Optical flow evaluation results Statistics:     Average   SD   R2.5   R5.0   R10.0   A90   A95   A99  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
A95
interpolation
error		 avg. Mequon
(Hidden texture)
im0   GT   im1 		Schefflera
(Hidden texture)
im0   GT   im1 		Urban
(Synthetic)
im0   GT   im1 		Teddy
(Stereo)
im0   GT   im1 		Backyard
(High-speed camera)
im0   GT   im1 		Basketball
(High-speed camera)
im0   GT   im1 		Dumptruck
(High-speed camera)
im0   GT   im1 		Evergreen
(High-speed camera)
im0   GT   im1
rank all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext
PMMST [114]6.7 5.00 3 9.68 12 2.00 2 6.88 22 11.0 10 2.08 1 5.69 4 9.00 2 1.73 1 8.21 4 12.0 4 5.07 2 17.4 3 23.4 3 5.07 10 9.29 6 22.6 3 3.74 20 8.66 9 37.1 11 2.45 1 13.9 13 21.3 13 2.16 1
MDP-Flow2 [68]8.9 4.97 2 9.42 7 2.00 2 6.68 9 11.0 10 2.08 1 5.69 4 9.04 7 1.73 1 8.19 1 12.0 4 5.10 22 17.5 5 23.5 6 5.07 10 9.95 32 24.7 32 3.74 20 8.60 6 36.4 3 2.45 1 13.9 13 21.5 15 2.16 1
NNF-Local [87]17.7 5.07 11 10.1 25 2.00 2 6.40 1 10.0 3 2.08 1 5.69 4 9.00 2 1.73 1 8.66 35 14.5 77 5.10 22 17.6 8 23.8 15 5.07 10 10.4 66 25.8 62 3.74 20 8.66 9 37.5 17 2.45 1 13.9 13 21.6 18 2.16 1
PH-Flow [101]18.5 5.20 38 10.7 55 2.00 2 6.45 3 10.3 5 2.08 1 5.69 4 9.38 13 1.73 1 8.19 1 11.9 1 5.07 2 17.7 21 24.0 26 5.03 6 10.6 80 26.5 81 3.70 1 8.68 12 38.8 47 2.45 1 14.0 20 21.7 22 2.16 1
NN-field [71]18.7 5.07 11 10.4 41 2.00 2 6.45 3 10.0 3 2.08 1 5.97 49 9.00 2 1.73 1 8.76 48 15.0 87 5.10 22 17.6 8 23.7 11 5.07 10 10.1 44 25.0 40 3.74 20 8.54 4 36.9 8 2.45 1 13.9 13 21.6 18 2.16 1
NNF-EAC [103]20.9 5.35 73 10.0 21 2.08 59 7.05 32 11.6 23 2.08 1 6.00 50 9.35 10 1.73 1 8.35 7 12.4 9 5.23 77 17.7 21 23.9 23 5.07 10 9.47 7 22.9 4 3.70 1 8.83 23 37.0 10 2.45 1 14.0 20 21.6 18 2.16 1
IROF++ [58]22.8 5.23 57 10.8 64 2.00 2 6.88 22 11.5 21 2.08 1 6.00 50 10.0 28 1.73 1 8.19 1 11.9 1 5.07 2 17.9 41 24.4 49 5.10 29 9.49 9 24.2 24 3.74 20 9.09 53 37.2 14 2.45 1 14.0 20 22.1 36 2.16 1
SepConv-v1 [127]23.1 3.87 1 8.50 1 1.73 1 7.05 32 11.4 17 2.16 61 3.46 1 6.56 1 2.00 78 8.58 30 12.6 17 5.26 92 17.5 5 23.6 7 4.97 2 8.35 1 22.4 2 3.70 1 8.08 2 33.3 1 2.52 96 12.8 1 19.1 1 2.38 104
DF-Auto [115]25.9 5.03 8 8.87 2 2.16 85 7.72 63 13.1 59 2.38 93 5.69 4 9.20 9 1.73 1 8.68 37 12.5 13 5.10 22 17.4 3 23.4 3 5.16 54 9.47 7 24.0 17 3.74 20 8.98 37 38.4 37 2.45 1 14.0 20 21.8 26 2.16 1
DeepFlow2 [108]26.5 5.07 11 9.85 17 2.08 59 7.53 58 13.1 59 2.16 61 5.69 4 10.0 28 1.73 1 8.83 65 13.4 45 5.10 22 17.6 8 23.7 11 5.20 61 9.24 4 23.0 5 3.74 20 9.00 40 37.9 27 2.45 1 13.9 13 21.5 15 2.16 1
COFM [59]28.2 5.07 11 10.7 55 2.00 2 6.86 21 11.4 17 2.08 1 5.69 4 9.75 20 1.73 1 8.35 7 12.5 13 5.07 2 18.1 63 24.7 67 5.03 6 11.0 100 27.5 104 3.70 1 8.06 1 39.1 51 2.45 1 14.4 64 22.7 63 2.16 1
WLIF-Flow [93]28.8 5.10 29 10.2 31 2.00 2 7.00 31 11.9 34 2.08 1 5.69 4 9.68 16 1.73 1 8.29 5 12.2 6 5.23 77 17.8 31 24.0 26 5.10 29 10.6 80 26.6 85 3.83 95 8.83 23 37.5 17 2.45 1 14.1 33 21.9 33 2.16 1
LME [70]29.5 5.07 11 10.1 25 2.00 2 7.05 32 12.0 37 2.16 61 5.69 4 10.7 77 1.73 1 8.35 7 12.8 26 5.10 22 18.0 50 24.4 49 5.29 123 10.2 52 25.3 49 3.74 20 8.70 13 36.4 3 2.45 1 14.0 20 21.7 22 2.16 1
DeepFlow [86]29.7 5.07 11 9.63 11 2.08 59 7.44 54 13.0 53 2.16 61 5.74 42 10.0 28 1.73 1 8.96 79 13.0 29 5.20 51 17.6 8 23.8 15 5.20 61 9.15 3 23.2 7 3.87 103 8.81 20 35.6 2 2.45 1 13.7 5 21.1 7 2.16 1
Layers++ [37]30.0 5.10 29 10.1 25 2.08 59 6.45 3 9.88 1 2.08 1 5.69 4 10.0 28 1.73 1 8.37 14 12.7 22 5.10 22 18.1 63 24.9 84 5.10 29 10.7 88 28.3 115 3.74 20 8.76 14 38.0 31 2.45 1 14.1 33 21.9 33 2.16 1
ProbFlowFields [128]30.2 5.03 8 10.7 55 2.00 2 6.68 9 11.3 14 2.08 1 5.69 4 9.47 14 1.73 1 8.52 27 13.3 44 5.20 51 18.2 83 24.9 84 5.23 106 10.5 71 26.2 76 3.74 20 8.60 6 37.7 22 2.45 1 13.8 8 21.6 18 2.16 1
SuperFlow [81]30.4 5.00 3 9.35 4 2.16 85 7.85 68 13.1 59 2.38 93 6.00 50 9.47 14 2.00 78 8.70 41 12.7 22 5.20 51 17.6 8 23.7 11 5.20 61 9.27 5 23.9 15 3.70 1 8.81 20 37.6 20 2.45 1 13.8 8 21.2 10 2.16 1
CombBMOF [113]30.7 5.35 73 10.5 47 2.00 2 6.83 19 11.4 17 2.08 1 5.80 45 10.0 28 1.73 1 8.83 65 14.4 72 5.10 22 17.9 41 24.3 43 5.07 10 9.88 26 24.1 20 3.70 1 10.7 114 38.3 34 2.45 1 14.0 20 21.9 33 2.16 1
nLayers [57]30.8 5.16 36 10.5 47 2.00 2 6.66 8 10.9 9 2.08 1 5.69 4 9.00 2 1.73 1 8.49 26 13.0 29 5.10 22 18.3 93 25.2 103 5.20 61 10.4 66 25.6 56 3.74 20 8.66 9 38.5 41 2.45 1 14.2 48 22.4 53 2.16 1
Aniso. Huber-L1 [22]31.7 5.26 59 10.0 21 2.08 59 8.81 91 14.5 94 2.16 61 6.00 50 9.75 20 1.73 1 8.72 44 13.0 29 5.16 43 17.6 8 23.8 15 5.10 29 9.87 25 23.2 7 3.70 1 9.26 65 37.8 24 2.45 1 13.8 8 21.0 5 2.16 1
IROF-TV [53]31.9 5.20 38 10.7 55 2.08 59 7.05 32 11.9 34 2.08 1 6.00 50 10.3 55 1.73 1 8.37 14 12.6 17 5.16 43 17.8 31 24.1 34 5.23 106 10.1 44 25.0 40 3.70 1 9.04 47 39.1 51 2.45 1 13.7 5 21.0 5 2.16 1
Brox et al. [5]32.2 5.20 38 9.83 14 2.00 2 7.62 62 12.6 44 2.16 61 6.00 50 10.2 51 2.00 78 8.76 48 12.6 17 5.07 2 17.5 5 23.6 7 5.16 54 10.1 44 25.3 49 3.74 20 9.00 40 40.1 64 2.45 1 13.8 8 21.3 13 2.16 1
FMOF [94]32.3 5.42 90 11.0 71 2.00 2 6.76 14 11.0 10 2.08 1 6.00 50 10.3 55 1.73 1 8.83 65 14.1 65 5.10 22 17.8 31 24.1 34 5.07 10 10.0 42 25.6 56 3.74 20 8.58 5 37.7 22 2.45 1 14.3 55 22.4 53 2.16 1
Sparse-NonSparse [56]32.6 5.20 38 10.7 55 2.00 2 6.78 15 11.6 23 2.08 1 5.69 4 10.0 28 1.73 1 8.43 22 12.5 13 5.07 2 18.1 63 24.7 67 5.10 29 10.5 71 26.7 88 3.74 20 8.76 14 42.1 91 2.45 1 14.3 55 23.0 78 2.16 1
TV-L1-MCT [64]32.9 5.48 103 11.4 94 2.00 2 7.35 43 13.1 59 2.08 1 5.48 2 10.3 55 1.73 1 8.35 7 12.4 9 5.07 2 18.3 93 25.3 107 5.10 29 9.49 9 23.5 10 3.79 76 8.81 20 39.2 54 2.45 1 13.7 5 21.1 7 2.16 1
FlowFields [110]33.1 5.10 29 11.1 81 2.00 2 6.88 22 11.5 21 2.08 1 5.69 4 10.0 28 1.73 1 8.76 48 14.9 83 5.20 51 18.0 50 24.4 49 5.16 54 10.3 59 25.8 62 3.74 20 8.76 14 37.8 24 2.45 1 14.1 33 22.5 56 2.16 1
ComponentFusion [96]34.2 5.07 11 11.2 87 2.00 2 6.81 18 11.6 23 2.08 1 5.72 41 9.81 24 1.73 1 8.37 14 13.2 41 5.07 2 18.1 63 24.7 67 5.10 29 9.90 27 24.9 38 3.74 20 9.20 62 44.1 109 2.45 1 14.2 48 23.3 90 2.16 1
MDP-Flow [26]34.4 5.03 8 9.95 19 2.00 2 6.68 9 11.3 14 2.08 1 5.69 4 9.04 7 1.73 1 8.89 74 13.7 51 5.20 51 17.8 31 24.2 41 5.20 61 11.3 112 27.9 110 3.74 20 9.27 69 39.3 56 2.45 1 14.1 33 22.3 49 2.16 1
CLG-TV [48]36.5 5.20 38 9.49 8 2.08 59 8.43 82 14.3 88 2.16 61 6.00 50 10.1 47 2.00 78 8.76 48 13.1 37 5.20 51 17.6 8 23.8 15 5.10 29 9.59 16 23.1 6 3.74 20 9.20 62 38.4 37 2.45 1 14.0 20 21.5 15 2.16 1
2DHMM-SAS [92]36.5 5.42 90 11.2 87 2.00 2 7.90 70 13.7 72 2.08 1 5.60 3 9.85 25 1.73 1 8.35 7 12.2 6 5.10 22 18.0 50 24.6 65 5.10 29 9.93 30 25.7 59 3.74 20 8.96 32 39.8 62 2.45 1 14.4 64 23.0 78 2.16 1
PGM-C [120]37.1 5.07 11 10.9 69 2.00 2 6.93 25 11.6 23 2.08 1 6.00 50 10.3 55 1.73 1 8.76 48 15.2 89 5.16 43 18.0 50 24.7 67 5.20 61 9.97 37 24.8 36 3.74 20 9.00 40 40.1 64 2.45 1 14.1 33 22.7 63 2.16 1
FlowFields+ [130]37.3 5.10 29 11.1 81 2.00 2 6.78 15 11.3 14 2.08 1 5.69 4 10.0 28 1.73 1 8.70 41 14.9 83 5.16 43 18.2 83 24.9 84 5.20 61 10.4 66 26.3 79 3.74 20 8.79 18 38.6 45 2.45 1 14.1 33 22.7 63 2.16 1
ALD-Flow [66]38.2 5.20 38 10.7 55 2.08 59 7.35 43 12.9 49 2.16 61 6.00 50 10.1 47 1.73 1 8.39 19 13.0 29 5.16 43 17.9 41 24.3 43 5.20 61 9.56 13 23.5 10 3.79 76 8.79 18 36.8 7 2.45 1 14.5 75 23.0 78 2.16 1
CPM-Flow [116]38.3 5.07 11 10.9 69 2.00 2 6.95 27 11.6 23 2.08 1 5.80 45 10.0 28 1.73 1 9.00 82 15.9 104 5.20 51 18.1 63 24.7 67 5.20 61 9.81 20 24.3 25 3.79 76 9.26 65 38.3 34 2.45 1 14.0 20 22.2 42 2.16 1
HAST [109]39.1 5.07 11 10.5 47 2.00 2 6.68 9 10.7 8 2.08 1 6.00 50 10.3 55 1.73 1 8.29 5 12.4 9 5.00 1 18.4 103 25.3 107 5.03 6 11.0 100 30.7 124 3.70 1 8.60 6 41.8 86 2.45 1 14.9 101 23.9 103 2.16 1
Second-order prior [8]39.1 5.20 38 9.83 14 2.08 59 8.43 82 14.5 94 2.08 1 6.35 93 11.0 94 2.00 78 8.83 65 13.8 58 5.07 2 17.7 21 23.8 15 5.07 10 9.70 18 24.1 20 3.74 20 9.33 72 38.4 37 2.45 1 14.0 20 21.8 26 2.16 1
CBF [12]39.7 5.00 3 9.40 6 2.08 59 7.77 66 13.0 53 2.16 61 6.00 50 9.68 16 1.73 1 8.68 37 12.5 13 5.35 107 17.6 8 23.4 3 5.20 61 9.85 24 24.3 25 3.74 20 9.11 56 39.3 56 2.52 96 14.0 20 21.1 7 2.38 104
S2F-IF [123]39.8 5.10 29 11.6 103 2.00 2 6.78 15 11.4 17 2.08 1 5.69 4 10.3 55 1.73 1 8.74 45 15.2 89 5.07 2 18.3 93 25.1 99 5.20 61 10.5 71 26.1 71 3.74 20 9.02 45 38.5 41 2.45 1 14.1 33 22.6 57 2.16 1
Local-TV-L1 [65]40.1 5.20 38 9.38 5 2.16 85 8.96 97 14.5 94 2.38 93 5.69 4 9.35 10 1.73 1 8.70 41 13.0 29 5.45 112 17.6 8 23.8 15 5.16 54 9.54 12 24.0 17 4.08 124 8.76 14 37.2 14 2.45 1 13.6 4 20.9 4 2.31 86
Ramp [62]40.4 5.29 66 10.8 64 2.00 2 6.83 19 11.6 23 2.08 1 5.69 4 10.1 47 1.73 1 8.35 7 12.2 6 5.07 2 18.1 63 24.7 67 5.10 29 10.9 95 27.8 109 3.79 76 8.83 23 43.0 102 2.45 1 14.5 75 23.2 86 2.16 1
SIOF [67]40.8 5.42 90 10.4 41 2.08 59 8.83 92 15.0 107 2.38 93 5.69 4 10.4 74 1.73 1 8.68 37 13.1 37 5.20 51 17.3 2 23.2 2 5.07 10 9.83 21 23.6 12 3.74 20 9.00 40 36.9 8 2.45 1 14.3 55 22.1 36 2.31 86
p-harmonic [29]41.1 5.07 11 9.98 20 2.00 2 8.68 88 14.4 90 2.16 61 6.00 50 10.7 77 1.91 74 9.20 90 13.7 51 5.20 51 17.8 31 24.0 26 5.10 29 9.90 27 23.7 13 3.74 20 9.61 91 38.5 41 2.45 1 14.0 20 21.7 22 2.16 1
LDOF [28]42.5 5.35 73 9.83 14 2.16 85 7.94 71 12.1 39 2.52 109 6.00 50 10.3 55 2.00 78 8.91 77 13.6 50 5.23 77 17.6 8 23.6 7 5.20 61 9.49 9 24.5 29 3.74 20 8.96 32 37.9 27 2.45 1 14.0 20 21.8 26 2.16 1
Kuang [131]42.9 5.20 38 11.2 87 2.00 2 7.16 39 12.0 37 2.08 1 6.00 50 10.7 77 1.73 1 8.83 65 15.3 93 5.20 51 18.1 63 24.5 56 5.20 61 10.1 44 25.4 52 3.70 1 9.56 88 39.5 60 2.45 1 14.0 20 22.2 42 2.16 1
DPOF [18]43.2 5.35 73 11.7 105 2.08 59 6.56 7 10.4 6 2.08 1 6.00 50 9.71 19 1.91 74 8.76 48 14.4 72 5.20 51 17.7 21 24.1 34 5.07 10 10.3 59 26.7 88 3.70 1 9.33 72 39.1 51 2.45 1 14.4 64 22.8 69 2.16 1
ComplOF-FED-GPU [35]43.4 5.20 38 11.1 81 2.00 2 7.19 40 12.6 44 2.08 1 6.35 93 10.0 28 2.00 78 8.68 37 14.0 64 5.10 22 17.9 41 24.5 56 5.10 29 9.97 37 25.1 43 3.74 20 9.40 77 38.8 47 2.45 1 14.5 75 23.2 86 2.16 1
OFLAF [77]44.0 5.07 11 10.6 52 2.00 2 6.48 6 10.5 7 2.08 1 5.69 4 10.0 28 1.73 1 8.37 14 12.6 17 5.07 2 18.4 103 25.4 113 5.20 61 10.9 95 27.4 102 3.74 20 9.59 90 44.9 113 2.45 1 15.1 106 24.1 105 2.16 1
AGIF+OF [85]44.1 5.42 90 11.1 81 2.00 2 6.98 28 11.8 31 2.08 1 5.69 4 10.0 28 1.73 1 8.43 22 12.8 26 5.07 2 18.5 110 25.2 103 5.20 61 10.8 93 27.6 105 3.74 20 8.98 37 37.9 27 2.45 1 14.7 91 23.4 94 2.16 1
LSM [39]44.5 5.35 73 11.5 99 2.00 2 6.98 28 11.9 34 2.08 1 5.80 45 10.7 77 1.73 1 8.58 30 13.4 45 5.07 2 18.1 63 24.9 84 5.10 29 10.6 80 27.1 96 3.74 20 8.83 23 42.2 93 2.45 1 14.4 64 23.0 78 2.16 1
FC-2Layers-FF [74]44.5 5.26 59 11.0 71 2.00 2 6.40 1 9.88 1 2.08 1 5.69 4 10.3 55 1.73 1 8.39 19 12.8 26 5.10 22 18.2 83 25.0 93 5.20 61 11.0 100 28.1 112 3.79 76 8.91 30 42.8 97 2.45 1 14.5 75 23.0 78 2.16 1
Classic+NL [31]44.9 5.35 73 11.0 71 2.08 59 6.98 28 11.7 29 2.08 1 5.69 4 10.2 51 1.73 1 8.43 22 12.4 9 5.20 51 18.1 63 24.8 77 5.10 29 10.6 80 26.8 91 3.79 76 8.83 23 42.9 98 2.45 1 14.4 64 22.9 76 2.16 1
RFlow [90]45.0 5.07 11 10.2 31 2.08 59 8.58 86 14.7 100 2.08 1 6.00 50 10.3 55 1.73 1 8.91 77 14.4 72 5.20 51 17.7 21 23.9 23 5.10 29 9.95 32 25.4 52 3.70 1 9.13 58 40.4 70 2.45 1 14.3 55 22.6 57 2.31 86
OAR-Flow [125]45.0 5.20 38 10.7 55 2.08 59 7.44 54 13.0 53 2.16 61 5.74 42 10.0 28 1.73 1 8.35 7 13.0 29 5.10 22 18.1 63 24.9 84 5.23 106 10.2 52 24.7 32 3.74 20 9.54 87 39.4 59 2.45 1 14.4 64 22.7 63 2.16 1
RNLOD-Flow [121]46.0 5.20 38 11.0 71 2.00 2 7.53 58 13.4 65 2.08 1 6.00 50 11.0 94 1.73 1 8.52 27 13.0 29 5.07 2 18.2 83 25.0 93 5.10 29 10.6 80 26.9 93 3.74 20 8.96 32 38.4 37 2.45 1 14.9 101 23.5 96 2.16 1
TF+OM [100]46.2 5.00 3 10.2 31 2.08 59 6.93 25 11.7 29 2.16 61 5.69 4 10.5 75 1.73 1 8.81 60 14.6 78 5.20 51 18.0 50 24.4 49 5.20 61 9.95 32 26.1 71 3.79 76 9.09 53 41.0 76 2.45 1 14.1 33 21.8 26 2.38 104
TC/T-Flow [76]46.7 5.45 98 11.5 99 2.00 2 7.42 51 13.0 53 2.08 1 5.69 4 9.76 22 1.73 1 8.60 33 13.7 51 5.16 43 18.3 93 24.9 84 5.20 61 10.1 44 24.9 38 3.74 20 9.75 94 42.6 94 2.45 1 14.5 75 22.6 57 2.16 1
F-TV-L1 [15]46.9 5.35 73 10.3 38 2.16 85 8.83 92 14.6 99 2.16 61 6.00 50 10.3 55 2.00 78 8.76 48 13.2 41 5.26 92 17.6 8 23.8 15 5.03 6 9.57 15 23.2 7 3.79 76 9.18 61 37.6 20 2.45 1 13.8 8 21.2 10 2.31 86
EpicFlow [102]47.6 5.07 11 11.0 71 2.00 2 7.39 47 12.9 49 2.08 1 5.80 45 10.3 55 1.73 1 8.85 72 15.5 98 5.20 51 18.1 63 24.8 77 5.20 61 10.2 52 25.1 43 3.74 20 9.33 72 40.4 70 2.45 1 14.5 75 24.1 105 2.16 1
TC-Flow [46]48.5 5.07 11 10.8 64 2.00 2 7.39 47 13.2 64 2.16 61 6.00 50 10.3 55 1.73 1 8.66 35 13.7 51 5.23 77 18.2 83 25.0 93 5.20 61 10.2 52 24.5 29 3.79 76 9.04 47 38.1 32 2.45 1 14.5 75 23.5 96 2.16 1
S2D-Matching [84]48.6 5.35 73 11.2 87 2.00 2 7.75 65 13.5 68 2.08 1 5.69 4 10.0 28 1.73 1 8.37 14 12.6 17 5.20 51 18.3 93 25.2 103 5.07 10 11.0 100 27.7 108 3.79 76 9.09 53 40.3 68 2.45 1 14.4 64 23.0 78 2.16 1
Fusion [6]50.8 5.20 38 10.4 41 2.00 2 7.14 38 11.8 31 2.08 1 5.74 42 9.68 16 1.73 1 9.33 93 14.2 66 5.20 51 18.3 93 24.7 67 5.07 10 11.6 116 28.1 112 3.70 1 9.63 92 41.4 81 2.45 1 15.3 117 24.2 108 2.16 1
Modified CLG [34]51.0 5.07 11 9.49 8 2.16 85 9.42 110 14.2 86 2.65 113 6.00 50 11.5 103 2.00 78 9.15 87 14.3 68 5.10 22 17.7 21 23.9 23 5.10 29 10.1 44 24.7 32 3.74 20 9.31 71 37.5 17 2.45 1 14.1 33 21.8 26 2.31 86
Classic++ [32]51.6 5.20 38 10.3 38 2.08 59 7.94 71 13.8 74 2.08 1 6.00 50 10.1 47 1.73 1 8.89 74 13.7 51 5.23 77 18.0 50 24.5 56 5.10 29 10.3 59 25.8 62 3.87 103 9.13 58 40.1 64 2.45 1 14.2 48 22.2 42 2.31 86
Sparse Occlusion [54]52.9 5.26 59 10.5 47 2.08 59 8.04 73 14.4 90 2.08 1 6.00 50 10.0 28 1.73 1 8.83 65 13.7 51 5.20 51 18.1 63 24.7 67 5.20 61 11.0 100 26.5 81 3.74 20 9.42 78 42.0 90 2.45 1 14.4 64 22.8 69 2.16 1
AggregFlow [97]53.0 5.45 98 13.8 121 2.08 59 7.44 54 13.1 59 2.16 61 5.69 4 9.95 27 1.73 1 9.15 87 16.1 105 5.10 22 18.0 50 24.5 56 5.20 61 9.90 27 24.6 31 3.83 95 8.98 37 40.7 73 2.45 1 14.4 64 23.0 78 2.16 1
FESL [72]53.9 5.42 90 11.0 71 2.00 2 7.05 32 11.8 31 2.08 1 5.69 4 10.7 77 1.73 1 8.81 60 13.5 48 5.20 51 18.4 103 25.1 99 5.20 61 11.0 100 27.0 95 3.74 20 9.06 51 42.9 98 2.45 1 14.8 96 23.7 100 2.16 1
BlockOverlap [61]54.1 5.20 38 9.29 3 2.16 85 8.74 90 14.1 81 2.65 113 6.00 50 9.35 10 2.00 78 8.52 27 11.9 1 5.60 119 17.8 31 24.0 26 5.32 125 9.83 21 25.0 40 4.04 120 8.83 23 37.1 11 2.52 96 13.5 3 20.6 3 2.38 104
PMF [73]54.2 5.20 38 11.4 94 2.00 2 7.35 43 12.4 42 2.08 1 6.00 50 12.0 110 1.73 1 8.76 48 14.4 72 5.07 2 18.4 103 25.0 93 5.10 29 10.2 52 25.8 62 3.87 103 9.04 47 41.3 80 2.45 1 15.2 115 24.5 112 2.16 1
Classic+CPF [83]54.6 5.35 73 11.3 92 2.00 2 7.07 37 12.1 39 2.08 1 5.69 4 10.5 75 1.73 1 8.43 22 12.7 22 5.07 2 18.7 119 25.7 122 5.20 61 11.2 109 28.7 118 3.74 20 9.42 78 42.9 98 2.45 1 15.1 106 24.2 108 2.16 1
TCOF [69]55.2 5.35 73 10.7 55 2.00 2 9.27 104 15.4 114 2.16 61 5.69 4 10.2 51 1.73 1 8.74 45 13.1 37 5.23 77 17.7 21 23.8 15 5.07 10 10.7 88 26.6 85 3.70 1 10.0 102 44.7 112 2.45 1 14.6 86 22.9 76 2.38 104
FlowNetS+ft+v [112]55.5 5.26 59 10.1 25 2.16 85 9.11 101 14.5 94 2.45 104 6.00 50 10.3 55 2.00 78 8.96 79 13.5 48 5.26 92 17.8 31 24.1 34 5.23 106 9.76 19 23.9 15 3.74 20 9.38 76 41.6 84 2.45 1 14.1 33 22.2 42 2.16 1
OFH [38]56.2 5.35 73 11.0 71 2.08 59 8.06 76 13.7 72 2.08 1 6.00 50 11.6 104 1.73 1 8.58 30 13.9 62 5.07 2 18.2 83 24.9 84 5.16 54 10.3 59 25.1 43 3.74 20 9.88 98 42.7 96 2.45 1 14.8 96 24.7 113 2.16 1
SVFilterOh [111]56.2 5.20 38 10.6 52 2.00 2 6.73 13 11.0 10 2.08 1 6.00 50 10.0 28 1.73 1 8.76 48 13.8 58 5.26 92 18.4 103 25.3 107 5.26 116 10.6 80 28.0 111 3.74 20 8.45 3 39.2 54 2.52 96 14.7 91 23.3 90 2.31 86
CRTflow [80]56.9 5.29 66 10.5 47 2.16 85 8.43 82 14.5 94 2.16 61 6.35 93 11.1 100 2.00 78 8.64 34 13.0 29 5.29 101 18.0 50 24.5 56 5.20 61 9.68 17 23.8 14 3.74 20 9.00 40 40.9 75 2.45 1 14.1 33 22.2 42 2.31 86
EPPM w/o HM [88]57.5 5.23 57 12.6 113 2.00 2 7.39 47 13.0 53 2.08 1 6.35 93 14.0 124 1.91 74 8.83 65 15.3 93 5.10 22 18.0 50 24.5 56 5.10 29 10.5 71 27.6 105 3.74 20 9.11 56 41.9 87 2.45 1 14.5 75 23.2 86 2.16 1
SRR-TVOF-NL [91]57.5 5.45 98 12.1 111 2.08 59 7.77 66 13.5 68 2.16 61 6.00 50 10.3 55 1.73 1 9.26 91 14.7 80 5.07 2 18.1 63 24.6 65 5.10 29 10.4 66 26.6 85 3.70 1 9.42 78 38.5 41 2.45 1 15.1 106 23.9 103 2.16 1
Efficient-NL [60]57.6 5.35 73 10.7 55 2.00 2 7.42 51 13.0 53 2.08 1 6.35 93 10.7 77 2.00 78 8.81 60 13.4 45 5.10 22 18.1 63 24.7 67 5.10 29 11.2 109 27.6 105 3.70 1 9.47 82 43.6 107 2.45 1 15.1 106 23.8 102 2.16 1
2D-CLG [1]58.0 5.16 36 10.0 21 2.16 85 9.90 115 14.2 86 2.83 120 6.35 93 10.7 77 2.00 78 10.0 107 15.2 89 5.10 22 17.7 21 24.1 34 5.20 61 10.1 44 24.1 20 3.74 20 9.81 95 43.6 107 2.45 1 14.1 33 21.8 26 2.16 1
MLDP_OF [89]58.2 5.32 70 11.1 81 2.00 2 7.55 61 13.6 70 2.08 1 5.69 4 10.0 28 1.73 1 8.76 48 13.1 37 5.26 92 18.0 50 24.5 56 5.20 61 11.0 100 26.9 93 4.08 124 9.26 65 38.2 33 2.52 96 14.4 64 22.6 57 2.38 104
Steered-L1 [118]58.4 5.07 11 9.81 13 2.00 2 7.35 43 12.8 48 2.16 61 6.35 93 10.3 55 2.00 78 9.31 92 14.3 68 5.35 107 18.2 83 24.7 67 5.07 10 10.2 52 25.7 59 3.79 76 9.33 72 40.4 70 2.45 1 14.6 86 22.8 69 2.31 86
Occlusion-TV-L1 [63]60.0 5.20 38 10.2 31 2.08 59 8.89 94 15.3 112 2.16 61 6.00 50 10.3 55 2.00 78 9.15 87 15.4 95 5.26 92 17.6 8 23.7 11 5.10 29 9.98 39 25.5 55 3.87 103 10.3 107 39.3 56 2.52 96 14.1 33 22.3 49 2.16 1
IAOF [50]60.2 5.60 108 11.0 71 2.16 85 12.0 129 16.9 130 2.52 109 5.69 4 11.0 94 2.00 78 9.76 104 14.3 68 5.20 51 17.7 21 24.0 26 5.07 10 10.0 42 25.2 47 3.74 20 9.47 82 41.4 81 2.45 1 14.2 48 22.1 36 2.16 1
Complementary OF [21]62.0 5.20 38 12.0 109 2.00 2 7.19 40 12.9 49 2.08 1 6.68 110 10.8 90 2.00 78 8.76 48 14.6 78 5.16 43 18.2 83 25.2 103 5.10 29 10.3 59 25.9 66 3.74 20 9.97 101 42.6 94 2.45 1 15.6 120 28.0 125 2.16 1
Ad-TV-NDC [36]62.4 5.66 110 9.88 18 2.52 124 10.1 118 15.1 108 2.71 116 6.00 50 10.7 77 1.73 1 9.49 100 14.2 66 5.35 107 17.7 21 24.0 26 5.20 61 9.56 13 24.0 17 3.87 103 9.56 88 38.6 45 2.45 1 13.9 13 21.2 10 2.38 104
Adaptive [20]63.0 5.32 70 10.3 38 2.16 85 9.29 107 15.4 114 2.16 61 6.00 50 10.7 77 1.73 1 8.81 60 13.8 58 5.20 51 17.9 41 24.3 43 5.07 10 10.4 66 26.0 67 3.79 76 9.83 96 44.6 110 2.45 1 14.5 75 22.8 69 2.31 86
Black & Anandan [4]64.4 5.45 98 10.1 25 2.16 85 10.2 121 15.3 112 2.45 104 6.68 110 11.3 101 2.00 78 10.2 108 15.6 99 5.20 51 17.8 31 24.0 26 5.16 54 9.83 21 24.7 32 3.74 20 10.2 106 41.9 87 2.45 1 14.2 48 21.8 26 2.16 1
CostFilter [40]64.5 5.32 70 13.2 118 2.00 2 7.33 42 12.3 41 2.08 1 6.06 89 13.5 123 1.73 1 8.96 79 16.1 105 5.07 2 18.6 115 25.6 121 5.16 54 9.98 39 24.8 36 4.04 120 9.20 62 43.5 106 2.45 1 15.1 106 24.9 115 2.16 1
HBM-GC [105]64.6 5.35 73 10.6 52 2.16 85 7.42 51 13.4 65 2.16 61 5.69 4 9.00 2 1.73 1 8.74 45 13.2 41 5.26 92 18.6 115 25.5 118 5.26 116 11.8 122 31.5 127 3.83 95 8.83 23 41.1 78 2.45 1 14.3 55 22.2 42 2.31 86
CNN-flow-warp+ref [117]64.6 5.00 3 9.59 10 2.16 85 8.35 80 13.6 70 2.16 61 6.35 93 11.8 109 2.00 78 10.6 113 15.4 95 5.48 116 17.8 31 24.3 43 5.23 106 9.95 32 24.3 25 3.83 95 9.83 96 44.6 110 2.45 1 14.2 48 22.3 49 2.16 1
BriefMatch [124]65.3 5.29 66 11.4 94 2.08 59 7.44 54 12.7 46 2.16 61 6.38 108 9.93 26 2.00 78 9.83 106 14.9 83 5.83 126 18.0 50 24.4 49 5.20 61 10.5 71 27.3 100 4.32 129 9.04 47 37.9 27 2.45 1 14.3 55 22.8 69 2.16 1
AdaConv-v1 [126]66.9 6.24 121 14.4 122 2.38 118 9.02 98 12.7 46 3.11 125 7.00 118 11.0 94 2.38 124 13.1 126 18.8 119 5.83 126 16.8 1 22.5 1 4.83 1 8.79 2 22.0 1 3.70 1 8.91 30 36.6 6 2.58 118 13.3 2 20.2 2 2.38 104
HBpMotionGpu [43]67.1 5.48 103 10.8 64 2.38 118 10.1 118 15.4 114 2.71 116 5.69 4 10.0 28 1.73 1 9.40 95 16.2 109 5.23 77 17.9 41 24.3 43 5.20 61 10.5 71 26.4 80 3.83 95 8.96 32 37.8 24 2.45 1 14.3 55 22.6 57 2.38 104
TriFlow [95]68.5 5.26 59 12.0 109 2.16 85 8.39 81 14.4 90 2.38 93 6.00 50 11.0 94 1.73 1 9.02 83 15.4 95 5.10 22 18.5 110 25.4 113 5.20 61 10.6 80 27.3 100 3.74 20 9.26 65 39.7 61 2.45 1 14.6 86 23.1 85 2.16 1
Nguyen [33]68.6 5.42 90 10.0 21 2.38 118 10.9 124 15.1 108 2.65 113 6.00 50 12.0 110 2.00 78 10.4 112 16.1 105 5.20 51 17.8 31 24.1 34 5.07 10 9.98 39 25.3 49 3.70 1 10.9 118 46.9 119 2.52 96 14.1 33 22.1 36 2.16 1
Aniso-Texture [82]69.9 5.07 11 10.2 31 2.00 2 8.89 94 15.2 111 2.16 61 6.35 93 10.3 55 1.73 1 9.04 84 16.1 105 5.29 101 18.3 93 24.9 84 5.23 106 11.8 122 30.0 122 3.83 95 9.06 51 40.2 67 2.45 1 14.7 91 23.5 96 2.16 1
TV-L1-improved [17]72.2 5.10 29 10.2 31 2.08 59 9.20 103 15.4 114 2.16 61 6.35 93 10.3 55 2.00 78 8.85 72 13.8 58 5.23 77 18.0 50 24.4 49 5.10 29 10.6 80 26.5 81 3.79 76 9.93 100 46.9 119 2.52 96 14.3 55 22.7 63 2.38 104
FlowNet2 [122]72.2 6.45 125 19.1 129 2.16 85 7.85 68 13.4 65 2.38 93 6.06 89 11.7 105 1.73 1 9.40 95 18.2 115 5.23 77 18.5 110 25.3 107 5.20 61 10.3 59 25.2 47 3.74 20 9.27 69 41.9 87 2.45 1 14.3 55 22.8 69 2.16 1
Bartels [41]73.3 5.35 73 11.4 94 2.16 85 7.72 63 14.0 80 2.38 93 6.00 50 10.3 55 2.00 78 9.11 86 15.0 87 5.69 121 17.6 8 23.6 7 5.45 129 10.7 88 27.2 97 4.55 131 8.96 32 36.4 3 2.65 127 14.1 33 22.1 36 2.38 104
GraphCuts [14]73.5 5.66 110 11.9 108 2.16 85 7.53 58 12.5 43 2.38 93 7.68 124 10.2 51 2.00 78 9.47 99 14.9 83 5.23 77 18.1 63 24.5 56 5.00 3 10.1 44 25.7 59 3.70 1 9.02 45 42.1 91 2.52 96 15.1 106 24.1 105 2.31 86
SimpleFlow [49]74.0 5.35 73 11.0 71 2.00 2 8.04 73 13.9 77 2.08 1 6.56 109 11.3 101 2.00 78 8.41 21 12.7 22 5.20 51 18.4 103 25.4 113 5.20 61 11.4 114 28.9 119 3.74 20 10.1 104 53.7 128 2.52 96 15.3 117 26.5 121 2.16 1
Filter Flow [19]76.5 5.42 90 10.2 31 2.16 85 9.40 109 14.7 100 2.71 116 6.00 50 10.7 77 2.00 78 9.49 100 13.9 62 5.35 107 18.1 63 24.3 43 5.26 116 10.2 52 25.6 56 3.83 95 9.52 86 41.4 81 2.45 1 14.6 86 22.3 49 2.38 104
ROF-ND [107]77.1 5.74 113 10.4 41 2.00 2 8.04 73 14.1 81 2.16 61 6.06 89 10.7 77 1.73 1 10.6 113 19.9 124 5.26 92 18.1 63 24.8 77 5.20 61 11.7 119 28.6 116 3.74 20 11.1 120 41.0 76 2.52 96 15.3 117 25.3 117 2.16 1
Shiralkar [42]78.5 5.48 103 12.7 114 2.08 59 9.06 100 14.7 100 2.08 1 6.00 50 12.8 118 2.00 78 10.7 115 19.7 123 5.20 51 18.1 63 24.8 77 5.00 3 10.8 93 26.1 71 3.87 103 10.8 117 47.5 123 2.45 1 14.9 101 25.8 119 2.16 1
TriangleFlow [30]79.1 5.60 108 11.6 103 2.16 85 8.50 85 14.4 90 2.08 1 6.35 93 10.7 77 2.00 78 9.42 97 15.8 101 5.23 77 18.0 50 24.5 56 5.00 3 11.1 108 27.2 97 3.74 20 10.4 108 47.2 122 2.52 96 15.6 120 26.7 122 2.16 1
Correlation Flow [75]79.6 5.42 90 11.7 105 2.00 2 8.58 86 15.4 114 2.08 1 5.69 4 9.80 23 1.73 1 8.89 74 14.7 80 5.32 105 18.1 63 24.8 77 5.32 125 12.3 129 30.3 123 3.83 95 10.5 111 48.8 125 2.52 96 14.8 96 23.7 100 2.31 86
Rannacher [23]79.8 5.26 59 10.8 64 2.16 85 9.27 104 15.5 121 2.16 61 6.35 93 10.9 92 2.00 78 8.76 48 14.4 72 5.23 77 17.9 41 24.4 49 5.20 61 10.5 71 26.7 88 3.79 76 9.90 99 45.9 116 2.52 96 14.4 64 23.5 96 2.38 104
Horn & Schunck [3]82.2 5.48 103 10.4 41 2.16 85 10.5 123 15.4 114 2.52 109 6.68 110 12.0 110 2.00 78 11.5 121 17.6 114 5.23 77 17.9 41 24.0 26 5.20 61 9.93 30 24.1 20 3.79 76 11.1 120 42.9 98 2.52 96 14.5 75 22.2 42 2.38 104
IAOF2 [51]83.2 5.74 113 11.5 99 2.16 85 9.49 111 15.9 128 2.38 93 5.69 4 11.0 94 2.00 78 9.61 103 15.8 101 5.26 92 18.7 119 25.3 107 5.20 61 10.9 95 27.4 102 3.74 20 9.47 82 41.1 78 2.45 1 14.5 75 22.8 69 2.31 86
TI-DOFE [24]85.0 5.80 115 11.0 71 2.52 124 11.5 127 15.8 126 3.11 125 6.35 93 12.3 114 2.00 78 11.4 120 17.4 112 5.29 101 17.9 41 24.2 41 5.07 10 9.95 32 24.4 28 3.79 76 10.5 111 39.9 63 2.52 96 14.8 96 22.1 36 2.38 104
LocallyOriented [52]86.5 5.45 98 11.2 87 2.16 85 9.49 111 15.7 124 2.16 61 6.06 89 11.7 105 1.91 74 9.42 97 17.0 110 5.23 77 18.2 83 24.8 77 5.07 10 11.0 100 26.5 81 4.04 120 10.4 108 43.0 102 2.45 1 14.8 96 23.4 94 2.31 86
SegOF [10]87.0 5.10 29 11.4 94 2.16 85 8.29 78 13.9 77 2.38 93 7.00 118 12.1 113 2.00 78 9.81 105 21.0 125 5.20 51 18.2 83 25.1 99 5.20 61 10.9 95 26.1 71 3.79 76 10.4 108 48.4 124 2.58 118 14.7 91 25.1 116 2.16 1
SPSA-learn [13]87.6 5.29 66 10.4 41 2.16 85 9.04 99 14.1 81 2.45 104 6.68 110 11.7 105 2.00 78 10.3 111 15.8 101 5.10 22 18.4 103 25.3 107 5.20 61 10.5 71 26.8 91 3.74 20 12.3 128 58.4 130 2.71 130 17.6 128 35.0 130 2.16 1
2bit-BM-tele [98]89.8 5.35 73 10.1 25 2.16 85 8.91 96 15.4 114 2.45 104 6.00 50 10.0 28 2.00 78 9.04 84 14.3 68 5.60 119 18.3 93 24.9 84 5.35 128 11.7 119 31.3 126 4.24 128 12.0 127 58.7 131 2.83 131 13.9 13 21.7 22 2.45 129
StereoOF-V1MT [119]90.3 5.69 112 13.0 117 2.08 59 8.68 88 14.1 81 2.08 1 6.73 117 12.4 116 2.00 78 11.6 122 19.1 122 5.45 112 18.5 110 25.4 113 5.20 61 11.3 112 26.1 71 3.92 116 11.2 122 44.9 113 2.58 118 14.2 48 22.6 57 2.16 1
StereoFlow [44]90.6 8.68 131 20.4 131 2.45 122 10.3 122 16.1 129 2.71 116 6.00 50 10.7 77 1.73 1 8.81 60 13.7 51 5.16 43 22.6 129 31.6 129 5.26 116 14.3 131 35.7 131 3.79 76 9.13 58 38.8 47 2.45 1 15.6 120 25.3 117 2.31 86
ACK-Prior [27]91.5 5.35 73 11.7 105 2.00 2 7.39 47 12.9 49 2.08 1 6.68 110 10.8 90 2.00 78 9.54 102 15.7 100 5.32 105 18.7 119 25.5 118 5.29 123 11.9 125 29.5 120 3.87 103 10.1 104 41.7 85 2.52 96 16.1 123 24.8 114 2.38 104
UnFlow [129]92.2 5.97 117 15.5 123 2.16 85 9.13 102 14.1 81 2.38 93 6.68 110 13.0 120 2.00 78 9.35 94 17.1 111 5.23 77 18.6 115 25.8 124 5.20 61 11.5 115 29.5 120 3.74 20 9.66 93 37.4 16 2.45 1 16.9 127 28.1 126 2.38 104
Dynamic MRF [7]92.9 5.26 59 11.5 99 2.00 2 8.12 77 14.3 88 2.16 61 6.68 110 12.8 118 2.00 78 10.9 118 18.3 117 5.51 118 18.3 93 25.0 93 5.20 61 11.6 116 28.6 116 3.87 103 10.7 114 45.7 115 2.52 96 14.9 101 23.3 90 2.31 86
NL-TV-NCC [25]95.3 6.03 118 12.8 116 2.00 2 8.29 78 14.7 100 2.16 61 6.35 93 11.7 105 2.00 78 10.7 115 18.6 118 5.45 112 18.1 63 24.1 34 5.45 129 12.0 126 28.2 114 3.79 76 13.0 130 43.4 105 2.58 118 15.1 106 23.2 86 2.38 104
SILK [79]96.8 5.80 115 12.7 114 2.38 118 11.1 125 15.6 123 2.83 120 7.35 123 13.0 120 2.00 78 10.8 117 17.5 113 5.48 116 18.3 93 24.8 77 5.20 61 10.5 71 26.0 67 4.20 127 10.0 102 37.1 11 2.52 96 14.6 86 22.7 63 2.31 86
Learning Flow [11]98.2 5.57 107 11.1 81 2.16 85 9.27 104 15.1 108 2.16 61 7.00 118 13.3 122 2.00 78 10.2 108 15.2 89 5.45 112 18.5 110 25.1 99 5.32 125 10.7 88 26.2 76 3.87 103 10.6 113 40.8 74 2.52 96 15.1 106 23.3 90 2.38 104
FOLKI [16]105.4 6.14 120 12.4 112 3.11 128 11.5 127 15.5 121 3.32 128 7.00 118 14.7 126 2.38 124 13.5 127 18.2 115 6.27 129 18.6 115 25.0 93 5.23 106 10.3 59 25.1 43 4.04 120 11.0 119 38.3 34 2.58 118 14.7 91 22.4 53 2.38 104
Adaptive flow [45]105.6 6.24 121 11.3 92 2.71 126 11.2 126 15.7 124 3.42 129 6.35 93 10.9 92 2.00 78 10.2 108 14.7 80 5.72 122 18.7 119 25.4 113 5.23 106 11.7 119 30.8 125 3.87 103 9.42 78 38.8 47 2.58 118 14.9 101 24.3 110 2.38 104
GroupFlow [9]107.1 6.56 126 19.6 130 2.16 85 9.38 108 14.7 100 2.52 109 7.68 124 16.8 129 2.00 78 11.1 119 23.5 129 5.29 101 20.7 128 29.3 128 5.23 106 12.4 130 32.8 129 3.87 103 11.3 124 49.6 127 2.45 1 16.8 126 30.4 129 2.16 1
Heeger++ [104]110.0 7.16 129 18.5 128 2.16 85 9.75 113 13.8 74 2.45 104 9.35 128 16.1 128 2.38 124 13.0 124 18.9 120 5.74 123 19.8 127 27.4 127 5.23 106 12.2 127 26.0 67 3.92 116 13.5 131 46.5 117 2.52 96 16.1 123 27.4 123 2.16 1
SLK [47]113.2 6.03 118 13.6 119 2.45 122 10.1 118 13.8 74 2.89 122 7.68 124 12.4 116 2.38 124 13.8 129 21.0 125 5.77 125 19.1 126 26.4 126 5.20 61 11.2 109 26.2 76 3.87 103 11.8 125 46.9 119 2.58 118 15.2 115 26.1 120 2.38 104
FFV1MT [106]115.0 6.40 124 16.8 125 2.16 85 9.87 114 13.9 77 2.89 122 9.35 128 18.7 130 2.52 129 13.0 124 18.9 120 5.74 123 18.8 123 25.7 122 5.26 116 10.9 95 26.0 67 3.92 116 12.8 129 46.5 117 2.52 96 16.2 125 27.4 123 2.45 129
HCIC-L [99]116.5 7.62 130 17.7 127 3.16 129 9.98 116 14.8 106 3.16 127 7.14 122 14.0 124 2.00 78 12.4 123 21.5 128 5.35 107 18.9 125 25.5 118 5.26 116 11.8 122 31.9 128 3.87 103 9.47 82 43.2 104 2.58 118 18.7 130 30.1 128 2.38 104
PGAM+LK [55]116.7 6.56 126 16.0 124 2.71 126 10.0 117 14.7 100 3.00 124 7.75 127 15.7 127 2.38 124 13.7 128 21.1 127 6.27 129 18.8 123 25.8 124 5.26 116 11.6 116 27.2 97 4.08 124 10.7 114 40.3 68 2.58 118 15.1 106 24.4 111 2.38 104
Pyramid LK [2]118.3 6.24 121 13.7 120 3.16 129 12.7 130 15.8 126 3.79 130 11.8 130 12.3 114 3.00 130 25.5 131 41.4 130 7.14 131 22.9 130 33.6 130 5.20 61 10.7 88 25.4 52 3.92 116 11.2 122 49.2 126 2.65 127 19.6 131 37.8 131 2.38 104
Periodicity [78]129.4 6.81 128 17.5 126 3.27 131 15.3 131 16.9 130 4.24 131 13.7 131 22.7 131 4.36 131 18.0 130 41.4 130 6.16 128 23.9 131 34.4 131 5.60 131 12.2 127 34.5 130 4.51 130 11.8 125 55.6 129 2.65 127 17.9 129 29.7 127 2.71 131
Move the mouse over the numbers in the table to see the corresponding images. Click to compare with the ground truth.

References

Methodtime*framescolor Reference and notes
[1] 2D-CLG 844 2 gray The 2D-CLG method by Bruhn et al. as implemented by Stefan Roth. [A. Bruhn, J. Weickert, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. IJCV 63(3), 2005.] Parameters were set to match the published performance on Yosemite sequence, which may not be optimal for other sequences.
[2] Pyramid LK 12 2 color A modification of Bouguet's pyramidal implementation of Lucas-Kanade.
[3] Horn & Schunck 49 2 gray A modern Matlab implementation of the Horn & Schunck method by Deqing Sun. Parameters set to optimize AAE on all training data.
[4] Black & Anandan 328 2 gray A modern Matlab implementation of the Black & Anandan method by Deqing Sun.
[5] Brox et al. 18 2 color T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004. (Improved using separate robust functions as proposed in A. Bruhn and J. Weickert, Towards ultimate motion estimation, ICCV 2005; improved by training on the training set.)
[6] Fusion 2,666 2 color V. Lempitsky, S. Roth, and C. Rother. Discrete-continuous optimization for optical flow estimation. CVPR 2008.
[7] Dynamic MRF 366 2 gray B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic MRFs. CVPR 2008. (Method improved since publication.)
[8] Second-order prior 14 2 gray W. Trobin, T. Pock, D. Cremers, and H. Bischof. An unbiased second-order prior for high-accuracy motion estimation. DAGM 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[9] GroupFlow 600 2 gray X. Ren. Local Grouping for Optical Flow. CVPR 2008.
[10] SegOF 60 2 color L. Xu, J. Chen, and J. Jia. Segmentation based variational model for accurate optical flow estimation. ECCV 2008. Code available.
[11] Learning Flow 825 2 gray D. Sun, S. Roth, J.P. Lewis, and M. Black. Learning optical flow (SRF-LFC). ECCV 2008.
[12] CBF 69 2 color W. Trobin, T. Pock, D. Cremers, and H. Bischof. Continuous energy minimization via repeated binary fusion. ECCV 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[13] SPSA-learn 200 2 color Y. Li and D. Huttenlocher. Learning for optical flow using stochastic optimization. ECCV 2008.
[14] GraphCuts 1,200 2 color T. Cooke. Two applications of graph-cuts to image processing. DICTA 2008.
[15] F-TV-L1 8 2 gray A. Wedel, T. Pock, J. Braun, U. Franke, and D. Cremers. Duality TV-L1 flow with fundamental matrix prior. IVCNZ 2008.
[16] FOLKI 1.4 2 gray G. Le Besnerais and F. Champagnat. Dense optical flow by iterative local window registration. ICIP 2005.
[17] TV-L1-improved 2.9 2 gray A. Wedel, T. Pock, C. Zach, H. Bischof, and D. Cremers. An improved algorithm for TV-L1 optical flow computation. Proceedings of the Dagstuhl Visual Motion Analysis Workshop 2008. Code at GPU4Vision.
[18] DPOF 287 2 color C. Lei and Y.-H. Yang. Optical flow estimation on coarse-to-fine region-trees using discrete optimization. ICCV 2009. (Method improved since publication.)
[19] Filter Flow 34,000 2 color S. Seitz and S. Baker. Filter flow. ICCV 2009.
[20] Adaptive 9.2 2 gray A. Wedel, D. Cremers, T. Pock, and H. Bischof. Structure- and motion-adaptive regularization for high accuracy optic flow. ICCV 2009.
[21] Complementary OF 44 2 color H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[22] Aniso. Huber-L1 2 2 gray M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009. Code at GPU4Vision.
[23] Rannacher 0.12 2 gray J. Rannacher. Realtime 3D motion estimation on graphics hardware. Bachelor thesis, Heidelberg University, 2009.
[24] TI-DOFE 260 2 gray C. Cassisa, S. Simoens, and V. Prinet. Two-frame optical flow formulation in an unwarped multiresolution scheme. CIARP 2009.
[25] NL-TV-NCC 20 2 color M. Werlberger, T. Pock, and H. Bischof. Motion estimation with non-local total variation regularization. CVPR 2010.
[26] MDP-Flow 188 2 color L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. CVPR 2010.
[27] ACK-Prior 5872 2 color K. Lee, D. Kwon, I. Yun, and S. Lee. Optical flow estimation with adaptive convolution kernel prior on discrete framework. CVPR 2010.
[28] LDOF 122 2 color T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI 33(3):500-513, 2011.
[29] p-harmonic 565 2 gray J. Gai and R. Stevenson. Optical flow estimation with p-harmonic regularization. ICIP 2010.
[30] TriangleFlow 4200 2 gray B. Glocker, H. Heibel, N. Navab, P. Kohli, and C. Rother. TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. ECCV 2010.
[31] Classic+NL 972 2 color D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR 2010. Matlab code.
[32] Classic++ 486 2 gray A modern implementation of the classical formulation descended from Horn & Schunck and Black & Anandan; see D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, CVPR 2010.
[33] Nguyen 33 2 gray D. Nguyen. Tuning optical flow estimation with image-driven functions. ICRA 2011.
[34] Modified CLG 133 2 gray R. Fezzani, F. Champagnat, and G. Le Besnerais. Combined local global method for optic flow computation. EUSIPCO 2010.
[35] ComplOF-FED-GPU 0.97 2 color P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, and J. Weickert. A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. CVGPU Workshop 2010.
[36] Ad-TV-NDC 35 2 gray M. Nawaz. Motion estimation with adaptive regularization and neighborhood dependent constraint. DICTA 2010.
[37] Layers++ 18206 2 color D. Sun, E. Sudderth, and M. Black. Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010.
[38] OFH 620 3 color H. Zimmer, A. Bruhn, J. Weickert. Optic flow in harmony. IJCV 93(3) 2011.
[39] LSM 1615 2 color K. Jia, X. Wang, and X. Tang. Optical flow estimation using learned sparse model. ICCV 2011.
[40] CostFilter 55 2 color C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. CVPR 2011.
[41] Bartels 0.15 2 gray C. Bartels and G. de Haan. Smoothness constraints in recursive search motion estimation for picture rate conversion. IEEE TCSVT 2010. Version improved since publication: mapped on GPU.
[42] Shiralkar 600 2 gray M. Shiralkar and R. Schalkoff. A self organization-based optical flow estimator with GPU implementation. MVA 23(6):1229-1242.
[43] HBpMotionGpu 1000 5 gray S. Grauer-Gray and C. Kambhamettu. Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. WACV 2009. (Method improved since publication.)
[44] StereoFlow 7200 2 color G. Rosman, S. Shem-Tov, D. Bitton, T. Nir, G. Adiv, R. Kimmel, A. Feuer, and A. Bruckstein. Over-parameterized optical flow using a stereoscopic constraint. SSVM 2011:761-772.
[45] Adaptive flow 121 2 gray T. Arici. Energy minimization based motion estimation using adaptive smoothness priors. Submitted to IEEE TIP 2011.
[46] TC-Flow 2500 5 color S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer. Modeling temporal coherence for optical flow. ICCV 2011.
[47] SLK 300 2 gray T. Corpetti and E. Mémin. Stochastic uncertainty models for the luminance consistency assumption. IEEE TIP 2011.
[48] CLG-TV 29 2 gray M. Drulea. Total variation regularization of local-global optical flow. ITSC 2011. Matlab code.
[49] SimpleFlow 1.7 2 color M. Tao, J. Bai, P. Kohli, S. Paris. SimpleFlow: a non-iterative, sublinear optical flow algorithm. EUROGRAPHICS 2012.
[50] IAOF 57 2 gray D. Nguyen. Improving motion estimation using image-driven functions and hybrid scheme. PSIVT 2011.
[51] IAOF2 56 2 gray D. Nguyen. Enhancing the sharpness of flow field using image-driven functions with occlusion-aware filter. Submitted to TIP 2011.
[52] LocallyOriented 9541 2 gray Y.Niu, A. Dick, and M. Brooks. Locally oriented optical flow computation. To appear in TIP 2012.
[53] IROF-TV 261 2 color H. Rashwan, D. Puig, and M. Garcia. On improving the robustness of differential optical flow. ICCV 2011 Artemis workshop.
[54] Sparse Occlusion 2312 2 color A. Ayvaci, M. Raptis, and S. Soatto. Sparse occlusion detection with optical flow. Submitted to IJCV 2011.
[55] PGAM+LK 0.37 2 gray A. Alba, E. Arce-Santana, and M. Rivera. Optical flow estimation with prior models obtained from phase correlation. ISVC 2010.
[56] Sparse-NonSparse 713 2 color L. Chen, J. Wang, and Y. Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. Submitted to PAMI 2013.
[57] nLayers 36150 4 color D. Sun, E. Sudderth, and M. Black. Layered segmentation and optical flow estimation over time. CVPR 2012.
[58] IROF++ 187 2 color H. Rashwan, D. Puig, and M. Garcia. Variational optical flow estimation based on stick tensor voting. IEEE TIP 2013.
[59] COFM 600 3 color M. Mozerov. Constrained optical flow estimation as a matching problem. IEEE TIP 2013.
[60] Efficient-NL 400 2 color P. Krähenbühl and V. Koltun. Efficient nonlocal regularization for optical flow. ECCV 2012.
[61] BlockOverlap 2 2 gray M. Santoro, G. AlRegib, and Y. Altunbasak. Motion estimation using block overlap minimization. Submitted to MMSP 2012.
[62] Ramp 1200 2 color A. Singh and N. Ahuja. Exploiting ramp structures for improving optical flow estimation. ICPR 2012.
[63] Occlusion-TV-L1 538 3 gray C. Ballester, L. Garrido, V. Lazcano, and V. Caselles. A TV-L1 optical flow method with occlusion detection. DAGM-OAGM 2012.
[64] TV-L1-MCT 90 2 color M. Mohamed and B. Mertsching. TV-L1 optical flow estimation with image details recovering based on modified census transform. ISVC 2012.
[65] Local-TV-L1 500 2 gray L. Raket. Local smoothness for global optical flow. ICIP 2012.
[66] ALD-Flow 61 2 color M. Stoll, A. Bruhn, and S. Volz. Adaptive integration of feature matches into variational optic flow methods. ACCV 2012.
[67] SIOF 234 2 color L. Xu, Z. Dai, and J. Jia. Scale invariant optical flow. ECCV 2012.
[68] MDP-Flow2 342 2 color L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012. Code available.
[69] TCOF 1421 all gray J. Sanchez, A. Salgado, and N. Monzon. Optical flow estimation with consistent spatio-temporal coherence models. VISAPP 2013.
[70] LME 476 2 color W. Li, D. Cosker, M. Brown, and R. Tang. Optical flow estimation using Laplacian mesh energy. CVPR 2013.
[71] NN-field 362 2 color L. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013.
[72] FESL 3310 2 color W. Dong, G. Shi, X. Hu, and Y. Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. Submitted to IEEE TIP 2013.
[73] PMF 35 2 color J. Lu, H. Yang, D. Min, and M. Do. PatchMatch filter: efficient edge-aware filtering meets randomized search for fast correspondence field estimation. CVPR 2013.
[74] FC-2Layers-FF 2662 4 color D. Sun, J. Wulff, E. Sudderth, H. Pfister, and M. Black. A fully-connected layered model of foreground and background flow. CVPR 2013.
[75] Correlation Flow 290 2 color M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code.
[76] TC/T-Flow 341 5 color M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013.
[77] OFLAF 1530 2 color T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013.
[78] Periodicity 8000 4 color G. Khachaturov, S. Gonzalez-Brambila, and J. Gonzalez-Trejo. Periodicity-based computation of optical flow. Submitted to Computacion y Sistemas (CyS) 2013.
[79] SILK 572 2 gray P. Zille, C. Xu, T. Corpetti, L. Shao. Observation models based on scale interactions for optical flow estimation. Submitted to IEEE TIP.
[80] CRTflow 13 3 color O. Demetz, D. Hafner, and J. Weickert. The complete rank transform: a tool for accurate and morphologically invariant matching of structures. BMVC 2013.
[81] SuperFlow 178 2 color Anonymous. Superpixel based optical flow estimation. ICCV 2013 submission 507.
[82] Aniso-Texture 300 2 color Anonymous. Texture information-based optical flow estimation using an incremental multi-resolution approach. ITC-CSCC 2013 submission 267.
[83] Classic+CPF 640 2 gray Z. Tu, R. Veltkamp, and N. van der Aa. A combined post-filtering method to improve accuracy of variational optical flow estimation. Submitted to Pattern Recognition 2013.
[84] S2D-Matching 1200 2 color Anonymous. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013 submission 1479.
[85] AGIF+OF 438 2 gray Z. Tu, R. Poppe, and R. Veltkamp. Adaptive guided image filter to warped interpolation image for variational optical flow computation. Submitted to Signal Processing 2015.
[86] DeepFlow 13 2 color P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[87] NNF-Local 673 2 color Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow with nearest neighbor field. Submitted to PAMI 2014.
[88] EPPM w/o HM 2.5 2 color L. Bao, Q. Yang, and H. Jin. Fast edge-preserving PatchMatch for large displacement optical flow. CVPR 2014.
[89] MLDP_OF 165 2 gray M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia, and D. Puig. Illumination-robust optical flow approach using local directional pattern. IEEE TCSVT 24(9):1499-1508, 2014.
[90] RFlow 20 2 gray S. Ali, C. Daul, and W. Blondel. Robust and accurate optical flow estimation for weak texture and varying illumination condition: Application to cystoscopy. IPTA 2014.
[91] SRR-TVOF-NL 32 all color P. Pohl, M. Sirotenko, E. Tolstaya, and V. Bucha. Edge preserving motion estimation with occlusions correction for assisted 2D to 3D conversion. IS&T/SPIE Electronic Imaging 2014.
[92] 2DHMM-SAS 157 2 color M.-C. Shih, R. Shenoy, and K. Rose. A two-dimensional hidden Markov model with spatially-adaptive states with application of optical flow. ICIP 2014 submission.
[93] WLIF-Flow 700 2 color Z. Tu, R. Veltkamp, N. van der Aa, and C. Van Gemeren. Weighted local intensity fusion method for variational optical flow estimation. Submitted to TIP 2014.
[94] FMOF 215 2 color N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014.
[95] TriFlow 150 2 color TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914.
[96] ComponentFusion 6.5 2 color Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941.
[97] AggregFlow 1642 2 color D. Fortun, P. Bouthemy, and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. Preprint arXiv:1407.5759.
[98] 2bit-BM-tele 124 2 gray R. Xu and D. Taubman. Robust dense block-based motion estimation using a two-bit transform on a Laplacian pyramid. ICIP 2013.
[99] HCIC-L 330 2 color Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114.
[100] TF+OM 600 2 color R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015.
[101] PH-Flow 800 2 color J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015.
[102] EpicFlow 16 2 color J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015.
[103] NNF-EAC 380 2 color Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336.
[104] Heeger++ 6600 5 gray Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238.
[105] HBM-GC 330 2 color A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015.
[106] FFV1MT 358 5 gray F. Solari, M. Chessa, N. Medathati, and P. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation? Submitted to Signal Processing: Image Communication 2015.
[107] ROF-ND 4 2 color S. Ali, C. Daul, E. Galbrun, and W. Blondel. Illumination invariant large displacement optical flow using robust neighbourhood descriptors. Submitted to CVIU 2015.
[108] DeepFlow2 16 2 color J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015.
[109] HAST 2667 2 color Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221.
[110] FlowFields 15 2 color C. Bailer, B. Taetz, and D. Stricker. Flow Fields: Dense unregularized correspondence fields for highly accurate large displacement optical flow estimation. ICCV 2015.
[111] SVFilterOh 1.56 2 color Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788.
[112] FlowNetS+ft+v 0.5 2 color Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235.
[113] CombBMOF 51 2 color M. Brüggemann, R. Kays, P. Springer, and O. Erdler. Combined block-matching and adaptive differential motion estimation in a hierarchical multi-scale framework. ICGIP 2014. (Method improved since publication.)
[114] PMMST 182 2 color F. Zhang, S. Xu, and X. Zhang. High accuracy correspondence field estimation via MST based patch matching. Submitted to TIP 2015.
[115] DF-Auto 70 2 color N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015.
[116] CPM-Flow 3 2 color Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241.
[117] CNN-flow-warp+ref 1.4 3 color D. Teney and M. Hebert. Learning to extract motion from videos in convolutional neural networks. ArXiv 1601.07532, 2016.
[118] Steered-L1 804 2 color Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016.
[119] StereoOF-V1MT 343 2 gray Anonymous. Visual features for action-oriented tasks: a cortical-like model for disparity and optic flow computation. BMVC 2016 submission 132.
[120] PGM-C 5 2 color Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016.
[121] RNLOD-Flow 1040 2 gray C. Zhang, Z. Chen, M. Wang, M. Li, and S. Jiang. Robust non-local TV-L1 optical flow estimation with occlusion detection. Submitted to TIP 2016.
[122] FlowNet2 0.091 2 color Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900.
[123] S2F-IF 20 2 color Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765.
[124] BriefMatch 0.068 2 gray G. Eilertsen, P.-E. Forssen, and J. Unger. Dense binary feature matching for real-time optical flow estimation. SCIA 2017 submission 62.
[125] OAR-Flow 60 2 color Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20.
[126] AdaConv-v1 2.8 2 color S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017.
[127] SepConv-v1 0.2 2 color S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017.
[128] ProbFlowFields 37 2 color A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017.
[129] UnFlow 0.12 2 color Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018.
[130] FlowFields+ 10.5 2 color C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. Submitted to PAMI 2017.
[131] Kuang 9.9 2 gray F. Kuang. PatchMatch algorithms for motion estimation and stereo reconstruction. Master thesis, University of Stuttgart, 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.