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        
R5.0
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]10.8 7.69 2 26.2 5 3.54 2 7.19 14 30.7 27 6.11 20 5.88 5 19.3 8 4.53 15 4.01 10 23.9 16 2.00 12 11.7 1 16.4 3 5.52 3 10.4 15 29.7 9 9.30 10 5.25 17 20.0 23 2.61 7 1.88 7 6.47 27 0.19 1
MDP-Flow2 [68]11.9 10.3 20 30.4 19 6.57 20 5.28 2 23.9 3 4.13 3 5.46 3 17.6 3 3.58 7 4.49 18 25.3 21 2.11 15 15.8 21 21.4 21 10.2 26 10.6 16 29.9 10 9.87 16 4.44 7 19.3 16 2.81 8 1.39 2 4.82 5 1.11 3
OFLAF [77]12.8 8.96 9 27.9 9 4.57 8 7.36 17 26.4 8 6.68 22 4.80 2 14.9 2 3.37 6 4.43 17 21.2 8 2.81 26 13.4 7 19.0 10 7.08 7 11.4 25 28.0 3 9.28 9 5.35 19 19.1 15 3.15 10 2.47 16 5.73 18 5.50 33
NN-field [71]14.0 8.65 5 28.3 10 4.00 4 8.38 26 33.1 41 7.44 28 5.86 4 19.0 7 4.53 15 3.15 2 21.4 10 1.25 3 12.0 4 16.9 5 5.41 2 6.58 2 20.2 1 3.45 1 8.64 47 23.6 52 2.88 9 2.47 16 8.48 39 0.20 2
PMMST [114]19.6 12.9 46 32.6 31 8.45 43 10.2 42 30.5 24 10.7 58 7.39 15 22.2 12 6.31 32 3.87 9 13.5 1 2.73 23 14.0 10 18.7 9 7.52 8 10.9 22 28.9 6 9.95 17 4.99 14 20.8 28 3.18 11 1.52 3 3.87 2 1.12 4
ComponentFusion [96]22.0 8.86 8 28.7 13 5.91 14 6.30 5 24.2 4 5.98 15 6.79 10 21.6 10 4.99 21 4.11 11 24.4 17 2.04 14 16.2 29 22.0 26 11.3 37 13.4 41 40.4 57 12.4 56 7.66 39 21.3 34 5.22 34 2.05 8 5.21 9 3.61 17
HAST [109]22.5 7.13 1 21.8 1 3.37 1 7.28 15 26.1 7 6.10 19 3.86 1 12.2 1 0.97 1 3.85 8 21.3 9 1.50 4 11.7 1 16.5 4 4.64 1 15.1 62 35.5 32 14.0 67 19.4 87 36.3 95 39.0 115 1.24 1 3.55 1 1.32 5
ALD-Flow [66]22.5 8.44 4 27.6 8 4.09 5 6.49 7 27.2 12 5.04 9 7.66 20 24.1 22 3.72 9 4.58 20 27.1 32 2.01 13 16.0 24 22.7 34 8.55 12 9.39 8 33.3 21 8.46 8 7.21 36 18.5 10 17.3 85 4.06 48 11.1 53 5.94 41
nLayers [57]23.6 8.66 6 25.4 3 4.54 6 13.0 80 32.1 34 13.7 87 7.74 22 21.8 11 8.85 61 3.29 4 18.2 2 1.89 9 11.8 3 16.2 2 6.65 6 12.2 30 28.4 4 10.3 20 8.59 46 21.5 38 4.98 30 2.36 14 5.74 19 5.08 29
NNF-EAC [103]23.6 10.9 28 31.8 26 6.97 22 6.64 9 26.4 8 5.65 11 6.61 9 20.7 9 4.44 14 5.48 39 27.1 32 2.97 31 16.5 32 22.3 30 11.1 34 12.1 29 30.7 11 9.97 18 6.42 27 21.4 36 3.94 22 2.95 30 7.51 34 4.63 26
LME [70]23.7 9.71 15 29.0 15 6.46 19 5.49 4 22.8 2 4.79 7 8.62 36 22.4 13 11.2 72 4.73 21 28.3 47 2.35 18 16.5 32 21.9 24 12.6 50 10.7 17 34.0 25 9.81 15 5.57 20 21.3 34 3.87 20 2.40 15 6.32 24 4.52 24
TC/T-Flow [76]24.6 9.15 10 32.1 28 3.69 3 6.89 12 31.2 30 4.32 4 7.32 14 23.1 17 4.08 10 5.14 30 27.2 35 2.80 25 15.6 20 21.9 24 9.71 20 8.63 6 29.2 7 8.40 6 6.72 29 20.2 25 19.7 99 3.86 46 9.43 43 6.28 47
WLIF-Flow [93]25.8 9.40 14 27.1 6 6.06 16 10.0 39 33.0 39 9.71 43 7.26 13 22.4 13 5.90 31 4.53 19 23.7 14 2.56 22 16.1 25 22.3 30 11.3 37 12.8 33 33.2 20 10.4 23 6.85 31 18.8 13 7.36 46 2.80 27 6.48 28 5.62 36
FC-2Layers-FF [74]26.8 9.97 16 28.7 13 7.96 32 10.4 44 35.3 49 9.95 44 6.11 7 18.1 6 6.82 36 4.12 12 20.9 6 2.48 21 13.3 6 17.7 6 9.47 17 13.5 42 32.6 15 11.3 35 14.0 71 26.0 65 12.5 66 1.84 5 4.18 3 4.53 25
RNLOD-Flow [121]27.0 7.93 3 24.8 2 5.41 12 8.33 25 31.7 31 6.84 24 7.47 16 23.3 18 4.60 17 3.62 6 21.1 7 1.66 7 14.4 12 21.0 15 7.80 9 12.7 32 32.9 16 12.2 55 17.4 83 34.4 90 20.7 101 2.55 19 5.57 16 5.30 31
Layers++ [37]27.8 10.2 18 29.1 16 8.58 46 10.8 49 30.6 25 11.0 64 5.90 6 17.7 4 6.34 33 3.40 5 18.2 2 1.66 7 12.2 5 16.0 1 9.69 19 13.9 47 33.6 22 11.9 49 13.9 70 27.9 70 8.74 51 2.33 13 4.94 6 5.70 39
FESL [72]28.9 8.67 7 25.6 4 4.73 9 13.6 85 39.4 85 12.9 82 8.22 30 24.3 26 7.10 40 3.76 7 20.3 5 2.12 16 14.1 11 20.1 12 9.08 14 11.2 24 31.0 12 9.80 14 11.3 56 30.4 78 7.66 48 2.10 9 5.40 12 2.40 8
SVFilterOh [111]29.2 12.7 44 28.4 11 8.58 46 9.41 34 29.1 19 8.31 34 6.39 8 17.7 4 5.05 23 3.23 3 18.8 4 0.95 2 14.9 15 20.9 14 6.51 5 13.2 37 32.1 14 11.6 42 22.0 93 46.5 118 30.5 110 1.61 4 4.97 8 2.45 10
TC-Flow [46]30.4 9.24 11 30.9 22 5.24 11 5.48 3 25.7 6 4.01 2 7.25 12 23.3 18 2.66 3 5.52 40 28.4 48 3.26 37 16.7 35 23.9 40 9.52 18 11.7 26 36.8 37 11.5 41 6.69 28 21.4 36 19.0 96 4.21 50 10.6 50 6.91 59
OAR-Flow [125]32.2 10.8 26 34.0 40 6.23 17 8.94 29 31.9 32 7.53 29 10.4 52 30.3 54 7.58 49 5.60 44 25.9 27 3.04 33 17.0 36 24.0 41 9.35 15 8.62 5 33.0 17 7.87 4 3.37 4 14.6 3 5.54 37 4.80 57 10.4 49 9.44 74
Efficient-NL [60]33.0 9.31 12 27.5 7 5.65 13 12.1 72 38.1 70 11.2 66 8.07 29 24.1 22 6.69 35 5.39 36 25.9 27 3.51 47 14.9 15 21.1 17 9.39 16 14.0 50 35.2 30 11.1 32 11.5 57 25.7 63 6.90 43 2.19 11 5.45 14 1.94 7
AGIF+OF [85]33.2 10.4 21 29.4 17 7.42 25 12.4 73 37.5 62 12.4 77 7.91 27 24.3 26 7.24 42 4.84 24 23.8 15 2.91 28 14.8 14 20.6 13 9.72 21 13.2 37 36.5 35 10.5 25 7.06 33 20.8 28 7.54 47 3.02 35 6.27 22 6.37 49
IROF++ [58]33.5 10.2 18 30.9 22 7.02 23 11.1 56 38.1 70 10.7 58 8.32 34 25.1 31 7.61 50 5.83 47 28.0 43 4.08 57 15.4 19 21.3 19 9.83 22 13.6 43 38.0 46 11.3 35 5.83 21 20.8 28 1.97 6 2.32 12 5.71 17 4.87 28
PMF [73]34.0 11.6 37 29.9 18 4.55 7 7.81 20 30.2 21 6.00 16 7.17 11 23.3 18 3.21 5 4.88 26 23.1 12 2.42 19 13.6 8 18.5 7 6.49 4 16.1 70 42.7 68 15.4 76 27.2 107 43.5 115 28.9 107 2.15 10 4.96 7 4.81 27
PH-Flow [101]34.7 10.9 28 32.6 31 7.94 31 10.9 51 37.4 60 10.5 52 7.56 19 22.8 16 7.74 53 5.75 46 27.2 35 4.04 55 14.4 12 19.8 11 8.81 13 13.0 35 33.6 22 11.1 32 12.7 64 23.1 48 17.6 87 1.84 5 4.19 4 4.50 23
Classic+CPF [83]35.8 10.9 28 31.7 25 7.88 30 11.5 62 37.9 67 10.9 62 8.27 32 25.1 31 7.51 48 5.05 28 25.8 26 3.16 35 15.1 17 21.0 15 10.6 27 13.1 36 34.6 28 10.3 20 9.87 51 22.0 43 13.1 72 2.68 22 5.85 20 5.53 34
COFM [59]36.2 10.1 17 32.0 27 7.63 26 8.06 22 30.4 23 7.17 25 8.93 41 25.9 37 8.04 57 4.17 13 24.9 19 1.63 6 18.8 48 24.0 41 18.6 89 14.4 54 33.0 17 11.7 44 8.15 42 20.4 26 14.7 79 3.16 37 5.36 11 8.09 69
Ramp [62]37.0 10.9 28 32.7 35 7.96 32 10.9 51 37.1 57 10.6 54 7.85 23 24.2 24 7.41 46 5.29 33 27.0 31 3.44 42 16.1 25 22.3 30 10.8 28 13.8 46 35.4 31 11.0 31 11.6 58 21.1 32 18.2 91 2.52 18 5.44 13 5.23 30
Sparse-NonSparse [56]37.7 10.7 24 32.5 30 8.38 41 10.9 51 36.8 55 10.7 58 7.95 28 24.5 30 7.30 44 5.42 38 27.6 38 3.49 46 16.1 25 22.1 27 11.0 32 13.3 39 36.0 34 10.6 27 10.6 54 21.1 32 10.9 57 2.91 28 5.93 21 6.18 45
Correlation Flow [75]37.7 11.9 41 35.3 41 6.03 15 6.85 11 28.0 14 4.77 6 8.29 33 25.8 35 2.17 2 4.84 24 27.2 35 2.77 24 18.5 46 25.9 53 11.7 39 16.9 78 39.5 52 16.7 83 12.1 61 24.6 59 17.8 88 2.59 20 7.33 33 3.08 11
LSM [39]38.2 10.4 21 32.6 31 8.24 36 10.8 49 37.4 60 10.4 51 7.85 23 24.3 26 7.05 38 5.32 34 27.6 38 3.41 41 15.8 21 21.5 22 11.1 34 13.7 44 35.6 33 10.9 30 13.0 65 23.2 49 12.5 66 2.99 34 6.43 26 6.14 44
ProbFlowFields [128]39.1 16.2 63 47.8 75 11.7 73 8.96 30 31.0 28 8.86 37 9.73 47 28.4 46 10.1 66 6.09 55 25.5 23 4.53 66 18.2 44 25.5 47 10.9 30 9.76 12 34.2 26 11.7 44 4.63 8 18.8 13 3.79 19 2.95 30 8.94 42 3.52 15
Classic+NL [31]40.6 10.5 23 31.4 24 8.38 41 11.1 56 37.9 67 10.6 54 7.87 25 24.0 21 7.48 47 5.57 42 27.6 38 3.62 49 15.8 21 21.5 22 10.8 28 14.1 51 37.4 41 11.4 38 14.8 74 25.9 64 13.4 75 2.61 21 5.29 10 6.10 43
FMOF [94]42.0 11.0 35 30.4 19 8.33 40 13.0 80 38.5 76 12.6 78 7.51 17 22.6 15 7.34 45 5.06 29 25.2 20 3.44 42 15.3 18 21.3 19 9.87 24 14.9 61 33.1 19 11.4 38 11.7 60 24.3 58 15.0 81 3.92 47 8.59 40 6.28 47
S2D-Matching [84]42.1 10.7 24 32.2 29 8.71 48 10.7 47 36.6 54 10.2 47 8.94 42 27.2 42 6.96 37 5.17 31 26.0 29 3.36 40 16.3 30 22.1 27 10.9 30 14.4 54 37.0 38 11.7 44 16.4 78 26.0 65 16.5 83 2.79 26 5.49 15 6.49 50
IIOF-NLDP [131]44.0 14.6 51 41.6 59 6.71 21 11.0 55 37.5 62 8.25 33 8.77 39 26.9 40 4.19 11 6.07 54 28.0 43 3.76 51 19.7 60 27.1 66 12.6 50 16.7 75 40.7 59 15.4 76 4.68 11 23.0 46 4.41 26 2.78 25 7.26 32 3.16 12
IROF-TV [53]44.8 11.6 37 35.3 41 9.03 51 11.2 59 38.2 73 10.9 62 8.85 40 26.5 38 7.73 52 6.04 53 33.0 71 3.62 49 17.1 37 23.1 36 13.5 61 16.3 71 44.8 75 13.5 65 3.41 5 16.9 5 1.13 3 2.71 23 6.80 30 5.67 38
TV-L1-MCT [64]45.2 10.9 28 30.5 21 8.56 45 13.8 88 40.9 95 13.2 84 8.68 37 25.8 35 7.98 56 4.83 23 25.7 24 3.26 37 17.4 39 23.5 39 13.7 65 14.8 60 36.7 36 12.7 59 5.84 22 19.4 17 10.1 55 3.53 41 6.42 25 6.63 53
Aniso-Texture [82]46.5 9.33 13 28.5 12 7.26 24 9.17 32 26.8 11 10.2 47 10.1 51 29.2 49 7.28 43 2.77 1 22.6 11 0.94 1 19.9 63 27.1 66 13.3 59 14.6 57 38.5 49 12.4 56 31.5 113 46.3 117 18.2 91 4.45 53 10.1 46 6.60 52
2DHMM-SAS [92]46.6 10.9 28 32.6 31 8.06 34 11.5 62 39.5 86 10.6 54 10.0 50 28.3 45 7.91 55 5.93 49 28.2 46 4.07 56 16.1 25 22.3 30 11.0 32 13.7 44 38.3 48 11.1 32 12.3 63 23.2 49 18.0 90 3.08 36 6.48 28 6.24 46
AggregFlow [97]46.7 13.9 49 33.8 38 11.2 67 13.7 86 39.6 88 12.6 78 12.0 65 31.3 55 13.7 81 5.40 37 23.5 13 3.44 42 17.5 40 25.4 45 7.98 11 8.57 4 25.9 2 8.42 7 7.00 32 24.1 57 4.53 27 5.53 67 9.80 45 11.7 85
CostFilter [40]46.8 14.1 50 36.2 47 8.48 44 8.61 28 30.6 25 7.43 27 8.26 31 26.9 40 4.40 13 5.72 45 28.1 45 3.24 36 13.7 9 18.5 7 7.81 10 16.6 74 45.0 80 16.0 80 26.8 105 48.6 121 32.7 111 2.93 29 7.59 35 5.38 32
SimpleFlow [49]46.9 11.6 37 33.7 37 8.98 50 12.5 76 38.9 79 12.6 78 10.4 52 29.3 50 9.20 62 5.99 50 27.6 38 4.08 57 16.3 30 22.2 29 11.1 34 16.7 75 37.4 41 12.7 59 8.29 43 19.9 21 6.11 41 2.74 24 6.28 23 5.86 40
Adaptive [20]47.5 10.9 28 33.8 38 4.92 10 10.5 45 35.0 48 9.53 42 12.2 66 33.7 59 7.68 51 5.57 42 30.3 57 2.95 30 21.7 86 26.7 61 20.6 95 10.8 19 34.9 29 7.26 3 14.0 71 28.8 73 4.88 28 4.50 55 10.2 48 6.84 57
MDP-Flow [26]48.0 12.2 43 40.6 54 8.88 49 9.32 33 28.3 16 10.5 52 9.09 43 28.1 44 9.37 64 6.03 52 30.6 59 3.99 53 17.2 38 23.1 36 12.4 45 13.9 47 42.7 68 12.5 58 7.10 35 23.6 52 4.09 24 5.35 63 13.2 63 7.09 62
RFlow [90]48.4 14.8 52 43.9 64 11.2 67 6.64 9 26.6 10 5.76 12 11.7 61 35.9 68 5.04 22 4.31 15 27.1 32 1.94 10 19.4 52 26.8 63 13.0 58 14.7 58 42.2 65 11.8 48 13.1 66 22.2 44 13.1 72 5.87 72 14.1 70 8.71 72
Occlusion-TV-L1 [63]49.1 12.9 46 36.1 46 8.26 39 9.51 37 32.7 36 8.99 38 12.3 67 34.4 63 8.27 58 5.53 41 29.8 55 3.04 33 20.5 74 28.5 85 13.8 67 9.95 13 37.9 43 11.6 42 7.64 38 21.8 42 3.47 13 5.69 69 13.9 68 7.59 66
OFH [38]50.4 15.0 54 40.9 55 14.4 84 7.06 13 29.9 20 5.37 10 10.8 55 33.1 58 4.86 20 5.84 48 30.6 59 3.46 45 19.5 55 26.1 54 15.3 73 15.6 66 46.5 87 16.6 82 4.19 6 21.7 40 3.74 18 5.39 65 15.4 79 7.23 63
MLDP_OF [89]52.8 18.8 84 51.3 90 16.0 87 8.16 23 32.0 33 6.76 23 10.7 54 31.9 57 5.45 26 4.81 22 26.1 30 2.44 20 18.7 47 24.3 43 13.7 65 15.6 66 37.9 43 18.6 91 19.2 86 28.5 71 38.7 114 3.53 41 7.25 31 4.27 21
DeepFlow2 [108]54.7 15.0 54 43.6 63 11.0 63 10.1 40 34.2 45 9.29 40 12.9 69 36.8 69 11.1 71 7.47 70 32.1 66 4.75 70 17.8 41 25.4 45 9.97 25 10.7 17 40.2 56 10.3 20 6.78 30 18.7 12 13.3 74 9.05 91 17.3 87 15.3 95
Steered-L1 [118]55.5 11.4 36 37.9 49 7.71 29 4.42 1 21.7 1 3.76 1 7.71 21 25.7 33 4.29 12 4.91 27 29.8 55 2.26 17 20.2 69 26.7 61 16.6 80 18.1 83 46.1 86 14.6 69 32.4 114 37.9 102 51.5 124 8.58 88 15.5 81 15.2 94
Sparse Occlusion [54]56.7 12.7 44 35.8 45 8.24 36 12.4 73 33.4 42 13.4 85 9.67 45 29.1 48 6.55 34 5.99 50 28.5 49 3.56 48 19.4 52 26.4 59 12.4 45 14.7 58 39.4 51 11.7 44 37.7 121 48.6 121 17.8 88 3.66 43 9.43 43 5.64 37
S2F-IF [123]56.7 18.0 77 51.9 93 10.9 60 11.1 56 38.6 77 10.6 54 13.9 74 40.6 83 13.4 80 7.68 77 32.6 68 5.18 76 19.7 60 27.2 69 13.3 59 10.8 19 39.5 52 11.9 49 4.99 14 19.9 21 6.26 42 3.26 39 10.1 46 3.57 16
PGM-C [120]56.9 17.7 72 50.5 84 11.0 63 11.9 66 39.1 81 11.6 71 13.9 74 40.4 82 13.3 79 7.52 73 35.8 84 4.62 69 19.6 58 27.5 72 12.4 45 9.48 10 37.9 43 9.36 11 4.63 8 16.9 5 5.02 31 4.83 58 14.2 73 6.69 54
Classic++ [32]57.0 10.8 26 32.7 35 8.25 38 10.5 45 32.9 37 10.7 58 10.8 55 31.6 56 8.46 59 5.25 32 29.7 54 2.99 32 20.0 65 28.0 80 13.9 68 15.2 64 44.1 73 11.9 49 17.3 82 26.2 67 18.3 93 5.82 71 12.7 59 8.14 70
CPM-Flow [116]57.7 17.7 72 50.5 84 11.0 63 11.9 66 39.0 80 11.7 72 13.7 73 39.8 80 13.2 77 7.49 72 35.5 82 4.58 68 19.5 55 27.2 69 12.3 44 9.44 9 37.3 40 9.46 12 5.05 16 19.5 18 5.17 33 5.21 61 14.8 75 7.36 64
BriefMatch [124]57.9 11.8 40 35.7 44 6.41 18 7.52 18 30.3 22 5.97 14 7.54 18 24.2 24 4.62 18 4.28 14 25.4 22 1.98 11 20.6 76 26.2 57 20.9 97 26.8 110 49.2 90 28.2 112 22.8 97 35.9 94 39.6 118 9.81 94 15.1 78 18.3 103
FlowFields+ [130]58.2 18.4 79 52.2 96 11.4 70 11.9 66 39.9 90 11.5 68 14.9 80 43.4 91 14.4 84 7.97 80 33.1 72 5.58 80 19.4 52 26.9 65 12.7 53 10.2 14 39.9 55 10.5 25 4.74 12 20.1 24 4.29 25 3.80 44 12.4 58 3.48 14
ACK-Prior [27]58.3 19.5 88 41.5 57 14.3 83 6.57 8 27.6 13 4.53 5 7.87 25 25.7 33 3.70 8 4.33 16 25.7 24 1.53 5 20.5 74 25.6 50 18.3 86 23.1 101 44.0 72 18.5 90 29.9 109 33.1 85 45.6 122 7.91 85 14.8 75 11.7 85
NL-TV-NCC [25]58.8 16.5 66 40.4 52 9.10 52 10.7 47 37.0 56 8.07 31 8.59 35 26.8 39 3.17 4 6.24 57 33.4 73 3.26 37 21.4 83 29.7 97 12.7 53 21.2 93 48.2 89 17.3 86 13.4 69 35.6 93 13.0 71 4.73 56 12.8 60 3.24 13
EpicFlow [102]59.0 17.7 72 50.6 86 10.9 60 12.0 70 39.3 84 11.7 72 14.5 77 42.2 87 13.2 77 7.47 70 35.5 82 4.57 67 19.8 62 27.6 73 12.8 56 9.73 11 38.1 47 10.1 19 4.63 8 17.2 7 4.88 28 5.31 62 14.3 74 7.47 65
CombBMOF [113]59.4 15.2 56 48.2 77 7.67 27 11.3 60 34.5 46 9.95 44 8.75 38 27.2 42 5.37 25 7.60 76 32.1 66 5.65 83 18.0 43 23.0 35 13.9 68 21.7 96 44.9 77 24.3 104 22.6 95 37.2 97 14.5 78 2.97 33 7.73 37 4.35 22
Complementary OF [21]60.5 20.9 90 51.7 91 21.5 96 6.41 6 28.3 16 4.86 8 9.56 44 30.2 53 5.62 27 8.21 82 31.4 62 6.20 86 19.2 50 25.6 50 15.5 74 21.5 95 49.3 91 17.4 87 6.34 25 19.8 20 11.5 60 6.44 78 16.1 84 10.2 78
FlowFields [110]60.9 18.3 78 51.9 93 11.1 66 11.9 66 39.5 86 11.5 68 14.8 78 43.3 90 14.2 82 7.96 79 33.5 76 5.52 79 19.9 63 27.6 73 13.6 63 11.0 23 40.5 58 12.1 53 4.93 13 19.7 19 5.34 36 3.85 45 12.3 57 3.89 18
TF+OM [100]61.7 14.8 52 35.4 43 7.68 28 9.06 31 28.4 18 9.32 41 11.6 60 28.4 46 16.0 89 6.43 58 29.0 52 4.29 60 20.2 69 25.6 50 18.4 87 17.9 81 38.5 49 16.9 85 16.6 79 33.8 86 14.7 79 6.87 81 15.5 81 9.68 75
ROF-ND [107]62.3 18.4 79 45.8 71 11.5 71 7.31 16 25.4 5 6.02 17 9.70 46 29.4 51 4.66 19 9.09 88 28.7 50 5.98 85 21.6 85 29.5 94 14.5 71 19.9 89 44.8 75 15.3 75 33.3 118 41.0 107 30.1 109 2.95 30 7.63 36 2.41 9
TV-L1-improved [17]63.5 11.9 41 36.8 48 8.23 35 8.49 27 31.0 28 7.83 30 11.9 63 33.7 59 7.19 41 5.35 35 28.9 51 2.91 28 20.3 72 28.0 80 12.0 42 27.2 112 55.4 107 30.4 114 23.1 100 38.0 103 22.9 104 5.61 68 14.0 69 7.74 68
DeepFlow [86]63.9 17.5 71 46.9 73 16.5 88 11.8 64 35.8 50 11.2 66 15.1 81 39.6 79 15.2 87 7.81 78 32.6 68 5.12 75 17.8 41 25.5 47 9.86 23 12.0 28 44.9 77 11.4 38 6.11 24 18.0 8 12.8 69 10.8 100 18.7 94 18.8 105
ComplOF-FED-GPU [35]64.0 17.9 75 52.0 95 15.4 86 7.90 21 33.9 44 5.82 13 10.8 55 34.2 61 5.67 28 6.99 65 31.5 63 4.51 65 19.2 50 26.3 58 12.9 57 18.2 84 50.5 97 18.6 91 15.1 75 23.6 52 22.3 103 5.37 64 15.4 79 6.76 55
TCOF [69]64.1 17.2 70 45.4 70 15.3 85 12.6 77 37.6 64 12.3 75 15.7 83 39.5 77 16.6 90 6.72 61 27.7 42 4.48 64 22.5 92 30.9 106 11.9 40 9.21 7 28.4 4 10.8 29 22.9 98 35.0 92 9.29 52 4.22 51 11.3 54 6.79 56
EPPM w/o HM [88]65.0 19.4 86 53.2 98 11.2 67 8.23 24 34.8 47 6.07 18 11.1 58 35.1 66 5.89 30 7.31 68 33.4 73 4.76 71 18.9 49 23.2 38 17.1 82 21.3 94 50.3 96 20.1 95 20.7 91 30.3 77 40.9 120 3.20 38 8.13 38 5.59 35
HBM-GC [105]65.7 31.9 101 41.2 56 25.6 101 13.2 82 32.9 37 14.2 89 9.93 49 24.4 29 8.75 60 10.1 96 24.7 18 6.95 94 16.6 34 21.1 17 13.6 63 18.5 85 33.7 24 15.5 78 33.9 120 47.5 120 20.1 100 3.38 40 8.62 41 5.97 42
Aniso. Huber-L1 [22]67.8 13.6 48 40.4 52 9.77 53 19.4 94 40.1 91 22.0 94 16.4 86 38.4 71 18.3 92 7.56 74 33.4 73 5.00 74 20.1 68 27.7 78 12.5 48 14.5 56 39.7 54 10.4 23 20.8 92 32.0 81 12.9 70 4.35 52 10.8 51 6.56 51
SIOF [67]68.2 16.5 66 40.1 51 10.8 59 10.3 43 37.1 57 9.10 39 16.4 86 38.3 70 18.4 93 8.56 83 35.1 80 5.87 84 21.3 82 28.5 85 16.5 79 17.6 80 43.6 71 19.7 94 7.08 34 21.6 39 3.65 16 6.65 79 16.1 84 10.9 82
F-TV-L1 [15]68.5 31.8 100 60.6 103 43.6 112 13.7 86 38.4 75 13.1 83 15.6 82 39.4 76 10.1 66 10.9 98 37.3 90 8.78 99 20.0 65 26.5 60 16.0 78 12.9 34 40.7 59 10.7 28 9.68 50 23.7 55 3.52 15 4.49 54 12.0 55 4.19 20
Rannacher [23]68.5 15.5 59 43.5 62 10.7 57 11.4 61 35.8 50 11.5 68 14.2 76 39.0 75 10.8 68 6.59 60 30.8 61 4.20 59 21.0 79 29.6 96 12.6 50 19.1 87 50.8 98 15.2 73 14.7 73 26.8 68 16.7 84 4.86 59 12.9 61 7.03 61
LocallyOriented [52]71.1 15.8 62 41.5 57 10.9 60 15.0 91 44.5 100 13.7 87 17.6 90 43.4 91 14.2 82 7.16 66 31.5 63 4.82 72 21.0 79 29.0 90 12.5 48 11.7 26 34.5 27 12.9 61 11.6 58 29.6 74 12.0 63 7.94 86 18.4 91 11.1 83
Brox et al. [5]71.2 18.5 81 51.2 88 20.8 95 14.0 89 37.8 66 15.1 91 13.6 72 38.8 73 11.7 73 7.20 67 36.8 87 4.02 54 23.0 96 28.5 85 24.3 105 10.8 19 45.3 82 9.57 13 7.81 40 22.7 45 1.58 4 9.61 93 19.2 97 15.0 93
SRR-TVOF-NL [91]71.3 22.3 96 44.7 66 12.5 77 12.0 70 38.1 70 10.2 47 14.8 78 40.6 83 10.9 70 6.13 56 34.1 77 2.81 26 19.6 58 25.5 47 13.5 61 16.4 72 42.4 66 13.0 62 30.5 110 42.5 111 18.3 93 6.41 77 11.0 52 12.0 87
CRTflow [80]72.0 16.5 66 49.5 80 10.6 56 9.63 38 33.8 43 8.65 36 13.1 70 38.8 73 7.80 54 6.86 63 34.3 78 4.44 63 20.0 65 27.8 79 12.2 43 31.4 117 59.0 113 36.7 118 10.3 52 30.4 78 12.0 63 8.56 87 20.4 103 12.9 91
DPOF [18]72.4 20.5 89 50.2 83 10.5 54 12.6 77 41.8 97 11.0 64 11.8 62 34.3 62 10.8 68 8.61 85 38.9 96 5.43 77 19.5 55 26.1 54 15.1 72 16.8 77 41.5 64 15.2 73 23.3 101 23.9 56 50.1 123 5.05 60 14.1 70 4.13 19
Bartels [41]74.2 19.3 85 39.6 50 22.4 98 9.47 36 28.2 15 10.0 46 9.91 48 29.7 52 7.09 39 9.18 90 29.3 53 7.40 96 21.7 86 27.6 73 21.1 98 19.1 87 44.4 74 24.2 103 23.0 99 36.3 95 36.2 112 7.46 84 14.9 77 11.5 84
Dynamic MRF [7]74.8 22.0 94 52.3 97 25.2 100 7.67 19 33.0 39 6.18 21 12.4 68 39.8 80 5.34 24 6.49 59 35.4 81 3.86 52 22.9 94 29.2 92 20.7 96 22.2 98 57.8 110 22.9 100 7.42 37 18.1 9 25.1 105 13.2 106 21.3 107 20.5 108
DF-Auto [115]77.2 19.4 86 46.0 72 10.5 54 26.6 103 46.1 102 31.1 103 23.7 101 46.1 95 37.0 103 9.05 87 36.8 87 5.59 81 21.7 86 29.1 91 17.4 84 7.80 3 31.8 13 7.93 5 19.5 88 37.4 99 3.25 12 10.9 101 19.6 99 16.4 97
TriangleFlow [30]77.2 18.7 83 43.9 64 18.0 89 10.1 40 37.2 59 8.18 32 11.9 63 35.5 67 5.81 29 6.72 61 34.6 79 4.37 62 26.7 112 34.7 113 23.4 102 23.1 101 49.6 92 23.5 101 16.7 80 37.2 97 16.3 82 6.85 80 17.3 87 10.3 79
CBF [12]77.4 15.2 56 44.8 67 12.1 75 23.7 99 37.7 65 30.9 102 13.2 71 34.6 64 14.5 85 6.86 63 32.8 70 4.32 61 22.6 93 28.4 84 20.2 93 15.6 66 41.0 62 12.1 53 32.9 116 39.7 105 29.8 108 5.49 66 13.2 63 8.30 71
Local-TV-L1 [65]77.7 24.6 97 51.2 88 30.0 102 22.5 98 40.6 93 25.2 97 23.5 100 46.1 95 28.3 98 9.73 93 37.4 91 6.92 93 18.3 45 25.2 44 12.7 53 13.9 47 43.2 70 12.0 52 5.25 17 20.6 27 5.15 32 15.8 111 21.0 105 32.1 116
SuperFlow [81]78.9 16.2 63 42.7 61 13.0 79 20.9 95 39.6 88 25.0 96 19.7 95 40.6 83 31.8 100 9.89 95 41.2 99 7.16 95 20.9 78 27.1 66 20.3 94 12.2 30 41.1 63 11.3 35 19.0 85 32.1 83 3.87 20 10.1 96 19.3 98 16.4 97
LDOF [28]79.2 17.1 69 48.0 76 12.9 78 13.3 83 40.6 93 12.2 74 15.8 84 42.4 88 12.7 76 9.70 92 44.0 101 6.27 88 20.7 77 28.0 80 16.8 81 14.3 53 45.9 85 13.8 66 8.36 44 23.3 51 7.98 49 11.2 103 21.2 106 18.3 103
CNN-flow-warp+ref [117]80.2 18.5 81 50.0 81 13.9 82 17.8 93 37.9 67 21.1 93 21.3 98 47.3 99 29.7 99 9.13 89 38.8 94 6.72 91 21.8 89 28.2 83 19.6 90 14.2 52 45.7 84 13.1 63 5.94 23 18.5 10 10.9 57 12.4 105 20.6 104 16.4 97
CLG-TV [48]80.4 15.7 61 42.2 60 11.7 73 20.9 95 39.2 83 24.8 95 16.4 86 39.5 77 18.0 91 9.23 91 37.9 92 6.54 89 22.9 94 30.0 101 17.9 85 16.5 73 47.2 88 14.2 68 19.9 89 30.4 78 11.5 60 5.79 70 14.1 70 6.98 60
TriFlow [95]80.8 21.3 92 44.9 68 13.5 81 16.0 92 36.5 53 18.7 92 18.2 92 38.6 72 27.8 97 7.35 69 30.3 57 5.59 81 21.2 81 27.3 71 18.5 88 15.1 62 37.1 39 15.0 72 49.5 126 41.7 109 95.6 129 6.38 76 13.3 65 9.77 76
p-harmonic [29]81.2 21.2 91 63.8 108 20.6 94 12.4 73 35.9 52 12.7 81 17.7 91 47.5 100 14.9 86 10.9 98 42.1 100 8.85 100 20.4 73 26.1 54 17.1 82 17.9 81 52.5 101 18.4 89 15.6 77 28.6 72 5.86 39 5.89 73 13.5 66 7.67 67
FlowNetS+ft+v [112]81.9 15.2 56 44.9 68 10.7 57 13.4 84 38.2 73 13.4 85 18.8 94 42.8 89 24.4 95 9.01 86 38.8 94 6.24 87 23.2 98 31.5 110 15.9 76 13.3 39 42.6 67 13.1 63 18.2 84 32.6 84 21.9 102 8.73 89 19.1 96 12.8 90
Second-order prior [8]84.2 15.6 60 48.2 77 12.1 75 12.6 77 39.1 81 12.3 75 16.2 85 44.6 93 12.2 75 7.57 75 31.6 65 5.45 78 22.2 90 30.6 103 14.3 70 20.8 92 56.8 109 17.7 88 28.0 108 33.8 86 27.1 106 7.43 83 17.4 89 10.4 81
StereoFlow [44]86.0 85.4 129 89.0 129 87.9 128 73.1 129 88.5 129 68.8 124 66.8 128 87.5 128 52.4 121 81.5 128 91.1 128 78.5 128 25.9 111 27.6 73 29.7 116 6.38 1 29.4 8 6.60 2 1.39 1 10.9 1 0.20 1 6.34 75 13.8 67 10.3 79
Fusion [6]87.1 17.9 75 57.7 100 18.6 90 9.42 35 32.3 35 10.2 47 11.4 59 34.8 65 11.7 73 8.57 84 40.2 97 6.89 92 25.0 108 30.8 105 24.9 109 23.9 104 52.3 100 25.0 107 33.3 118 43.4 113 19.3 98 9.01 90 18.8 95 13.4 92
Learning Flow [11]87.8 16.4 65 47.3 74 11.5 71 14.0 89 40.3 92 14.4 90 16.4 86 41.7 86 15.6 88 8.05 81 40.7 98 4.87 73 27.1 114 35.0 115 22.5 101 17.2 79 50.0 95 16.0 80 15.5 76 34.1 89 13.9 77 10.1 96 20.2 102 12.5 89
SegOF [10]89.7 28.8 99 51.1 87 13.2 80 37.3 112 51.8 112 44.6 114 30.0 106 53.0 104 43.3 111 27.0 111 49.6 106 22.4 107 24.0 104 27.6 73 28.4 115 24.9 107 58.5 111 24.4 105 2.04 2 16.2 4 0.47 2 10.0 95 16.5 86 16.7 100
Ad-TV-NDC [36]91.6 44.8 115 63.0 106 69.1 121 40.3 115 48.4 107 48.3 116 34.8 110 58.5 107 39.9 106 26.5 110 47.8 105 27.7 111 20.2 69 28.5 85 11.9 40 15.2 64 40.9 61 14.7 70 8.46 45 21.0 31 5.69 38 23.9 121 28.3 121 41.9 125
StereoOF-V1MT [119]92.2 21.7 93 68.0 113 20.3 93 11.8 64 50.4 110 7.18 26 20.7 97 62.8 110 9.21 63 9.80 94 50.8 107 6.56 90 27.9 116 35.8 116 23.9 103 25.0 108 67.3 118 24.0 102 8.00 41 27.7 69 12.2 65 13.4 107 23.5 111 15.8 96
Shiralkar [42]92.5 22.0 94 69.5 115 19.6 91 10.9 51 42.6 98 8.48 35 18.4 93 54.0 105 9.43 65 10.1 96 45.4 102 7.72 97 21.5 84 28.9 89 15.9 76 26.8 110 60.7 114 25.4 109 24.3 103 29.9 76 39.4 116 11.0 102 23.8 112 12.2 88
FlowNet2 [122]95.2 47.2 116 61.0 104 42.4 109 44.5 117 57.5 114 51.3 118 37.6 113 64.7 112 43.1 110 21.0 105 35.8 84 17.9 104 25.8 109 30.6 103 24.6 107 20.4 90 49.7 94 21.0 96 32.5 115 53.3 125 4.06 23 4.13 49 13.0 62 1.49 6
HBpMotionGpu [43]98.0 32.0 102 50.0 81 22.9 99 36.1 111 47.0 104 43.9 113 29.2 105 51.9 103 38.6 105 13.0 100 37.1 89 10.2 101 23.5 100 29.5 94 24.2 104 18.9 86 44.9 77 15.9 79 33.2 117 41.2 108 12.6 68 11.8 104 18.5 92 22.7 109
IAOF2 [51]98.2 25.3 98 49.2 79 22.2 97 24.6 100 44.3 99 28.6 101 20.0 96 45.4 94 25.5 96 49.8 120 57.5 115 60.5 122 23.2 98 31.0 107 15.7 75 23.2 103 49.6 92 19.3 93 30.5 110 39.0 104 19.0 96 9.25 92 18.6 93 9.82 77
Modified CLG [34]98.4 34.8 106 61.1 105 35.3 104 33.3 109 46.5 103 41.7 112 36.8 112 63.0 111 45.1 115 22.1 106 55.4 111 18.7 106 23.9 102 31.2 108 21.7 100 15.8 69 51.5 99 14.8 71 9.01 48 24.6 59 11.1 59 17.6 115 25.7 117 29.6 114
2D-CLG [1]99.7 44.0 113 63.3 107 36.1 105 44.3 116 52.3 113 55.1 120 49.1 121 75.4 119 50.5 119 64.3 125 76.4 124 67.8 125 24.8 106 29.7 97 27.4 112 20.5 91 53.6 104 22.4 98 2.52 3 13.0 2 3.50 14 22.8 120 27.9 120 36.9 119
Filter Flow [19]100.0 33.3 103 51.7 91 20.1 92 25.0 101 47.2 106 27.7 100 27.7 103 50.0 101 37.9 104 31.7 113 54.1 110 29.9 112 25.8 109 31.2 108 28.3 114 26.4 109 52.9 103 24.7 106 42.3 123 61.5 127 13.6 76 6.09 74 12.1 56 6.88 58
SPSA-learn [13]100.2 35.8 107 71.2 117 43.1 111 28.4 105 47.0 104 32.8 106 31.4 108 57.7 106 42.2 109 22.2 107 51.0 108 22.9 109 23.9 102 29.4 93 24.6 107 24.8 106 56.5 108 25.1 108 10.7 55 25.1 61 3.72 17 21.7 118 24.9 116 35.5 118
BlockOverlap [61]101.1 41.4 109 54.1 99 36.2 106 27.3 104 41.4 96 32.6 105 26.2 102 46.5 97 31.8 100 20.0 103 36.6 86 18.1 105 22.4 91 26.8 63 25.7 110 24.5 105 45.6 83 21.1 97 39.3 122 47.0 119 43.5 121 13.8 108 16.0 83 28.7 113
IAOF [50]102.1 33.8 104 58.3 101 40.6 107 33.0 108 44.5 100 39.5 110 30.6 107 58.7 108 33.8 102 34.1 116 52.4 109 40.8 116 23.1 97 29.9 99 19.7 92 22.5 99 53.7 105 16.8 84 22.3 94 34.0 88 10.0 54 19.5 116 23.9 113 37.1 121
GroupFlow [9]103.2 42.7 110 67.1 112 53.4 114 44.8 118 63.8 122 50.2 117 36.7 111 69.4 116 43.9 112 17.2 102 46.0 103 16.7 102 27.8 115 34.9 114 21.2 99 36.7 121 67.0 117 43.6 122 6.40 26 21.7 40 7.17 44 16.6 112 25.9 118 25.0 110
GraphCuts [14]103.4 34.5 105 59.0 102 32.1 103 26.2 102 51.1 111 26.4 99 28.1 104 51.7 102 40.4 108 13.0 100 47.5 104 7.98 98 23.7 101 30.0 101 24.3 105 33.4 118 45.2 81 25.7 110 31.2 112 37.7 101 36.8 113 10.7 99 19.7 100 17.7 102
Black & Anandan [4]103.6 38.5 108 69.5 115 53.4 114 28.5 106 49.6 109 32.0 104 33.4 109 60.5 109 40.2 107 22.6 108 55.9 112 22.8 108 24.5 105 32.2 112 19.6 90 21.7 96 58.9 112 22.7 99 22.6 95 37.6 100 5.27 35 16.7 113 22.6 110 25.4 111
2bit-BM-tele [98]106.4 55.1 120 64.1 110 69.1 121 21.4 97 38.7 78 25.3 98 23.3 99 46.7 98 21.2 94 26.2 109 38.7 93 25.2 110 24.9 107 29.9 99 27.7 113 31.3 116 52.6 102 34.6 116 43.3 124 51.7 124 54.5 125 10.3 98 20.1 101 16.8 101
Nguyen [33]107.8 43.9 112 66.0 111 42.8 110 54.0 123 49.4 108 70.1 125 42.9 116 67.4 114 47.3 117 55.4 123 65.7 119 64.4 123 27.0 113 31.8 111 31.0 117 22.8 100 54.8 106 27.3 111 13.1 66 25.2 62 6.08 40 22.2 119 26.9 119 38.9 122
SILK [79]109.9 49.5 119 69.2 114 69.3 123 39.9 114 60.6 117 47.0 115 40.4 115 70.7 117 45.6 116 32.0 114 56.5 113 31.2 114 31.4 118 36.9 119 33.3 119 31.1 115 63.2 115 32.3 115 10.3 52 23.0 46 17.3 85 25.0 122 31.9 122 36.9 119
UnFlow [129]110.1 70.9 125 78.9 120 58.5 117 51.3 122 67.4 125 56.9 121 54.4 123 83.6 126 52.8 122 33.4 115 60.2 116 30.1 113 36.7 125 38.4 122 46.2 126 38.2 122 69.6 121 42.8 121 26.2 104 40.3 106 1.60 5 7.20 82 18.3 90 8.75 73
Periodicity [78]111.8 48.9 118 63.9 109 41.0 108 34.5 110 60.0 116 37.5 109 55.4 124 67.4 114 56.6 124 20.4 104 56.9 114 17.5 103 53.2 128 66.7 129 46.5 127 48.3 126 76.0 127 46.4 124 9.14 49 34.4 90 9.98 53 28.3 125 48.2 128 40.6 124
Horn & Schunck [3]112.2 43.3 111 80.7 123 58.6 118 32.5 107 59.7 115 35.1 107 40.2 114 76.3 122 44.7 114 31.5 112 64.8 117 32.6 115 29.3 117 36.4 118 27.0 111 27.5 113 68.7 119 29.7 113 27.0 106 43.3 112 7.32 45 25.9 123 36.5 124 34.6 117
Heeger++ [104]114.8 61.9 123 80.2 122 47.4 113 44.8 118 77.8 128 40.9 111 68.0 129 84.7 127 62.1 127 43.6 119 69.1 120 41.9 117 32.6 120 37.9 120 32.0 118 51.1 127 78.4 128 54.2 127 13.3 68 43.4 113 10.4 56 15.0 109 21.4 108 19.6 106
SLK [47]115.5 44.7 114 78.9 120 59.1 119 58.2 125 71.2 126 70.9 126 47.5 119 83.5 125 50.6 120 65.0 126 69.5 121 73.4 126 34.7 122 38.9 124 42.9 125 34.8 119 70.9 124 39.4 119 12.1 61 29.8 75 11.5 60 34.4 126 40.1 125 48.8 126
TI-DOFE [24]117.6 73.1 126 84.6 127 89.6 129 61.2 127 64.7 124 74.8 128 58.6 125 88.7 129 58.0 125 70.9 127 81.6 126 76.1 127 31.7 119 38.0 121 35.4 120 29.7 114 68.7 119 36.3 117 17.1 81 32.0 81 8.67 50 35.5 127 42.8 126 49.8 127
FFV1MT [106]117.8 59.9 122 77.8 119 53.6 116 37.7 113 72.5 127 37.0 108 63.6 127 82.1 124 62.4 128 42.8 118 73.9 123 42.6 118 41.9 127 45.8 127 52.3 128 52.5 128 81.8 129 56.1 128 20.2 90 42.4 110 18.3 93 15.0 109 21.4 108 19.6 106
FOLKI [16]120.0 48.0 117 71.5 118 68.8 120 48.6 121 63.2 119 59.5 122 43.0 117 75.6 120 44.0 113 40.4 117 65.6 118 45.8 119 35.3 123 40.6 125 41.6 123 36.3 120 71.6 126 44.4 123 23.6 102 44.7 116 40.4 119 36.9 128 43.4 127 54.5 128
PGAM+LK [55]122.0 58.6 121 80.9 124 69.8 124 45.1 120 63.7 121 51.9 119 43.2 118 76.2 121 47.5 118 50.3 121 82.2 127 51.4 120 32.7 121 36.0 117 42.3 124 41.4 123 70.1 122 41.2 120 56.3 127 58.0 126 55.0 126 25.9 123 32.4 123 40.3 123
Adaptive flow [45]122.4 76.7 128 83.7 126 86.4 126 57.9 124 63.6 120 67.3 123 48.7 120 73.2 118 52.9 123 52.7 122 69.9 122 56.0 121 35.4 124 38.4 122 39.4 122 46.1 124 70.6 123 47.6 125 73.1 128 75.2 128 88.1 127 17.2 114 24.6 115 25.6 112
HCIC-L [99]124.2 76.5 127 86.4 128 73.3 125 70.1 128 62.5 118 85.3 129 63.5 126 66.1 113 79.5 129 83.3 129 91.8 129 86.5 129 39.0 126 42.8 126 38.7 121 46.4 125 66.6 116 52.3 126 89.6 129 85.9 129 94.0 128 19.8 117 24.2 114 29.6 114
Pyramid LK [2]125.7 68.1 124 83.5 125 86.8 127 59.4 126 64.5 123 73.1 127 52.8 122 76.3 122 61.4 126 60.2 124 79.0 125 65.9 124 53.8 129 61.8 128 64.5 129 59.4 129 71.1 125 63.0 129 43.9 125 49.4 123 39.5 117 50.2 129 60.2 129 70.8 129
AdaConv-v1 [126]130.0 100.0 130 99.9 130 100.0 130 100.0 130 100.0 130 100.0 130 99.9 130 99.9 130 99.9 130 100.0 130 100.0 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.8 130 100.0 130 99.7 130 97.0 130 99.9 130 99.9 130 99.9 130 99.9 130
SepConv-v1 [127]130.0 100.0 130 99.9 130 100.0 130 100.0 130 100.0 130 100.0 130 99.9 130 99.9 130 99.9 130 100.0 130 100.0 130 99.9 130 99.9 130 99.9 130 99.9 130 99.9 130 99.8 130 100.0 130 99.7 130 97.0 130 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.