Optical flow evaluation results Statistics:     Average   SD   R2.5   R5.0   R10.0   A50   A75   A95  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
R2.5
angle
error
avg. Army
(Hidden texture)
GT   im0   im1
Mequon
(Hidden texture)
GT   im0   im1
Schefflera
(Hidden texture)
GT   im0   im1
Wooden
(Hidden texture)
GT   im0   im1
Grove
(Synthetic)
GT   im0   im1
Urban
(Synthetic)
GT   im0   im1
Yosemite
(Synthetic)
GT   im0   im1
Teddy
(Stereo)
GT   im0   im1
rank all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext
NNF-Local [87]13.6 20.0 3 43.3 3 13.0 3 15.9 10 45.3 25 16.1 14 12.5 4 34.1 9 12.8 9 9.68 19 32.0 11 7.01 23 23.3 4 30.0 4 16.2 4 22.0 32 45.4 9 23.8 61 25.8 19 44.8 6 17.6 17 4.37 9 14.4 27 0.48 1
NN-field [71]14.2 22.1 7 44.0 5 14.6 4 18.4 23 47.3 37 19.2 27 12.5 4 32.9 6 14.0 14 6.57 2 28.2 4 3.57 2 23.4 5 30.1 5 15.9 3 17.9 15 36.8 1 15.8 4 35.9 50 51.5 32 27.4 48 4.58 12 15.3 30 0.55 2
TC/T-Flow [76]19.1 19.9 2 46.9 10 10.2 1 16.0 11 47.8 39 13.9 5 13.0 8 36.6 20 11.1 4 8.90 12 35.0 34 6.10 12 27.2 17 35.7 22 21.0 14 15.7 4 47.2 17 15.8 4 21.0 7 39.2 2 42.1 75 7.86 41 19.5 44 10.9 54
ALD-Flow [66]19.5 21.6 6 46.0 7 15.9 6 15.5 8 41.3 9 15.6 10 13.1 9 35.2 12 12.2 5 8.17 10 33.4 20 5.35 9 28.1 24 37.3 33 20.3 10 16.4 6 47.4 19 16.0 6 26.5 22 45.4 8 41.8 73 8.39 45 22.3 54 11.3 56
ComponentFusion [96]19.7 20.0 3 46.3 8 14.8 5 16.6 14 40.3 6 18.5 24 11.7 3 33.6 7 10.9 3 7.09 5 35.2 36 4.45 5 27.6 20 36.0 25 21.4 17 21.5 28 54.1 51 20.2 30 31.8 43 56.8 59 16.2 10 5.46 22 12.8 20 7.47 28
nLayers [57]20.8 22.7 9 40.3 2 18.4 9 27.2 88 45.9 29 30.4 88 15.7 22 35.4 14 21.4 60 8.12 9 26.6 2 6.21 13 22.5 2 29.0 3 15.5 2 19.9 20 40.8 4 17.8 9 31.3 39 52.6 41 16.9 15 4.26 6 11.2 7 5.84 6
OFLAF [77]21.3 29.6 34 47.4 12 24.8 18 17.8 19 40.9 7 18.3 22 11.6 2 26.9 2 13.2 11 11.5 39 29.3 8 8.97 58 23.7 6 30.9 6 16.5 5 22.2 36 41.5 5 19.3 20 30.2 34 50.6 27 32.9 54 5.94 27 13.8 22 8.44 37
RNLOD-Flow [121]21.6 20.7 5 43.8 4 19.4 10 18.1 20 45.9 29 17.0 19 12.7 6 34.1 9 12.3 6 7.38 6 28.7 6 4.87 6 26.5 13 34.9 18 20.9 12 20.3 21 46.2 12 20.2 30 43.1 74 60.3 69 47.5 92 4.98 19 12.7 19 6.55 14
WLIF-Flow [93]21.7 27.0 19 47.0 11 23.1 16 21.7 43 46.6 35 23.3 42 14.8 13 36.2 16 16.3 23 9.33 16 32.2 13 6.64 17 27.7 21 35.1 20 23.4 30 21.6 29 47.7 21 19.2 17 28.7 30 45.3 7 32.0 53 4.50 11 11.2 7 6.43 11
HAST [109]22.0 19.5 1 40.1 1 11.6 2 16.1 12 39.7 4 14.8 8 8.49 1 21.2 1 7.09 1 6.79 3 29.0 7 3.66 3 21.6 1 28.3 2 13.8 1 24.3 55 48.5 25 24.3 63 41.7 71 59.8 68 63.0 116 6.05 29 11.5 13 8.72 40
MDP-Flow2 [68]23.8 35.3 47 55.4 41 30.2 43 14.2 3 39.7 4 14.2 7 13.6 10 31.4 3 12.9 10 12.2 44 34.1 28 8.19 45 27.7 21 35.0 19 22.1 20 22.4 40 45.4 9 21.5 43 27.1 23 54.1 46 16.5 11 5.87 26 14.0 24 4.25 3
OAR-Flow [125]24.7 25.6 12 54.9 38 22.4 14 18.7 24 44.8 22 19.1 25 17.2 35 43.7 44 18.0 29 8.53 11 31.6 10 5.65 10 29.7 34 39.3 38 20.9 12 14.5 2 47.3 18 13.4 2 14.1 2 38.0 1 20.7 30 9.84 59 21.1 50 16.1 70
Layers++ [37]25.1 28.0 31 48.8 15 30.9 46 23.3 58 45.6 28 25.5 66 13.7 11 31.4 3 18.1 30 8.08 8 24.9 1 5.87 11 22.9 3 28.1 1 19.6 8 22.2 36 46.3 14 20.7 34 39.6 66 55.9 55 35.0 56 4.16 3 9.78 2 6.81 16
TC-Flow [46]25.2 24.4 11 52.2 31 22.6 15 11.8 2 38.8 3 11.2 1 12.7 6 35.7 15 9.84 2 9.88 21 34.9 32 7.49 31 28.9 31 38.6 37 21.2 16 20.6 23 52.1 36 21.6 44 22.6 14 47.1 12 36.3 59 9.05 51 21.5 52 12.4 61
LME [70]27.4 31.5 36 51.4 26 21.0 13 14.7 5 36.9 2 15.7 11 16.0 27 35.2 12 19.8 50 11.8 41 36.6 40 8.08 40 28.6 28 36.0 25 25.5 41 21.1 26 49.0 27 19.8 27 29.8 33 50.0 24 21.5 32 6.61 31 15.1 28 7.95 33
AGIF+OF [85]27.5 26.3 13 48.8 15 24.1 17 24.9 68 52.0 67 26.2 68 16.0 27 39.4 30 19.7 47 8.96 13 32.0 11 6.64 17 26.7 15 33.7 12 21.5 19 21.4 27 49.4 28 18.5 13 28.2 27 49.6 21 30.5 52 4.63 15 11.3 12 7.30 25
PH-Flow [101]28.4 26.7 15 51.6 27 25.6 25 21.7 43 49.4 49 23.8 47 15.5 17 37.6 23 19.5 43 10.2 26 33.8 24 7.44 30 26.4 11 33.7 12 21.0 14 22.0 32 50.3 30 20.4 32 38.8 60 48.4 17 45.4 82 4.26 6 11.2 7 6.38 10
FC-2Layers-FF [74]28.5 26.9 18 48.6 14 28.3 37 22.1 47 48.8 44 23.8 47 14.1 12 32.4 5 19.6 45 9.10 14 28.3 5 6.47 16 25.5 7 31.7 7 23.0 27 23.3 45 47.7 21 21.8 45 44.1 77 56.3 58 46.0 87 3.54 1 9.08 1 5.44 5
Classic+CPF [83]29.5 27.1 23 51.2 25 25.5 24 23.9 62 51.4 63 25.1 61 15.9 24 39.3 29 19.4 41 9.17 15 32.8 16 6.69 19 27.3 18 34.5 16 23.9 31 21.0 25 48.7 26 18.0 11 35.6 49 49.9 23 45.7 84 4.25 5 10.7 4 6.61 15
NNF-EAC [103]29.8 34.9 42 54.8 36 29.9 42 15.6 9 41.7 11 15.9 12 15.1 16 34.8 11 15.7 17 12.4 48 35.1 35 8.33 48 28.1 24 35.7 22 22.9 25 24.5 58 46.2 12 22.7 55 31.6 42 49.1 19 20.1 28 7.54 39 17.1 38 7.44 27
COFM [59]30.0 22.1 7 49.1 17 16.6 7 18.2 21 43.5 16 19.2 27 15.9 24 38.3 26 21.5 61 7.02 4 32.6 14 4.40 4 31.4 42 37.9 34 35.0 78 22.1 34 46.4 15 18.3 12 28.7 30 45.9 10 45.5 83 9.25 52 15.5 32 15.7 69
IROF++ [58]30.4 27.4 25 50.7 21 26.7 31 22.1 47 50.1 58 24.1 51 16.3 30 40.0 34 19.6 45 10.6 33 34.3 29 7.72 36 27.9 23 35.7 22 22.5 21 22.3 38 54.1 51 19.9 28 25.5 18 49.6 21 11.5 6 5.49 23 14.1 25 6.54 13
Sparse-NonSparse [56]30.5 26.8 17 51.9 30 26.8 32 22.2 52 49.0 47 24.6 57 15.6 19 39.4 30 19.0 36 9.46 18 33.6 22 7.07 25 29.0 32 37.2 32 24.3 36 21.7 31 50.4 31 19.5 25 34.0 45 45.8 9 41.6 71 4.44 10 10.9 5 6.96 20
FESL [72]31.4 27.0 19 46.3 8 31.2 48 26.0 79 51.8 66 26.9 72 15.8 23 37.0 21 19.5 43 7.89 7 30.7 9 5.17 8 26.6 14 33.8 14 22.6 22 20.3 21 45.8 11 19.3 20 39.9 67 61.2 74 35.6 57 5.04 20 12.5 18 6.48 12
Efficient-NL [60]31.9 23.3 10 44.7 6 17.6 8 24.6 67 51.6 65 25.4 65 15.0 15 36.2 16 17.5 27 9.92 22 33.1 18 6.94 21 26.4 11 34.0 15 20.3 10 27.2 67 49.4 28 22.6 50 37.6 55 50.5 26 37.1 60 6.98 36 16.2 34 8.16 34
LSM [39]33.5 26.5 14 50.8 22 27.0 33 22.1 47 49.4 49 24.4 55 15.6 19 38.2 25 19.4 41 10.1 23 33.0 17 7.43 29 28.6 28 36.3 27 24.7 37 22.8 43 50.5 32 20.9 38 40.3 69 49.3 20 45.8 86 4.76 17 12.0 15 6.93 19
PMMST [114]34.0 42.4 71 58.9 45 40.2 67 22.1 47 45.3 25 24.7 60 17.9 41 38.4 27 18.8 33 12.2 44 28.1 3 8.26 46 25.7 8 32.7 10 18.5 7 22.3 38 45.3 8 20.8 36 28.5 29 50.7 28 18.3 19 8.51 48 16.8 35 8.73 41
Classic+NL [31]34.9 27.2 24 47.8 13 28.4 38 22.0 46 49.4 49 24.1 51 15.5 17 37.4 22 19.8 50 10.4 30 33.8 24 7.40 28 28.5 26 36.4 28 24.2 35 23.3 45 52.2 37 21.1 39 43.9 75 52.5 38 44.6 79 4.60 14 11.2 7 7.04 22
Ramp [62]35.0 27.0 19 52.3 32 26.1 29 22.3 54 49.1 48 24.6 57 15.6 19 37.8 24 19.8 50 10.5 32 33.6 22 7.61 33 28.6 28 36.6 29 24.0 32 23.6 48 51.2 33 21.8 45 38.2 56 44.5 4 46.1 88 4.86 18 12.1 16 7.13 23
2DHMM-SAS [92]37.8 26.7 15 51.8 29 25.6 25 22.2 52 51.3 61 23.8 47 17.8 39 42.3 39 20.0 55 10.4 30 34.6 31 7.52 32 28.5 26 36.6 29 24.0 32 22.7 42 54.5 54 20.6 33 39.0 62 47.8 15 45.1 80 5.51 24 13.9 23 7.73 31
FMOF [94]38.1 27.8 30 50.4 20 27.1 35 26.2 81 52.5 71 27.4 75 15.9 24 36.4 19 21.7 62 9.71 20 32.7 15 6.98 22 27.3 18 34.6 17 24.1 34 23.7 50 47.6 20 19.7 26 38.4 57 55.4 52 48.9 95 5.80 25 14.1 25 7.00 21
SVFilterOh [111]39.2 39.6 61 54.6 35 39.8 66 23.8 61 44.7 21 24.0 50 17.3 36 33.8 8 18.8 33 10.2 26 36.4 39 4.93 7 25.9 10 32.1 8 19.6 8 24.7 59 46.7 16 22.9 58 52.0 99 76.3 117 59.2 110 4.23 4 10.9 5 5.01 4
S2D-Matching [84]40.1 27.7 27 50.2 19 28.4 38 22.1 47 48.8 44 24.3 53 16.5 32 40.0 34 19.3 39 10.6 33 33.8 24 7.78 37 29.1 33 36.7 31 25.0 38 24.4 57 53.0 47 22.6 50 47.0 88 53.6 43 50.8 100 4.58 12 11.2 7 7.54 29
ProbFlowFields [128]40.6 33.9 38 68.5 68 30.9 46 20.3 33 45.3 25 22.1 37 19.2 49 44.8 47 22.9 66 11.7 40 39.3 48 8.91 57 31.0 40 40.1 42 23.3 28 16.8 7 47.8 23 19.2 17 23.7 17 55.9 55 23.9 41 8.39 45 22.4 55 9.82 50
SimpleFlow [49]41.0 28.6 33 51.6 27 29.5 41 25.0 69 51.3 61 28.2 80 18.6 46 43.0 42 23.3 67 10.1 23 33.5 21 7.04 24 29.9 35 37.9 34 26.2 43 27.8 72 52.2 37 24.0 62 35.1 48 47.9 16 29.7 51 4.66 16 12.4 17 6.92 18
PMF [73]42.0 37.7 56 58.3 43 27.3 36 19.4 26 45.0 24 18.3 22 16.2 29 39.9 33 14.0 14 13.6 61 35.7 37 8.03 39 25.8 9 32.5 9 17.1 6 30.2 80 57.0 64 31.6 82 58.9 107 74.7 110 55.9 107 3.95 2 10.3 3 6.15 9
Adaptive [20]42.8 27.0 19 52.6 33 19.8 11 21.9 45 47.8 39 22.6 39 20.5 54 47.8 56 19.7 47 10.3 28 39.9 52 6.31 15 45.6 112 52.6 110 51.3 111 17.4 8 48.2 24 13.9 3 34.8 47 56.9 61 19.8 26 6.04 28 15.2 29 7.54 29
TV-L1-MCT [64]43.4 27.5 26 49.4 18 27.0 33 26.5 82 52.8 76 27.8 79 16.8 33 39.1 28 21.8 63 10.6 33 33.8 24 7.82 38 30.3 37 38.1 36 28.7 58 24.7 59 53.4 49 23.4 59 27.4 24 52.5 38 19.4 23 7.28 37 15.3 30 11.4 58
Correlation Flow [75]43.4 36.6 53 55.3 40 34.4 56 16.6 14 44.4 20 14.8 8 18.3 45 42.9 41 12.7 8 12.4 48 39.7 50 8.46 52 32.2 45 40.6 45 25.2 39 29.0 75 54.2 53 29.7 79 39.1 63 52.1 36 47.4 91 6.53 30 16.1 33 6.88 17
IROF-TV [53]44.1 30.9 35 54.8 36 31.6 50 22.8 57 50.5 59 25.1 61 16.9 34 40.8 36 20.8 57 14.0 63 43.7 66 10.1 63 31.2 41 39.5 39 28.6 56 26.8 66 58.7 73 25.6 66 18.8 4 48.4 17 8.08 5 5.28 21 13.6 21 7.77 32
AggregFlow [97]44.3 36.3 52 52.7 34 35.7 58 26.6 83 52.6 74 26.7 70 23.3 67 48.2 58 28.9 84 12.1 42 34.9 32 8.63 54 30.2 36 40.3 44 21.4 17 15.9 5 38.3 2 16.8 7 26.1 21 47.3 13 16.8 13 12.6 71 20.3 46 20.0 80
Occlusion-TV-L1 [63]45.0 34.3 41 58.5 44 25.1 20 20.0 29 46.5 34 20.8 33 22.3 64 49.9 59 20.6 56 12.6 54 41.7 58 8.41 51 35.2 65 44.9 74 32.3 69 17.7 11 52.6 42 21.1 39 28.2 27 52.5 38 13.0 7 9.66 55 23.7 59 10.6 52
Aniso-Texture [82]48.2 28.5 32 50.9 23 32.7 53 21.2 37 41.7 11 25.3 64 17.6 38 41.2 37 21.1 59 5.72 1 33.2 19 2.54 1 35.1 64 43.7 61 31.3 67 23.6 48 53.6 50 22.6 50 62.2 114 75.8 115 53.5 104 6.89 35 17.4 40 8.20 35
Classic++ [32]48.3 27.7 27 51.0 24 28.7 40 21.5 42 45.9 29 24.3 53 18.1 44 44.3 45 19.9 53 10.3 28 37.7 42 7.14 26 33.4 49 44.1 64 27.9 52 24.0 53 57.9 69 21.4 42 46.3 84 55.6 53 49.7 97 8.45 47 20.7 47 9.69 49
IIOF-NLDP [131]49.2 33.3 37 59.3 46 24.9 19 24.4 66 53.8 83 22.9 41 18.8 47 46.2 51 15.0 16 13.5 58 39.7 50 9.86 62 32.3 46 41.2 49 23.3 28 29.9 79 60.2 82 29.6 78 28.1 26 60.8 71 27.0 46 7.51 38 17.2 39 7.29 24
MDP-Flow [26]49.5 35.5 48 65.0 55 32.4 51 20.6 35 43.8 18 24.4 55 18.0 43 43.4 43 19.9 53 14.9 71 41.8 60 11.5 72 30.8 39 39.6 40 25.3 40 23.8 51 57.4 67 22.2 47 31.0 37 59.3 66 16.8 13 10.8 66 26.5 64 10.9 54
DeepFlow2 [108]49.9 39.1 59 66.4 59 44.4 75 20.1 31 47.9 41 20.9 34 23.8 70 52.8 66 26.3 76 12.2 44 43.2 65 7.64 34 31.4 42 41.7 53 22.9 25 18.2 17 52.4 39 17.9 10 29.6 32 44.7 5 39.0 64 16.2 85 31.2 85 22.7 86
OFH [38]50.1 41.9 70 61.6 50 48.5 81 14.7 5 42.7 14 14.1 6 17.4 37 47.6 54 12.6 7 10.6 33 38.5 44 8.39 50 34.8 62 43.7 61 34.5 76 27.2 67 61.9 87 29.5 77 21.3 10 57.6 62 21.4 31 12.2 70 29.8 77 16.2 71
BriefMatch [124]51.5 34.1 40 59.4 47 30.2 43 17.1 16 43.6 17 16.1 14 14.8 13 36.3 18 13.4 12 9.41 17 34.3 29 6.26 14 33.4 49 41.2 49 31.8 68 40.3 107 63.5 89 42.7 110 47.2 89 61.1 72 59.1 109 12.7 73 23.4 56 21.6 84
CPM-Flow [116]52.2 35.2 43 67.0 61 25.3 21 25.8 76 53.6 80 28.2 80 25.5 73 58.5 86 27.5 77 12.4 48 48.0 78 8.11 43 33.9 54 44.2 65 27.1 46 17.6 9 51.8 34 19.1 15 21.3 10 51.7 33 22.6 37 9.92 60 27.2 69 11.3 56
PGM-C [120]53.0 35.2 43 67.1 63 25.4 23 25.8 76 53.6 80 28.2 80 25.8 76 59.3 89 27.5 77 12.4 48 48.1 79 8.16 44 33.9 54 44.3 68 27.1 46 17.7 11 52.8 46 19.1 15 20.8 6 50.3 25 22.4 35 9.82 58 27.2 69 11.5 60
S2F-IF [123]53.0 35.7 49 69.0 71 26.6 30 24.1 64 54.0 88 26.0 67 25.6 75 60.9 91 25.9 72 12.4 48 46.6 75 8.38 49 34.3 58 44.2 65 27.7 51 18.0 16 53.2 48 19.2 17 21.2 9 51.7 33 22.9 38 8.97 50 24.9 62 9.11 45
CostFilter [40]54.5 43.6 77 65.7 57 40.8 68 20.8 36 45.9 29 21.0 35 18.8 47 44.9 48 18.3 32 17.3 80 39.5 49 13.9 82 26.7 15 32.8 11 22.7 24 30.9 83 60.0 80 31.6 82 59.7 108 81.5 124 59.3 111 4.31 8 11.9 14 5.93 7
RFlow [90]54.8 44.4 79 75.2 92 50.5 85 16.5 13 42.1 13 17.2 20 21.4 58 52.2 62 16.0 18 11.3 38 36.2 38 7.25 27 35.2 65 44.3 68 32.3 69 24.3 55 55.6 59 22.6 50 38.6 58 55.3 50 41.6 71 13.4 75 29.9 78 16.9 75
EpicFlow [102]54.8 35.2 43 67.2 64 25.3 21 25.8 76 53.8 83 28.2 80 26.1 78 60.1 90 27.5 77 12.4 48 48.1 79 8.10 42 34.2 57 44.5 70 28.0 53 17.8 13 52.7 44 19.4 22 21.0 7 52.1 36 22.5 36 10.4 63 27.3 71 12.7 63
FlowFields+ [130]55.2 35.8 51 69.2 73 26.0 28 25.6 74 55.3 94 27.7 77 26.7 79 62.9 95 27.7 81 12.7 55 45.9 73 8.84 56 34.0 56 44.2 65 26.6 44 17.6 9 54.9 56 19.0 14 20.6 5 53.9 45 22.3 34 9.31 54 26.5 64 8.79 42
TV-L1-improved [17]56.4 27.7 27 57.9 42 20.5 12 18.2 21 44.9 23 19.1 25 19.4 50 47.7 55 17.0 26 10.1 23 38.5 44 6.75 20 35.9 72 46.0 80 27.3 48 43.7 113 70.2 110 47.5 114 51.4 96 60.5 70 50.3 99 10.3 62 26.8 68 10.8 53
FlowFields [110]57.0 35.7 49 68.7 69 25.8 27 25.6 74 55.1 93 27.7 77 26.8 82 62.8 94 27.6 80 13.0 56 46.8 76 9.13 59 34.6 61 44.9 74 28.1 54 17.8 13 54.9 56 19.4 22 21.3 10 54.8 48 24.2 42 9.25 52 26.4 63 8.47 38
Steered-L1 [118]58.9 38.7 58 67.9 65 43.1 73 11.6 1 34.7 1 12.3 2 16.3 30 41.3 38 13.9 13 12.1 42 38.6 46 8.30 47 34.5 60 43.4 58 32.5 71 29.4 77 61.1 86 25.9 67 60.6 111 67.0 92 70.1 122 15.9 84 30.6 81 24.0 88
Sparse Occlusion [54]59.6 38.6 57 61.8 51 32.5 52 26.0 79 48.8 44 29.5 87 20.0 52 45.2 49 19.2 38 14.3 67 38.4 43 9.67 61 34.4 59 42.6 55 26.7 45 25.4 62 52.4 39 22.4 49 67.3 119 75.9 116 48.3 93 8.07 43 19.9 45 7.36 26
DeepFlow [86]60.8 47.3 85 71.9 82 64.0 99 21.4 40 48.2 42 22.7 40 27.9 86 58.2 83 31.6 88 15.1 72 42.7 61 10.6 68 31.5 44 42.1 54 22.6 22 19.6 19 56.7 63 19.4 22 27.6 25 46.2 11 39.7 65 20.6 93 35.3 100 28.0 96
MLDP_OF [89]61.0 48.8 88 77.3 95 52.2 87 20.1 31 49.6 54 19.3 29 23.5 69 54.6 70 18.9 35 12.3 47 38.6 46 7.65 35 33.5 52 40.9 46 29.1 62 28.4 73 55.9 60 31.6 82 49.1 93 62.2 78 60.5 113 7.91 42 16.8 35 8.95 43
TF+OM [100]62.2 39.8 62 55.2 39 30.4 45 20.4 34 41.0 8 23.5 44 19.5 51 39.4 30 28.0 82 18.4 83 37.0 41 18.0 88 35.0 63 41.1 47 39.6 90 29.6 78 52.6 42 29.1 76 49.0 92 66.8 91 43.6 78 14.4 77 29.3 74 18.0 77
CombBMOF [113]64.1 42.4 71 71.5 80 31.3 49 25.2 70 52.9 77 25.1 61 17.9 41 45.8 50 16.1 19 14.2 65 41.7 58 11.4 71 33.5 52 40.2 43 29.6 63 34.5 90 59.9 79 37.3 96 55.2 105 69.9 101 46.5 90 6.78 33 16.8 35 8.62 39
EPPM w/o HM [88]64.7 43.1 75 72.4 84 38.1 64 18.7 24 52.5 71 16.9 18 21.3 57 56.2 75 17.5 27 16.2 76 45.3 72 12.7 79 33.4 49 39.6 40 30.0 64 33.5 86 65.4 95 33.8 88 45.5 79 66.1 89 65.5 119 6.88 34 18.1 41 9.16 47
Complementary OF [21]66.0 51.9 95 74.9 91 59.3 95 14.2 3 41.6 10 13.7 3 20.4 53 46.6 52 19.7 47 22.2 90 40.8 54 21.0 93 36.0 74 43.4 58 38.5 88 33.9 89 63.8 90 31.6 82 31.1 38 51.9 35 36.2 58 18.9 90 34.4 98 29.4 98
Aniso. Huber-L1 [22]67.0 33.9 38 65.1 56 32.8 54 34.0 94 54.0 88 40.0 94 27.9 86 55.0 72 38.4 93 15.2 73 49.9 83 12.0 74 35.3 67 44.6 71 28.5 55 23.9 52 55.9 60 20.7 34 50.6 94 62.1 77 39.7 65 8.15 44 20.7 47 8.39 36
ComplOF-FED-GPU [35]69.0 49.5 90 75.7 94 55.3 90 15.3 7 47.0 36 13.8 4 21.1 56 52.7 65 16.1 19 17.1 79 40.6 53 14.2 83 35.7 71 45.1 77 32.5 71 35.3 93 67.5 103 34.4 90 46.5 86 59.0 65 50.8 100 12.8 74 29.6 75 16.6 74
ACK-Prior [27]69.0 55.9 97 72.7 86 59.3 95 17.5 18 43.3 15 16.0 13 17.8 39 42.3 39 16.3 23 17.6 81 41.3 56 12.0 74 35.3 67 41.1 47 35.9 79 37.4 103 59.6 77 34.3 89 59.8 110 61.1 72 74.7 123 17.7 89 29.2 73 27.4 91
TCOF [69]69.1 45.0 80 70.0 74 51.5 86 25.5 73 53.7 82 26.7 70 26.7 79 56.2 75 32.0 89 21.9 89 43.1 64 22.2 95 37.8 84 48.9 96 25.7 42 18.8 18 44.7 7 20.0 29 52.1 100 67.5 94 25.8 43 10.7 65 26.7 66 11.4 58
Rannacher [23]69.1 43.1 75 71.0 78 45.2 77 24.1 64 49.4 49 26.4 69 26.0 77 56.9 79 26.0 74 14.2 65 42.7 61 10.5 67 37.1 81 47.9 91 30.7 66 32.3 84 65.2 94 27.0 71 44.0 76 56.0 57 39.7 65 7.83 40 21.2 51 9.19 48
F-TV-L1 [15]71.2 66.8 105 84.2 104 77.3 107 27.1 86 52.0 67 29.1 86 27.2 83 57.3 81 24.2 70 24.1 94 52.0 87 19.5 91 39.3 91 47.7 90 39.3 89 24.2 54 56.5 62 24.9 64 33.2 44 53.7 44 20.1 28 6.69 32 18.6 42 5.94 8
LDOF [28]71.5 41.7 69 70.7 75 47.2 80 24.0 63 53.9 87 24.6 57 26.7 79 58.4 85 25.5 71 15.8 74 57.4 97 10.2 65 36.0 74 45.3 78 34.8 77 22.1 34 58.1 70 21.3 41 30.6 35 56.8 59 23.4 39 22.5 102 38.6 107 30.2 99
ROF-ND [107]71.5 49.2 89 71.5 80 49.4 83 22.5 56 46.2 33 20.6 31 21.4 58 50.0 60 18.1 30 23.1 92 53.7 91 16.0 85 35.9 72 46.1 82 28.6 56 33.5 86 58.5 71 30.3 80 60.7 112 70.5 102 60.4 112 9.67 56 20.9 49 10.2 51
SIOF [67]72.5 50.3 93 66.0 58 47.1 79 19.8 28 48.4 43 20.7 32 29.8 93 55.1 73 32.6 91 25.9 96 48.3 81 25.0 97 37.7 82 46.4 84 36.8 82 32.9 85 58.6 72 35.6 94 37.2 54 53.1 42 18.6 20 16.8 87 33.0 90 21.3 83
LocallyOriented [52]73.2 39.4 60 60.5 48 35.7 58 27.9 89 57.8 100 28.5 84 28.1 89 58.3 84 30.6 87 13.9 62 41.3 56 10.1 63 37.8 84 47.3 88 33.0 75 24.8 61 52.5 41 28.1 73 39.4 65 62.4 79 37.5 61 15.7 82 33.0 90 18.2 79
Second-order prior [8]73.6 37.6 55 70.9 77 37.2 62 22.4 55 51.2 60 23.7 46 24.5 72 59.1 88 22.6 64 11.2 37 41.2 55 8.48 53 37.9 86 49.6 103 28.8 59 29.0 75 68.8 107 26.0 68 55.5 106 64.7 84 52.7 103 14.7 79 34.1 96 17.3 76
CRTflow [80]73.8 40.9 64 72.5 85 36.2 60 21.3 39 49.4 49 21.8 36 22.9 65 57.5 82 19.0 36 14.0 63 44.7 69 10.3 66 35.4 70 45.0 76 30.4 65 46.6 117 73.4 112 53.8 118 38.8 60 65.5 86 38.2 62 19.5 92 38.5 106 27.6 94
Brox et al. [5]73.9 43.7 78 74.4 89 56.4 92 27.0 85 52.5 71 30.5 89 23.4 68 54.5 69 23.3 67 13.4 57 50.0 84 8.66 55 39.8 94 46.5 85 47.6 106 21.6 29 59.8 78 22.8 57 30.9 36 59.3 66 7.61 3 23.1 105 37.0 104 33.4 106
FlowNetS+ft+v [112]74.0 36.8 54 67.0 61 39.4 65 25.3 72 52.1 69 27.6 76 27.8 85 57.2 80 35.5 92 13.5 58 50.9 85 9.44 60 40.1 95 49.5 102 36.8 82 20.9 24 57.0 64 20.8 36 46.3 84 66.3 90 41.0 69 17.4 88 34.3 97 24.1 89
NL-TV-NCC [25]74.1 46.1 82 68.3 67 43.4 74 25.2 70 55.3 94 23.5 44 21.8 63 47.2 53 16.9 25 16.9 78 44.1 67 11.9 73 38.5 88 49.1 100 27.3 48 36.5 99 65.7 96 34.9 92 46.2 81 75.7 114 45.7 84 12.6 71 28.8 72 9.10 44
DF-Auto [115]74.2 41.6 68 64.2 54 32.8 54 42.0 99 58.8 103 49.6 100 34.8 99 62.2 93 47.2 100 20.7 87 53.4 90 14.5 84 36.2 77 44.7 72 37.2 85 15.1 3 42.9 6 17.4 8 45.3 78 69.0 98 13.0 7 24.2 108 37.7 105 31.9 102
TriangleFlow [30]74.9 41.5 67 63.2 52 42.6 72 21.4 40 52.4 70 20.2 30 21.7 62 53.6 68 16.2 21 14.8 69 44.4 68 10.9 69 43.2 105 52.9 112 43.4 97 36.8 102 65.9 97 38.8 99 42.2 72 65.4 85 41.8 73 15.8 83 35.2 99 22.5 85
SRR-TVOF-NL [91]74.9 47.6 86 69.1 72 41.9 71 23.3 58 52.7 75 23.3 42 25.5 73 56.8 78 25.9 72 13.5 58 47.9 77 8.09 41 36.9 80 43.8 63 32.9 74 25.8 63 57.7 68 22.6 50 62.8 116 75.2 112 49.1 96 21.0 95 29.7 76 31.6 101
DPOF [18]75.9 45.3 81 68.9 70 37.6 63 26.7 84 56.9 97 26.9 72 24.3 71 54.6 70 26.2 75 18.6 84 54.6 94 13.6 81 33.3 48 43.1 56 28.9 60 27.6 71 60.8 83 26.3 69 47.2 89 55.3 50 76.0 125 14.3 76 31.0 84 15.3 68
Bartels [41]76.8 48.6 87 63.2 52 61.4 98 23.4 60 44.0 19 27.1 74 21.4 58 44.6 46 23.8 69 26.2 97 43.0 63 25.4 98 36.8 78 44.7 72 41.7 94 33.7 88 60.0 80 41.7 107 52.6 101 67.2 93 61.1 114 11.2 67 23.4 56 16.3 72
Dynamic MRF [7]77.0 49.5 90 78.0 96 55.8 91 17.2 17 47.4 38 16.5 17 21.6 61 56.3 77 16.2 21 14.8 69 46.4 74 12.5 77 41.2 99 49.0 98 45.1 101 35.3 93 70.7 111 38.5 98 37.1 53 57.7 63 55.1 105 21.3 97 36.7 103 31.2 100
SuperFlow [81]77.2 35.2 43 61.1 49 34.4 56 35.2 95 53.5 79 42.5 95 27.7 84 52.5 64 43.5 99 27.5 98 60.5 101 27.6 99 36.0 74 43.3 57 42.2 95 22.6 41 59.2 76 22.2 47 46.2 81 62.0 76 23.4 39 21.8 100 36.0 101 32.5 104
CBF [12]77.9 41.4 65 74.0 88 48.5 81 40.2 98 51.5 64 51.7 102 22.9 65 50.8 61 28.5 83 14.3 67 44.7 69 11.2 70 38.3 87 46.1 82 36.1 80 26.3 65 55.1 58 24.9 64 61.5 113 71.0 103 52.0 102 11.6 69 26.7 66 14.8 67
Local-TV-L1 [65]78.0 56.8 99 79.1 97 74.5 103 39.5 97 53.8 83 46.1 97 38.1 100 66.2 98 43.1 98 23.9 93 52.9 89 21.1 94 32.3 46 41.4 52 27.5 50 23.2 44 54.8 55 22.7 55 25.8 19 47.5 14 33.4 55 26.9 111 40.5 109 40.5 115
CLG-TV [48]78.2 41.4 65 68.0 66 40.8 68 37.0 96 53.1 78 45.3 96 30.9 94 58.7 87 40.2 95 22.8 91 62.0 102 19.3 90 39.0 90 47.6 89 38.2 87 27.2 67 61.0 85 27.1 72 46.2 81 57.8 64 29.3 50 9.74 57 24.6 61 9.11 45
CNN-flow-warp+ref [117]78.4 42.6 73 71.2 79 49.8 84 31.6 93 53.8 83 37.3 92 32.7 96 63.8 96 42.7 97 16.0 75 55.1 95 12.1 76 38.6 89 46.0 80 43.8 99 23.3 45 59.1 75 23.6 60 23.5 16 50.9 30 21.9 33 24.2 108 36.2 102 32.9 105
HBM-GC [105]80.2 73.5 113 79.7 98 79.4 109 30.0 90 49.7 56 33.6 90 29.3 92 47.8 56 30.4 85 35.4 101 45.2 71 33.3 101 30.5 38 35.1 20 32.6 73 34.6 91 52.0 35 35.4 93 70.9 122 80.1 120 62.6 115 8.83 49 19.1 43 13.0 64
TriFlow [95]83.6 47.1 84 66.5 60 41.1 70 31.2 92 49.6 54 37.3 92 27.9 86 52.3 63 39.4 94 24.7 95 49.3 82 22.3 96 37.7 82 43.5 60 43.1 96 28.4 73 52.7 44 29.0 75 76.7 126 73.7 107 99.5 129 16.2 85 30.5 80 20.1 81
p-harmonic [29]85.8 50.4 94 86.6 114 56.5 94 27.1 86 54.5 90 28.9 85 32.9 97 69.3 104 30.4 85 19.8 85 65.1 105 16.2 86 39.6 93 47.2 87 40.3 92 30.4 81 66.5 99 32.2 86 45.6 80 64.4 82 28.9 49 10.2 61 24.1 60 13.2 65
Learning Flow [11]86.2 42.9 74 70.7 75 44.8 76 30.2 91 54.7 91 34.7 91 28.2 90 55.9 74 32.3 90 17.6 81 57.1 96 12.6 78 44.0 108 52.6 110 47.8 108 30.5 82 64.6 92 30.3 80 46.9 87 63.3 80 42.8 77 14.7 79 31.6 88 16.3 72
Fusion [6]87.2 40.5 63 75.6 93 45.9 78 20.0 29 50.0 57 22.5 38 20.8 55 52.8 66 22.8 65 16.2 76 52.8 88 13.5 80 43.1 104 49.0 98 47.5 103 39.6 106 67.8 105 43.8 112 63.9 117 75.0 111 46.3 89 35.5 120 42.6 115 53.3 125
StereoFlow [44]87.4 95.9 129 96.0 128 97.4 129 88.3 129 96.2 129 86.2 125 82.6 126 94.8 128 73.7 123 91.4 128 96.3 128 90.3 128 53.0 119 61.6 123 52.8 112 11.2 1 39.3 3 11.7 1 10.5 1 42.5 3 1.70 1 11.5 68 23.6 58 18.0 77
Shiralkar [42]89.2 46.1 82 85.6 110 54.3 89 19.7 27 57.7 99 18.2 21 28.2 90 70.8 105 19.3 39 20.5 86 59.0 99 18.4 89 39.5 92 49.7 104 36.3 81 40.4 108 76.1 114 41.2 106 51.9 98 65.8 87 64.2 117 21.0 95 42.4 114 25.3 90
SegOF [10]92.5 56.3 98 71.9 82 37.1 61 57.3 113 62.9 110 68.3 115 46.0 106 69.0 103 57.2 112 41.0 103 59.5 100 37.2 102 43.5 106 48.3 94 56.4 116 38.2 104 69.6 109 39.1 100 17.9 3 64.5 83 3.40 2 22.7 104 33.0 90 32.0 103
StereoOF-V1MT [119]93.0 49.7 92 86.0 111 56.4 92 21.2 37 68.8 113 16.3 16 32.2 95 80.7 112 20.8 57 21.3 88 66.1 107 17.4 87 47.0 114 57.0 114 47.5 103 41.7 111 81.2 118 40.9 104 38.6 58 68.1 96 48.6 94 23.2 106 42.2 113 27.7 95
Ad-TV-NDC [36]93.5 73.7 114 85.4 108 89.5 124 56.9 112 60.4 106 67.5 114 51.0 110 75.9 108 57.6 113 45.7 106 65.7 106 47.9 109 35.3 67 45.3 78 28.9 60 27.3 70 57.2 66 28.2 74 34.6 46 55.0 49 27.3 47 34.0 117 48.7 121 47.5 119
Modified CLG [34]95.7 68.7 108 80.5 99 76.1 105 52.0 109 61.0 107 63.6 111 51.9 111 79.4 110 55.6 110 47.4 109 72.1 112 46.7 108 41.2 99 49.7 104 46.0 102 26.0 64 64.7 93 26.7 70 31.4 41 55.6 53 19.9 27 29.0 114 43.8 117 39.9 114
IAOF2 [51]97.2 54.9 96 73.7 87 53.9 88 42.6 100 58.3 101 50.7 101 33.9 98 61.9 92 42.1 96 64.4 118 75.7 115 74.3 120 41.5 101 49.9 107 37.1 84 36.4 98 64.0 91 34.4 90 59.7 108 69.6 100 41.3 70 19.4 91 33.4 94 23.0 87
Filter Flow [19]99.3 62.9 100 74.4 89 60.8 97 42.8 101 60.1 105 49.4 99 42.5 102 66.0 97 51.2 103 52.1 114 69.5 109 50.2 110 44.8 110 49.7 104 54.4 114 41.9 112 66.7 100 43.6 111 74.3 125 88.9 127 42.6 76 10.6 64 21.6 53 12.6 62
GroupFlow [9]99.8 66.4 103 85.2 107 80.8 112 61.6 116 75.4 120 69.0 116 51.9 111 83.6 115 57.0 111 33.5 100 63.9 103 32.5 100 49.6 115 61.0 118 39.9 91 51.3 122 81.7 119 59.4 122 22.8 15 51.1 31 16.5 11 28.0 113 41.9 112 37.9 112
2D-CLG [1]100.8 77.2 117 82.3 102 75.4 104 61.5 115 65.9 112 73.7 119 63.2 120 89.6 122 60.8 118 82.8 126 88.3 123 86.8 126 43.5 106 49.3 101 54.8 115 35.1 92 67.5 103 36.1 95 21.3 10 50.8 29 15.5 9 34.4 119 46.3 120 46.0 117
SPSA-learn [13]100.8 65.1 101 87.4 117 72.7 102 45.7 105 59.3 104 53.4 105 45.2 105 74.7 107 52.2 105 41.6 104 69.9 111 42.5 104 42.7 103 48.9 96 48.7 110 38.8 105 69.0 108 42.5 109 39.1 63 61.9 75 19.3 22 36.0 121 45.6 118 48.3 120
IAOF [50]101.5 66.3 102 81.3 101 77.8 108 50.1 108 58.4 102 59.7 109 45.0 104 74.1 106 49.6 102 50.8 111 68.2 108 58.0 116 40.2 96 48.7 95 37.8 86 36.7 100 66.9 101 33.5 87 54.9 103 63.5 81 40.8 68 30.1 116 41.3 111 43.5 116
BlockOverlap [61]101.5 77.2 117 86.0 111 82.8 114 48.2 106 55.8 96 57.6 108 46.9 108 66.9 99 51.9 104 49.1 110 54.1 92 51.2 111 36.8 78 41.3 51 47.5 103 40.5 109 59.0 74 39.6 102 68.9 121 80.2 121 65.1 118 20.8 94 30.8 82 34.9 108
HBpMotionGpu [43]102.1 67.0 106 80.7 100 72.3 101 55.3 111 57.3 98 66.7 113 44.7 103 67.2 101 54.1 108 39.5 102 57.8 98 38.4 103 42.0 102 48.2 93 48.3 109 35.9 95 60.9 84 39.2 101 65.1 118 72.0 105 50.1 98 22.6 103 32.5 89 36.9 109
Black & Anandan [4]103.7 70.3 110 88.0 118 84.1 115 45.5 104 61.4 108 52.0 103 47.4 109 77.3 109 52.9 106 42.3 105 77.5 116 42.8 105 44.0 108 51.8 108 45.0 100 35.9 95 75.9 113 38.3 97 50.8 95 71.3 104 17.8 18 29.8 115 42.7 116 37.6 111
GraphCuts [14]104.0 66.6 104 87.0 115 80.0 111 43.1 102 63.0 111 46.1 97 41.8 101 67.0 100 53.4 107 28.5 99 64.0 104 20.8 92 40.2 96 48.1 92 43.5 98 46.5 115 63.4 88 40.5 103 62.7 115 75.4 113 69.5 121 23.8 107 33.3 93 38.5 113
FlowNet2 [122]105.6 78.8 119 86.2 113 79.4 109 63.5 120 70.2 115 72.8 118 57.2 117 82.0 113 59.7 117 46.2 107 51.4 86 45.6 107 45.3 111 54.0 113 41.1 93 36.7 100 67.3 102 41.0 105 67.6 120 80.8 122 55.1 105 14.5 78 30.0 79 13.9 66
Nguyen [33]107.8 75.6 116 85.4 108 85.8 117 67.0 121 61.6 109 83.0 123 57.1 116 80.2 111 64.1 120 70.8 119 80.2 118 77.4 122 45.9 113 52.2 109 56.4 116 36.3 97 68.1 106 42.0 108 41.4 70 66.0 88 19.7 25 34.1 118 45.6 118 46.1 118
SILK [79]107.8 72.5 111 85.1 106 88.3 121 61.9 117 71.8 116 73.7 119 54.9 114 85.3 116 58.0 115 53.6 115 69.8 110 54.6 115 52.8 117 57.9 115 61.8 120 46.5 115 77.6 115 48.9 115 31.3 39 54.3 47 38.9 63 37.8 122 50.6 122 49.7 122
2bit-BM-tele [98]107.9 82.4 121 87.2 116 91.8 125 44.4 103 54.7 91 52.9 104 46.0 106 68.3 102 47.4 101 51.5 113 54.4 93 53.4 114 40.5 98 47.1 86 47.7 107 47.9 118 66.4 98 53.1 116 71.7 123 83.6 125 75.1 124 21.9 101 39.3 108 29.2 97
UnFlow [129]109.5 89.6 126 94.1 124 86.4 118 72.2 123 83.9 126 77.9 122 71.4 123 93.2 125 69.8 121 54.8 116 74.4 113 51.4 112 62.0 124 66.9 125 69.1 127 50.0 120 80.9 116 57.1 119 53.2 102 69.0 98 7.68 4 15.6 81 30.8 82 21.0 82
Periodicity [78]110.8 68.9 109 83.5 103 65.5 100 52.2 110 69.8 114 57.0 107 78.4 125 82.5 114 87.2 127 47.2 108 74.7 114 45.5 106 69.7 129 81.9 129 65.6 124 59.5 124 84.9 126 60.7 123 36.3 52 79.8 119 19.2 21 40.7 124 66.5 128 53.1 124
Horn & Schunck [3]111.2 74.1 115 93.2 123 86.9 119 49.1 107 73.8 117 53.9 106 53.1 113 89.0 121 54.6 109 50.9 112 81.4 119 52.4 113 51.3 116 58.8 116 54.3 113 41.2 110 82.3 120 44.6 113 55.0 104 74.3 109 19.6 24 40.7 124 56.8 124 48.8 121
Heeger++ [104]113.4 86.1 123 91.0 120 77.1 106 67.6 122 91.6 128 65.1 112 85.6 128 94.6 127 83.6 126 71.0 120 88.2 122 68.8 118 62.9 125 69.8 127 67.3 125 69.4 128 91.0 128 70.8 126 40.2 68 80.9 123 26.3 45 21.7 98 31.2 85 27.4 91
SLK [47]114.3 67.5 107 90.3 119 82.1 113 72.2 123 84.7 127 84.8 124 58.4 118 94.0 126 58.1 116 78.1 123 82.5 120 84.6 125 55.4 122 61.5 121 68.1 126 49.4 119 83.7 124 57.7 120 36.2 51 68.1 96 26.2 44 50.3 127 60.0 125 65.2 128
FFV1MT [106]115.7 85.0 122 92.0 121 84.4 116 60.6 114 83.8 125 60.8 110 85.4 127 92.2 124 88.2 128 71.2 121 89.9 125 69.1 119 64.0 127 69.3 126 78.0 128 69.2 127 91.5 129 73.2 128 51.7 97 73.9 108 45.3 81 21.7 98 31.2 85 27.4 91
TI-DOFE [24]116.4 90.7 127 94.6 126 97.1 128 76.9 127 79.5 124 89.4 128 73.1 124 96.1 129 74.4 124 84.6 127 93.6 127 88.3 127 52.9 118 59.9 117 63.9 122 44.5 114 83.6 123 53.5 117 42.9 73 68.0 95 17.0 16 50.5 128 65.5 127 62.4 126
FOLKI [16]116.6 72.5 111 84.5 105 87.5 120 62.3 118 74.9 119 74.0 121 56.9 115 87.2 118 57.7 114 59.8 117 78.1 117 65.4 117 53.3 120 61.2 120 62.7 121 50.9 121 81.0 117 62.7 124 47.3 91 73.3 106 56.8 108 49.0 126 62.9 126 64.3 127
PGAM+LK [55]121.9 79.4 120 92.7 122 88.7 122 62.9 119 76.8 123 71.9 117 59.9 119 88.3 120 62.8 119 74.9 122 90.6 126 75.6 121 54.4 121 61.0 118 64.6 123 58.1 123 82.7 121 58.6 121 77.6 127 85.6 126 78.1 126 38.8 123 52.7 123 50.7 123
Adaptive flow [45]122.3 91.3 128 95.7 127 96.2 126 75.8 126 75.9 121 86.2 125 68.9 121 85.6 117 72.4 122 80.6 125 85.2 121 83.9 124 57.3 123 61.5 121 60.2 119 66.7 125 84.1 125 70.4 125 90.2 128 92.7 128 95.0 127 27.7 112 40.5 109 37.0 110
HCIC-L [99]123.1 88.6 125 96.7 129 89.3 123 80.8 128 74.4 118 93.1 129 86.6 129 87.2 118 95.2 129 95.8 129 97.4 129 96.9 129 63.4 126 66.7 124 57.8 118 68.9 126 82.7 121 73.1 127 96.0 129 95.9 129 98.6 128 25.3 110 33.9 95 34.7 107
Pyramid LK [2]125.3 86.7 124 94.5 125 96.2 126 73.1 125 76.5 122 86.4 127 70.8 122 89.6 122 78.5 125 78.1 123 88.6 124 82.7 123 68.8 128 76.4 128 80.4 129 75.7 129 85.1 127 78.1 129 73.1 124 79.6 118 69.3 120 60.8 129 74.5 129 79.9 129
AdaConv-v1 [126]130.0 100.0 130 100.0 130 100.0 130 100.0 130 100.0 130 100.0 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 100.0 130 99.7 130 99.9 130 99.9 130 99.9 130 99.9 130
SepConv-v1 [127]130.0 100.0 130 100.0 130 100.0 130 100.0 130 100.0 130 100.0 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 100.0 130 99.7 130 99.9 130 99.9 130 99.9 130 99.9 130
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.