 88.0 94 4.04 8 28.4 10 42.3 8 3.42 9
IROF-TV [53]37.9 11.7 38 24.7 57 3.00 60 15.5 38 22.0 41 3.70 24 11.0 10 23.7 46 3.00 2 17.3 6 31.3 26 7.57 44 43.8 48 56.0 50 11.2 100 27.6 45 48.4 51 4.97 19 35.9 44 74.5 41 4.08 38 28.0 6 42.6 9 3.56 67
ALD-Flow [66]38.8 12.0 61 28.4 94 3.11 73 16.3 49 22.8 53 3.83 43 11.0 10 21.7 28 3.00 2 17.9 24 33.6 54 7.39 12 43.4 43 54.6 34 10.8 80 25.8 12 44.8 18 5.00 39 34.1 9 70.4 8 4.04 8 31.9 73 50.3 67 3.46 36
nLayers [57]39.5 11.8 46 22.9 23 2.83 18 14.1 12 20.4 13 3.56 4 11.0 10 19.7 8 3.00 2 18.3 30 34.2 62 7.39 12 46.7 112 60.1 108 11.0 87 27.9 51 50.1 67 5.20 69 35.5 32 72.6 27 4.08 38 30.8 49 49.3 59 3.42 9
LDOF [28]41.0 11.4 16 22.5 20 3.56 114 16.1 44 21.4 31 6.35 118 12.0 61 20.3 14 3.70 82 19.0 47 29.7 9 7.94 84 41.2 6 50.9 5 10.1 29 26.8 28 50.2 69 4.90 6 34.8 19 80.2 71 4.08 38 29.4 22 44.5 15 3.46 36
p-harmonic [29]41.2 11.4 16 23.5 27 2.83 18 19.1 89 24.3 89 4.80 87 11.3 27 22.0 30 3.70 82 20.9 77 31.7 32 7.62 51 42.6 24 54.2 27 10.1 29 25.7 11 43.5 8 5.07 45 36.1 50 71.8 21 4.08 38 29.6 28 46.5 32 3.51 51
Layers++ [37]41.3 11.4 16 21.7 10 2.94 48 12.8 1 18.2 1 3.46 2 11.0 10 26.7 72 3.00 2 17.7 16 32.9 46 7.53 30 46.6 110 60.9 119 10.6 73 30.9 114 60.2 120 5.00 39 34.9 25 72.7 28 3.87 1 29.9 33 47.5 40 3.46 36
MDP-Flow [26]41.4 11.2 10 21.2 7 2.71 2 14.2 14 20.5 15 3.70 24 10.7 5 19.0 4 3.00 2 19.7 56 32.4 41 7.70 65 44.2 53 57.0 59 11.2 100 30.0 103 51.4 96 5.51 114 36.1 50 72.9 31 4.08 38 30.8 49 48.4 47 3.42 9
Second-order prior [8]41.8 11.3 14 22.0 15 3.11 73 19.0 88 24.2 87 4.32 69 13.3 88 27.7 77 3.70 82 18.8 43 31.6 31 7.51 25 42.9 30 54.7 36 10.0 17 26.2 17 45.0 20 4.97 19 35.6 34 71.2 15 4.04 8 29.5 25 45.4 24 3.56 67
SIOF [67]42.4 11.7 38 23.1 25 3.11 73 19.4 96 24.8 103 4.76 84 11.3 27 25.7 61 3.11 62 18.4 32 31.4 28 8.04 89 40.3 2 50.3 3 9.95 7 25.8 12 45.3 22 4.97 19 33.9 7 71.2 15 4.08 38 30.0 34 47.4 37 3.70 104
Local-TV-L1 [65]43.0 11.2 10 21.5 8 3.56 114 19.6 99 24.4 92 5.57 107 11.0 10 19.1 6 3.00 2 18.3 30 30.4 20 7.87 82 42.8 28 54.5 30 10.2 47 26.2 17 44.7 15 5.45 102 34.2 10 76.1 48 4.08 38 28.0 6 42.8 11 3.65 101
COFM [59]43.7 11.8 46 24.3 41 2.94 48 14.5 22 20.9 21 3.65 22 11.0 10 26.4 69 3.00 2 17.4 8 32.3 39 7.35 7 44.2 53 55.1 42 10.1 29 30.0 103 54.4 113 5.20 69 35.8 40 79.3 67 4.08 38 31.2 55 48.8 54 3.51 51
ProbFlowFields [128]45.0 11.6 30 25.4 66 2.83 18 14.4 18 21.1 25 3.56 4 10.7 5 23.7 46 3.00 2 18.4 32 33.4 51 7.59 45 46.2 100 59.2 94 11.2 100 28.5 73 50.7 85 5.32 90 34.7 16 76.9 53 4.08 38 29.4 22 46.5 32 3.46 36
FlowFields [110]45.2 11.8 46 25.6 68 2.83 18 14.4 18 20.9 21 3.56 4 11.3 27 24.3 52 3.00 2 20.0 64 38.1 93 7.51 25 43.6 45 54.5 30 11.0 87 28.2 61 50.7 85 5.16 64 34.8 19 75.1 44 4.04 8 32.0 78 52.0 90 3.46 36
TV-L1-MCT [64]47.5 12.4 97 24.7 57 2.83 18 16.4 50 23.1 61 3.83 43 11.9 60 32.7 110 3.00 2 17.6 13 31.7 32 7.53 30 47.0 120 61.2 120 11.0 87 25.5 10 44.7 15 4.97 19 36.0 46 80.7 76 4.04 8 28.4 10 44.8 20 3.46 36
BlockOverlap [61]47.8 11.1 8 20.1 4 3.56 114 19.3 93 23.7 74 6.16 114 11.3 27 20.4 19 3.70 82 18.4 32 29.6 8 8.72 109 43.1 35 54.5 30 10.2 47 27.4 40 48.6 54 5.35 97 34.8 19 72.8 30 4.08 38 27.2 3 40.9 3 3.56 67
Sparse-NonSparse [56]48.8 12.0 61 24.3 41 2.83 18 15.0 28 21.3 26 3.56 4 11.7 46 29.0 89 3.00 2 17.6 13 29.7 9 7.39 12 45.7 85 59.3 95 11.0 87 28.8 78 48.7 56 5.07 45 38.6 83 90.1 105 4.04 8 32.4 86 51.8 86 3.42 9
HAST [109]48.9 11.7 38 23.6 29 2.94 48 13.8 8 19.6 6 3.56 4 12.0 61 31.7 106 3.00 2 17.8 21 31.7 32 7.14 1 45.3 77 57.0 59 9.97 9 33.7 124 62.8 127 5.10 61 38.4 78 88.4 96 4.04 8 33.0 95 51.0 74 3.42 9
Modified CLG [34]49.7 11.0 3 21.9 14 3.11 73 19.6 99 23.9 78 5.94 112 12.4 81 26.3 67 3.87 95 19.8 58 30.8 22 8.12 94 42.1 14 52.9 17 10.1 29 27.0 31 48.1 49 5.23 76 34.7 16 70.8 11 4.08 38 29.5 25 45.3 23 3.56 67
OAR-Flow [125]49.8 12.0 61 24.9 62 3.00 60 16.4 50 22.4 46 4.08 60 11.0 10 20.5 20 3.00 2 17.4 8 33.6 54 7.33 3 46.2 100 60.0 107 11.3 116 27.0 31 47.6 41 5.23 76 37.6 67 74.0 36 4.08 38 31.0 54 49.2 57 3.46 36
CPM-Flow [116]50.2 11.8 46 27.3 84 2.83 18 14.4 18 20.4 13 3.70 24 11.7 46 24.0 49 3.00 2 21.4 86 40.1 105 7.77 74 45.5 81 58.1 76 11.2 100 26.6 27 48.0 47 5.07 45 36.0 46 72.3 24 4.04 8 30.9 52 50.4 68 3.56 67
AdaConv-v1 [126]50.8 15.0 123 28.2 93 3.70 118 17.6 70 20.7 19 7.68 126 17.4 106 22.0 30 7.00 120 27.5 116 33.7 58 17.0 127 39.9 1 49.8 1 8.19 1 23.8 1 39.5 1 4.76 2 34.2 10 68.5 3 4.12 81 26.9 2 39.5 2 3.42 9
2DHMM-SAS [92]51.0 12.2 81 24.5 50 2.83 18 17.9 73 24.1 84 3.87 48 12.0 61 28.7 86 3.00 2 17.3 6 31.4 28 7.51 25 45.1 73 58.2 80 11.2 100 27.9 51 49.0 58 4.83 4 37.0 59 76.1 48 4.08 38 31.9 73 50.5 69 3.42 9
F-TV-L1 [15]51.2 12.0 61 26.5 80 3.56 114 19.2 91 24.7 100 4.83 89 11.7 46 21.5 26 4.00 97 19.3 49 32.7 44 7.68 60 43.1 35 55.3 45 9.83 3 25.1 6 42.8 4 5.07 45 34.8 19 74.0 36 4.16 89 28.5 13 42.7 10 3.56 67
FlowFields+ [130]51.3 11.8 46 26.1 77 2.71 2 14.1 12 20.6 16 3.70 24 11.2 26 24.8 57 3.00 2 20.1 66 40.2 107 7.53 30 45.5 81 58.0 73 11.2 100 28.6 76 50.6 81 5.20 69 35.6 34 77.5 60 4.04 8 32.2 81 52.5 94 3.42 9
ComponentFusion [96]51.6 12.0 61 29.6 102 2.71 2 14.5 22 21.3 26 3.56 4 11.0 10 22.0 30 3.00 2 18.8 43 36.2 83 7.33 3 45.5 81 58.2 80 10.7 77 27.2 36 46.3 26 4.97 19 40.5 102 93.3 113 4.12 81 34.4 109 58.3 118 3.42 9
AGIF+OF [85]51.7 12.2 81 24.3 41 2.71 2 15.2 31 21.8 35 3.70 24 11.7 46 27.7 77 3.00 2 18.0 25 33.0 48 7.55 37 45.8 89 58.8 91 11.2 100 30.0 103 53.4 108 5.07 45 35.4 29 74.8 43 3.92 3 32.2 81 52.6 97 3.37 2
TC/T-Flow [76]53.0 12.4 97 26.4 79 2.83 18 16.5 54 23.1 61 3.83 43 11.0 10 22.4 34 3.00 2 18.9 45 34.5 63 7.33 3 45.5 81 58.1 76 11.4 123 27.3 39 47.6 41 4.93 12 41.1 104 80.4 74 4.20 95 30.9 52 49.7 63 3.37 2
DPOF [18]53.2 12.3 90 29.4 101 3.11 73 13.3 5 19.1 5 3.56 4 15.7 97 25.2 59 3.70 82 19.4 51 37.5 90 7.59 45 43.1 35 54.6 34 10.0 17 29.1 88 49.7 63 4.90 6 36.6 55 77.0 55 4.08 38 31.5 63 50.5 69 3.51 51
Ad-TV-NDC [36]54.7 12.2 81 22.5 20 4.32 125 20.6 120 24.8 103 5.80 108 11.7 46 21.6 27 3.37 67 21.6 87 31.8 35 8.04 89 42.5 21 53.4 20 9.97 9 26.4 23 47.6 41 5.16 64 36.8 57 70.9 12 4.08 38 28.3 9 41.8 6 3.70 104
PGM-C [120]55.4 11.8 46 27.3 84 2.83 18 14.4 18 20.7 19 3.70 24 12.3 77 23.0 40 3.00 2 20.6 75 42.3 113 7.62 51 45.8 89 59.5 101 11.2 100 27.2 36 47.4 38 4.97 19 37.1 61 79.2 65 4.04 8 32.4 86 55.0 108 3.51 51
Ramp [62]55.5 12.0 61 24.6 53 2.94 48 14.8 26 21.3 26 3.70 24 11.7 46 29.4 95 3.00 2 16.9 5 30.3 17 7.39 12 45.4 79 58.5 83 11.0 87 30.2 108 50.9 91 5.23 76 39.8 95 89.6 102 4.04 8 32.4 86 52.5 94 3.42 9
PMF [73]55.7 12.2 81 25.9 72 2.71 2 15.4 35 21.8 35 3.56 4 12.7 83 35.7 117 3.00 2 20.2 69 35.9 77 7.51 25 44.4 59 54.9 40 10.1 29 28.4 66 50.5 79 5.32 90 37.9 72 81.1 78 4.04 8 34.2 108 54.1 103 3.37 2
TF+OM [100]55.7 11.6 30 30.1 107 3.11 73 15.0 28 21.6 32 4.04 55 11.7 46 24.0 49 3.00 2 21.3 83 39.0 101 7.68 60 44.3 55 56.7 57 10.3 52 28.8 78 50.4 76 5.07 45 37.7 68 83.5 88 4.08 38 29.2 18 46.0 28 3.56 67
ComplOF-FED-GPU [35]55.9 12.0 61 27.9 90 2.94 48 15.7 41 22.2 43 3.79 39 16.0 98 21.4 24 3.70 82 18.4 32 33.6 54 7.48 21 44.9 70 57.7 67 10.7 77 27.4 40 45.9 23 5.00 39 36.6 55 78.7 64 4.08 38 32.6 93 52.3 92 3.51 51
AggregFlow [97]56.1 13.7 116 37.1 121 3.11 73 16.2 47 22.6 49 4.04 55 11.0 10 23.3 45 3.00 2 21.8 89 40.7 109 7.66 58 43.2 38 53.5 21 10.3 52 27.0 31 46.0 24 5.00 39 38.0 73 82.4 85 4.08 38 31.9 73 51.9 89 3.42 9
OFLAF [77]57.0 11.7 38 24.5 50 2.71 2 13.6 7 20.3 12 3.56 4 11.0 10 23.0 40 3.00 2 17.6 13 31.3 26 7.39 12 47.3 122 61.7 123 11.2 100 29.6 98 51.9 103 5.32 90 41.8 109 95.6 118 4.16 89 33.6 101 52.1 91 3.42 9
S2F-IF [123]57.2 12.1 74 29.8 104 2.71 2 14.2 14 20.6 16 3.56 4 11.3 27 26.3 67 3.00 2 20.2 69 40.1 105 7.53 30 45.9 94 58.7 90 11.3 116 28.4 66 50.7 85 5.20 69 35.7 38 76.0 45 4.08 38 32.3 84 53.1 98 3.46 36
Classic++ [32]57.7 11.6 30 23.7 30 3.11 73 17.8 72 24.4 92 4.08 60 11.7 46 20.3 14 3.37 67 20.1 66 33.8 59 7.62 51 44.7 64 57.8 70 10.0 17 28.0 54 49.7 63 5.35 97 37.4 65 81.4 80 4.08 38 30.7 48 49.5 61 3.56 67
Classic+NL [31]57.9 12.1 74 24.3 41 3.00 60 15.3 34 21.8 35 3.70 24 11.7 46 29.4 95 3.00 2 17.4 8 31.4 28 7.53 30 45.7 85 59.4 97 10.8 80 29.0 83 49.8 66 5.10 61 39.6 92 90.4 107 4.08 38 32.2 81 51.8 86 3.46 36
FlowNetS+ft+v [112]58.7 11.5 24 23.7 30 3.46 112 19.9 106 24.6 98 7.87 128 12.0 61 21.1 23 3.37 67 19.5 53 30.6 21 8.91 112 43.7 46 56.6 56 11.2 100 26.0 14 44.5 13 4.97 19 38.6 83 87.8 92 4.08 38 30.0 34 46.0 28 3.51 51
FC-2Layers-FF [74]59.6 12.1 74 26.0 76 2.83 18 13.0 3 18.7 4 3.56 4 11.4 43 25.7 61 3.00 2 17.8 21 33.5 53 7.48 21 46.5 106 60.3 113 11.2 100 30.4 111 52.3 107 5.32 90 39.8 95 90.0 104 4.08 38 31.8 69 51.6 82 3.46 36
MLDP_OF [89]59.7 11.9 56 24.7 57 2.83 18 17.4 68 23.8 76 3.87 48 10.7 5 24.6 55 3.00 2 20.5 74 33.6 54 8.35 101 44.1 51 56.5 54 10.1 29 29.3 91 50.5 79 5.57 115 35.8 40 73.4 33 4.20 95 31.2 55 50.6 72 3.70 104
LSM [39]59.7 12.3 90 24.7 57 2.83 18 15.4 35 21.9 39 3.56 4 12.0 61 30.3 101 3.00 2 18.7 40 33.2 50 7.44 17 46.1 98 59.4 97 11.1 93 29.3 91 51.9 103 5.07 45 39.2 88 91.0 109 4.04 8 32.3 84 52.5 94 3.42 9
RNLOD-Flow [121]60.8 11.8 46 24.6 53 2.89 44 17.3 67 24.0 81 3.74 37 12.7 83 36.0 119 3.11 62 18.1 27 31.2 25 7.48 21 45.8 89 59.6 102 11.1 93 29.3 91 50.6 81 5.16 64 35.4 29 74.1 38 4.08 38 32.0 78 51.6 82 3.42 9
TCOF [69]60.8 12.0 61 24.7 57 2.83 18 20.3 115 26.4 128 5.07 94 11.1 25 29.0 89 3.00 2 17.7 16 32.4 41 7.68 60 43.2 38 55.5 46 9.97 9 28.8 78 46.3 26 5.07 45 41.2 107 94.9 116 4.08 38 31.8 69 51.3 78 3.70 104
CRTflow [80]60.9 11.7 38 24.4 48 3.32 101 19.5 98 24.9 107 4.51 76 12.0 61 22.7 36 4.00 97 18.1 27 30.3 17 7.68 60 45.0 71 58.1 76 11.3 116 26.0 14 45.1 21 4.97 19 37.7 68 87.9 93 4.08 38 30.8 49 50.2 64 3.56 67
Fusion [6]61.4 11.6 30 24.3 41 2.89 44 15.6 39 21.9 39 3.83 43 11.0 10 23.7 46 3.37 67 21.0 78 33.4 51 7.62 51 44.1 51 56.3 53 10.1 29 30.3 110 54.1 111 5.45 102 38.0 73 83.7 89 4.08 38 34.0 106 54.7 105 3.56 67
RFlow [90]61.5 11.6 30 24.3 41 3.00 60 19.3 93 24.8 103 4.36 71 11.6 45 29.7 97 3.37 67 20.0 64 36.1 79 7.72 70 43.0 31 55.2 44 10.1 29 27.9 51 51.8 102 4.97 19 37.1 61 82.8 87 4.08 38 31.6 65 49.5 61 3.56 67
Sparse Occlusion [54]61.9 11.7 38 25.9 72 3.00 60 18.1 77 24.6 98 3.83 43 11.3 27 22.7 36 3.11 62 18.7 40 34.1 61 7.70 65 45.0 71 58.0 73 11.1 93 28.5 73 44.2 11 5.26 80 39.3 90 83.7 89 3.92 3 31.9 73 51.7 84 3.56 67
S2D-Matching [84]62.5 12.3 90 25.7 69 2.94 48 17.2 65 23.7 74 4.00 53 11.7 46 28.7 86 3.00 2 17.7 16 31.9 37 7.55 37 46.8 115 60.1 108 10.4 61 30.0 103 51.5 97 5.29 86 37.0 59 77.7 61 4.04 8 31.8 69 50.9 73 3.46 36
TC-Flow [46]63.1 12.0 61 30.3 109 2.89 44 16.8 59 23.4 67 3.92 52 11.7 46 21.4 24 3.00 2 19.5 53 36.1 79 8.12 94 46.5 106 59.8 105 11.3 116 27.0 31 48.4 51 5.26 80 35.5 32 74.6 42 4.04 8 33.3 98 54.5 104 3.51 51
HBM-GC [105]63.7 11.8 46 23.8 33 3.11 73 16.8 59 24.2 87 3.87 48 10.7 5 18.7 2 3.00 2 18.9 45 32.9 46 7.68 60 46.8 115 60.8 117 11.5 129 34.5 127 61.7 122 5.48 110 37.7 68 81.9 84 4.04 8 30.5 47 47.8 44 3.51 51
SVFilterOh [111]63.7 11.9 56 26.1 77 2.94 48 14.3 16 20.9 21 3.70 24 12.0 61 26.7 72 3.00 2 19.9 62 36.1 79 7.62 51 46.7 112 59.8 105 11.4 123 30.7 113 55.1 114 5.07 45 36.0 46 77.2 57 4.04 8 32.4 86 53.2 99 3.51 51
Black & Anandan [4]63.8 12.3 90 24.0 35 3.46 112 21.2 122 25.4 114 5.35 103 18.1 109 25.0 58 5.35 112 24.4 108 34.9 66 7.77 74 42.2 16 53.5 21 10.1 29 26.9 30 46.5 29 4.97 19 39.5 91 77.2 57 4.08 38 29.3 20 42.8 11 3.56 67
Classic+CPF [83]65.1 12.2 81 24.6 53 2.83 18 15.6 39 22.1 42 3.74 37 12.0 61 30.7 102 3.00 2 17.7 16 30.9 24 7.44 17 47.2 121 61.3 121 11.2 100 31.2 117 55.9 115 5.26 80 39.9 97 88.8 99 4.04 8 33.6 101 54.0 102 3.42 9
FESL [72]65.8 12.2 81 25.1 65 2.83 18 14.9 27 21.6 32 3.70 24 12.1 74 33.7 113 3.00 2 19.7 56 35.0 68 7.72 70 46.2 100 60.2 112 11.3 116 29.3 91 50.4 76 5.32 90 39.6 92 88.6 98 3.92 3 32.4 86 51.2 76 3.42 9
CostFilter [40]66.2 13.1 112 33.1 116 2.71 2 15.2 31 21.3 26 3.56 4 14.0 91 42.7 127 3.00 2 22.0 91 44.4 120 7.26 2 45.8 89 57.2 64 10.4 61 27.2 36 48.1 49 5.45 102 39.9 97 89.4 101 4.08 38 35.6 113 56.1 112 3.37 2
Efficient-NL [60]66.2 11.8 46 23.8 33 2.83 18 16.7 57 23.3 65 3.70 24 18.4 111 29.0 89 3.70 82 19.4 51 34.0 60 7.51 25 45.1 73 58.5 83 11.1 93 30.0 103 51.5 97 5.07 45 40.1 100 88.9 100 4.08 38 33.0 95 52.4 93 3.42 9
EpicFlow [102]66.6 11.9 56 27.6 88 2.83 18 16.0 43 22.2 43 3.79 39 11.8 59 21.7 28 3.00 2 21.3 83 42.9 115 7.85 79 46.3 103 59.4 97 11.2 100 27.4 40 47.5 40 5.16 64 38.2 75 76.6 51 4.12 81 35.2 112 58.0 116 3.56 67
Bartels [41]66.7 12.2 81 29.9 105 3.37 105 17.4 68 24.3 89 4.83 89 11.3 27 24.7 56 3.70 82 21.2 80 35.4 71 9.15 115 41.3 7 51.0 6 9.87 4 29.7 100 50.2 69 6.32 128 33.7 5 70.7 10 4.20 95 30.2 40 48.4 47 3.79 121
2D-CLG [1]66.8 11.6 30 24.1 37 3.11 73 19.4 96 23.3 65 6.24 116 18.7 112 24.3 52 4.69 106 22.4 95 31.8 35 8.66 108 43.3 42 56.1 52 10.4 61 26.0 14 44.2 11 5.35 97 40.2 101 91.5 111 4.20 95 29.6 28 44.5 15 3.51 51
Filter Flow [19]66.9 11.8 46 23.1 25 3.37 105 20.0 107 25.1 109 5.23 100 12.2 76 26.0 64 3.70 82 22.1 92 32.7 44 7.94 84 42.1 14 51.9 11 10.4 61 28.1 57 49.0 58 5.07 45 38.4 78 81.6 81 4.16 89 30.0 34 45.5 26 3.74 118
SRR-TVOF-NL [91]67.4 12.9 108 28.7 95 3.00 60 16.9 61 23.1 61 4.69 82 11.5 44 27.0 75 3.00 2 22.2 93 37.3 88 7.59 45 44.8 68 57.9 71 11.0 87 29.1 88 51.9 103 4.90 6 35.7 38 77.7 61 4.08 38 33.0 95 51.5 81 3.56 67
Steered-L1 [118]67.6 11.2 10 22.6 22 2.89 44 16.2 47 22.6 49 4.55 78 21.7 115 32.4 109 5.00 109 23.4 102 38.3 95 10.7 119 44.7 64 57.4 65 9.88 5 28.0 54 48.5 53 5.32 90 37.1 61 79.2 65 4.12 81 31.4 59 51.1 75 3.51 51
Occlusion-TV-L1 [63]70.5 11.6 30 25.0 64 3.11 73 19.8 104 26.0 122 4.83 89 11.3 27 23.0 40 3.46 79 22.5 98 43.0 117 7.94 84 43.0 31 54.8 39 9.88 5 28.0 54 50.7 85 5.32 90 39.6 92 76.6 51 4.62 118 31.5 63 50.5 69 3.56 67
EPPM w/o HM [88]71.1 12.7 105 30.9 111 2.71 2 16.1 44 23.1 61 3.70 24 17.7 107 42.4 126 3.70 82 21.3 83 42.5 114 7.70 65 43.0 31 53.1 18 10.3 52 30.2 108 57.1 117 4.97 19 38.5 81 89.6 102 4.12 81 32.4 86 51.3 78 3.42 9
OFH [38]71.4 12.0 61 27.3 84 3.00 60 18.1 77 23.4 67 4.20 65 12.4 81 32.7 110 3.00 2 18.6 38 35.4 71 7.35 7 46.5 106 60.1 108 10.8 80 27.5 43 47.2 34 5.26 80 41.1 104 81.1 78 4.20 95 35.7 114 56.1 112 3.46 36
CNN-flow-warp+ref [117]71.7 11.0 3 22.4 19 3.11 73 17.6 70 22.9 56 5.92 111 16.1 99 28.3 85 4.00 97 23.5 103 30.2 15 10.7 119 44.8 68 58.5 83 11.3 116 26.5 26 46.5 29 5.29 86 41.5 108 91.5 111 4.32 107 30.4 45 47.5 40 3.51 51
Adaptive [20]71.9 11.6 30 26.7 81 3.11 73 20.2 111 25.9 119 5.07 94 12.0 61 23.0 40 3.37 67 20.4 72 36.6 85 7.77 74 44.3 55 58.5 83 9.98 13 28.3 64 49.1 60 5.16 64 42.5 113 90.6 108 4.08 38 31.6 65 48.8 54 3.65 101
Horn & Schunck [3]73.5 12.1 74 23.7 30 3.32 101 21.4 124 25.6 117 5.89 110 17.0 103 28.2 84 5.35 112 27.3 115 37.9 91 8.04 89 42.4 19 54.3 28 10.3 52 26.2 17 44.7 15 5.07 45 40.9 103 81.7 82 4.20 95 30.2 40 44.3 14 3.70 104
IAOF [50]74.3 13.0 110 29.2 99 3.37 105 23.7 129 27.4 131 6.45 120 16.4 101 28.7 86 3.46 79 22.7 99 33.1 49 8.37 102 43.4 43 55.6 48 10.0 17 27.6 45 50.1 67 4.97 19 38.3 76 82.7 86 4.08 38 30.0 34 46.8 34 3.56 67
TV-L1-improved [17]74.8 11.5 24 25.4 66 3.11 73 20.1 110 26.0 122 5.26 101 16.8 102 19.7 8 4.04 102 19.5 53 32.3 39 7.79 77 43.8 48 56.5 54 10.0 17 28.9 82 51.1 94 5.07 45 43.2 116 98.9 122 4.43 113 31.4 59 50.2 64 3.70 104
TriFlow [95]75.2 12.5 101 36.7 120 3.00 60 18.7 83 24.5 95 4.76 84 11.7 46 28.1 83 3.00 2 21.7 88 41.4 111 7.62 51 46.8 115 60.4 114 11.2 100 29.9 101 51.7 101 4.97 19 37.8 71 76.9 53 4.08 38 31.7 68 48.6 51 3.51 51
HBpMotionGpu [43]76.0 12.3 90 32.0 114 3.79 120 20.6 120 25.4 114 6.00 113 11.3 27 26.1 66 3.00 2 23.2 101 44.0 119 7.85 79 44.3 55 56.9 58 10.8 80 29.0 83 53.5 110 5.26 80 34.9 25 69.8 6 4.04 8 31.8 69 51.4 80 3.70 104
Nguyen [33]76.3 12.0 61 25.9 72 3.37 105 21.2 122 24.5 95 6.27 117 12.7 83 28.0 81 3.70 82 23.8 104 34.7 64 8.58 106 43.0 31 54.7 36 10.1 29 27.7 50 50.7 85 4.97 19 43.4 118 93.7 114 4.43 113 30.2 40 47.4 37 3.56 67
BriefMatch [124]76.8 12.1 74 29.2 99 3.11 73 16.5 54 22.5 47 6.61 122 18.0 108 22.7 36 5.69 115 26.2 111 35.5 75 18.2 129 43.7 46 54.7 36 10.4 61 29.6 98 50.2 69 5.94 124 35.8 40 72.5 26 4.16 89 32.1 80 50.2 64 3.56 67
GraphCuts [14]77.0 13.9 119 30.2 108 3.32 101 16.4 50 22.5 47 4.36 71 33.4 127 24.1 51 5.35 112 22.3 94 34.7 64 7.87 82 44.5 61 57.0 59 9.98 13 28.3 64 50.3 74 4.90 6 38.5 81 88.2 95 4.20 95 33.9 105 53.6 101 3.56 67
FlowNet2 [122]78.5 19.1 127 47.5 128 3.11 73 17.1 62 24.1 84 4.55 78 14.2 93 29.8 99 3.37 67 23.8 104 42.9 115 8.33 99 45.9 94 58.1 76 10.6 73 27.6 45 49.4 62 4.93 12 39.2 88 81.0 77 4.08 38 31.6 65 49.2 57 3.56 67
TI-DOFE [24]78.5 12.7 105 27.6 88 3.87 124 22.2 127 25.3 111 6.66 123 14.1 92 25.3 60 4.36 104 27.7 117 38.7 99 9.06 113 42.7 25 53.6 24 10.1 29 26.8 28 48.8 57 4.97 19 38.3 76 76.0 45 4.24 105 31.9 73 44.7 18 3.87 123
ROF-ND [107]81.3 12.4 97 24.4 48 2.83 18 17.9 73 23.9 78 4.08 60 12.0 61 26.6 71 3.00 2 29.5 121 48.9 124 8.72 109 45.4 79 58.6 87 11.1 93 31.1 116 53.4 108 5.26 80 38.9 86 74.2 39 4.20 95 38.0 118 60.3 121 3.56 67
NL-TV-NCC [25]82.5 13.7 116 27.3 84 2.94 48 18.5 79 24.7 100 4.04 55 15.0 95 29.0 89 3.70 82 25.6 110 46.4 122 7.94 84 42.0 13 51.9 11 10.4 61 30.6 112 51.9 103 5.29 86 41.9 110 81.7 82 4.40 109 31.3 58 48.6 51 3.79 121
TriangleFlow [30]82.9 12.5 101 25.9 72 3.11 73 18.8 85 24.3 89 4.24 66 13.2 87 29.7 97 3.46 79 21.2 80 35.4 71 7.94 84 44.4 59 57.7 67 9.95 7 29.4 96 48.6 54 5.07 45 43.9 119 99.9 123 4.43 113 42.1 126 69.7 129 3.56 67
Complementary OF [21]83.4 12.4 97 34.5 119 2.83 18 16.4 50 23.5 71 3.79 39 30.7 121 32.2 108 7.05 123 19.9 62 43.9 118 7.44 17 46.9 118 60.4 114 10.7 77 28.1 57 47.7 44 5.23 76 41.1 104 80.3 73 4.12 81 42.0 125 62.0 123 3.56 67
Correlation Flow [75]83.5 12.6 104 28.0 91 2.71 2 20.0 107 25.8 118 4.36 71 11.3 27 22.3 33 3.00 2 20.7 76 38.6 98 7.72 70 45.7 85 59.0 93 10.3 52 33.4 123 60.4 121 5.45 102 45.6 123 99.9 123 4.40 109 33.4 99 54.9 107 3.56 67
LocallyOriented [52]84.1 12.2 81 28.1 92 3.27 99 20.5 118 25.9 119 5.07 94 14.3 94 30.0 100 3.37 67 24.2 107 41.7 112 7.66 58 44.7 64 57.1 63 10.1 29 28.8 78 47.4 38 5.48 110 42.4 111 80.6 75 4.12 81 32.4 86 51.2 76 3.56 67
IAOF2 [51]85.1 12.7 105 28.7 95 3.32 101 20.4 116 25.9 119 4.76 84 12.7 83 31.7 106 3.11 62 22.4 95 35.8 76 8.06 93 45.9 94 59.6 102 10.8 80 29.9 101 51.5 97 5.10 61 39.0 87 79.7 69 4.08 38 31.2 55 49.0 56 3.56 67
Aniso-Texture [82]86.1 11.5 24 24.1 37 2.83 18 20.2 111 26.0 122 4.97 92 20.0 114 24.4 54 3.37 67 26.9 113 50.7 125 9.11 114 46.1 98 60.5 116 11.4 123 32.7 122 62.2 126 5.94 124 37.3 64 80.2 71 4.04 8 34.1 107 55.0 108 3.42 9
ACK-Prior [27]87.8 12.5 101 29.7 103 2.83 18 16.1 44 22.7 51 4.00 53 25.6 117 27.7 77 5.72 117 22.4 95 36.0 78 10.7 119 45.7 85 59.3 95 11.4 123 31.8 120 50.6 81 5.35 97 38.8 85 79.9 70 4.16 89 33.5 100 51.7 84 3.70 104
Rannacher [23]88.4 11.7 38 28.7 95 3.16 98 20.4 116 26.3 126 5.07 94 19.0 113 26.0 64 4.80 108 19.8 58 38.1 93 7.79 77 44.5 61 57.4 65 10.1 29 29.0 83 50.3 74 5.20 69 42.6 114 97.0 119 4.40 109 33.7 103 55.9 111 3.70 104
IIOF-NLDP [131]89.1 12.9 108 29.0 98 2.71 2 18.6 82 24.8 103 4.08 60 13.4 89 26.7 72 3.00 2 21.9 90 39.8 102 8.16 96 45.8 89 59.4 97 10.4 61 31.6 119 59.9 119 6.06 127 54.7 131 99.9 123 6.03 130 35.7 114 57.2 115 3.42 9
Learning Flow [11]89.8 12.1 74 24.6 53 3.27 99 19.7 101 25.2 110 5.00 93 39.7 129 47.7 130 7.68 125 24.6 109 35.0 68 8.19 98 45.2 76 58.6 87 10.5 72 28.4 66 48.0 47 5.45 102 38.4 78 77.8 63 4.40 109 32.6 93 48.4 47 3.92 125
2bit-BM-tele [98]90.5 11.7 38 27.0 83 3.79 120 20.2 111 26.3 126 5.07 94 12.0 61 23.2 44 4.00 97 21.2 80 36.1 79 8.16 96 45.3 77 58.0 73 10.3 52 34.0 126 61.8 123 5.92 123 54.1 130 99.9 123 5.72 128 29.8 32 47.4 37 3.74 118
FOLKI [16]91.2 13.0 110 30.9 111 4.97 129 22.2 127 24.9 107 9.00 129 17.3 105 33.0 112 7.00 120 33.4 126 38.7 99 17.0 127 44.3 55 55.8 49 10.4 61 27.6 45 49.7 63 5.48 110 36.2 52 74.2 39 4.80 121 30.4 45 44.9 21 4.08 127
SimpleFlow [49]92.0 12.0 61 24.0 35 2.94 48 18.5 79 24.4 92 4.24 66 32.7 124 39.0 121 5.69 115 18.0 25 36.2 83 7.55 37 46.9 118 60.8 117 11.1 93 31.4 118 58.1 118 5.35 97 49.4 127 99.9 123 5.16 126 40.0 122 63.0 126 3.46 36
SILK [79]92.2 13.3 114 30.7 110 3.83 123 22.0 126 25.3 111 7.16 124 34.7 128 40.0 123 7.77 127 26.6 112 36.6 85 8.60 107 45.1 73 57.9 71 10.0 17 28.4 66 50.9 91 6.03 126 34.8 19 71.8 21 4.51 117 31.4 59 48.0 45 3.74 118
StereoFlow [44]93.9 22.8 131 48.3 129 3.74 119 20.5 118 26.8 129 5.07 94 11.3 27 29.3 94 3.37 67 20.1 66 37.0 87 7.62 51 59.3 129 75.2 129 10.8 80 39.3 131 71.4 130 5.45 102 35.8 40 73.9 35 4.08 38 35.7 114 55.1 110 3.70 104
StereoOF-V1MT [119]95.5 13.7 116 32.7 115 3.00 60 18.7 83 23.6 72 4.80 87 21.8 116 28.0 81 5.07 111 31.6 122 40.6 108 9.57 116 46.5 106 58.9 92 11.5 129 29.2 90 50.2 69 6.45 130 42.4 111 94.7 115 4.80 121 31.4 59 48.3 46 3.46 36
Shiralkar [42]96.0 13.2 113 31.6 113 3.00 60 19.7 101 24.5 95 4.65 81 17.0 103 30.7 102 4.08 103 32.1 124 53.1 127 8.04 89 46.3 103 59.7 104 10.3 52 28.4 66 50.2 69 5.45 102 45.5 122 95.2 117 4.24 105 39.2 121 62.6 124 3.42 9
Dynamic MRF [7]97.8 12.1 74 26.8 82 2.94 48 18.0 76 23.9 78 4.16 64 18.3 110 30.7 102 5.00 109 28.9 118 39.8 102 10.5 118 45.9 94 58.6 87 11.2 100 30.9 114 56.0 116 5.80 122 43.0 115 90.3 106 4.65 119 33.7 103 51.8 86 3.70 104
Adaptive flow [45]98.7 13.4 115 25.8 71 4.51 126 21.8 125 25.4 114 7.26 125 13.7 90 27.5 76 4.69 106 24.1 106 35.2 70 8.76 111 47.3 122 61.5 122 10.2 47 33.8 125 61.9 124 5.45 102 35.9 44 73.2 32 4.20 95 34.7 111 54.7 105 3.70 104
UnFlow [129]100.1 14.9 122 40.2 125 3.11 73 18.5 79 23.4 67 5.48 105 15.3 96 31.3 105 4.36 104 22.8 100 38.0 92 8.45 104 48.3 125 63.0 125 10.9 86 32.4 121 62.0 125 5.72 117 35.4 29 71.2 15 4.32 107 45.5 129 66.0 128 3.87 123
SPSA-learn [13]103.1 12.3 90 33.7 118 3.37 105 19.2 91 23.6 72 5.45 104 30.0 120 39.7 122 7.00 120 26.9 113 41.3 110 8.41 103 46.7 112 60.1 108 10.2 47 29.4 96 50.6 81 5.20 69 53.7 129 99.9 123 8.43 131 51.4 130 72.0 130 3.51 51
HCIC-L [99]104.8 21.0 130 41.8 126 5.07 130 20.2 111 26.1 125 5.80 108 16.3 100 42.3 125 4.00 97 31.7 123 51.0 126 8.50 105 44.7 64 55.5 46 10.4 61 35.2 128 69.8 129 5.07 45 39.9 97 91.2 110 4.16 89 40.4 124 58.0 116 3.65 101
SegOF [10]105.0 12.3 90 33.1 116 3.11 73 17.9 73 23.8 76 4.51 76 29.0 119 34.3 115 6.16 118 32.8 125 78.9 131 8.33 99 48.1 124 63.6 126 11.2 100 28.5 73 54.3 112 5.72 117 44.6 120 99.9 123 4.97 124 37.9 117 61.4 122 3.51 51
FFV1MT [106]106.3 17.0 125 37.6 123 3.37 105 19.3 93 22.9 56 6.40 119 28.2 118 46.7 129 6.95 119 29.3 119 38.4 96 11.4 123 46.3 103 58.2 80 10.4 61 29.0 83 50.4 76 5.72 117 46.7 124 88.5 97 4.93 123 39.0 120 56.9 114 4.43 129
PGAM+LK [55]106.8 15.5 124 39.4 124 4.55 127 19.8 104 24.0 81 7.68 126 33.1 126 43.4 128 8.00 128 34.5 128 45.7 121 11.2 122 46.6 110 57.7 67 10.6 73 29.3 91 50.8 90 5.74 121 37.4 65 77.2 57 4.43 113 34.4 109 53.3 100 4.24 128
Heeger++ [104]107.2 19.8 128 44.7 127 3.11 73 18.9 87 22.8 53 6.45 120 33.0 125 35.2 116 7.16 124 29.3 119 38.4 96 11.4 123 51.5 127 65.2 127 11.3 116 28.4 66 46.9 31 6.78 131 47.9 126 84.5 91 4.69 120 40.1 123 58.8 119 3.70 104
SLK [47]108.8 13.9 119 29.9 105 3.79 120 20.0 107 22.8 53 6.22 115 32.0 123 33.7 113 7.72 126 33.4 126 46.4 122 16.1 126 48.5 126 61.7 123 10.3 52 28.4 66 47.9 46 5.72 117 43.2 116 97.9 120 4.97 124 38.7 119 59.8 120 4.04 126
Pyramid LK [2]116.7 14.4 121 37.3 122 4.93 128 23.7 129 25.3 111 9.98 131 42.2 130 35.7 117 12.3 130 56.2 131 64.2 129 35.8 131 65.6 130 83.9 130 10.6 73 28.1 57 46.1 25 5.48 110 45.2 121 99.9 123 5.89 129 53.6 131 75.1 131 5.42 130
GroupFlow [9]117.3 19.9 129 49.6 130 3.42 111 19.1 89 24.1 84 5.48 105 31.4 122 40.0 123 8.19 129 36.2 129 61.9 128 12.1 125 55.6 128 71.3 128 11.4 123 36.3 129 67.0 128 5.60 116 46.7 124 98.5 121 4.20 95 43.6 127 62.8 125 3.56 67
Periodicity [78]129.5 17.6 126 55.7 131 5.45 131 26.8 131 27.0 130 9.75 130 49.4 131 51.5 131 17.7 131 51.3 130 70.3 130 27.9 130 66.6 131 86.3 131 11.7 131 38.7 130 82.5 131 6.38 129 51.8 128 99.9 123 5.48 127 44.3 128 65.5 127 5.80 131
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.