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        
R2.5
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
NNF-Local [87]12.1 13.4 3 36.1 7 1.56 2 24.2 2 35.7 6 2.60 5 18.4 21 30.4 6 1.43 3 59.2 19 68.4 50 41.6 17 79.1 11 87.3 5 43.1 21 36.4 14 66.6 16 25.0 21 31.7 11 63.5 9 4.66 19 38.9 9 78.4 8 3.01 6
PH-Flow [101]14.0 13.7 20 37.1 30 1.77 25 24.3 5 35.5 5 2.58 3 18.5 23 30.6 9 1.54 16 58.8 1 66.8 4 41.6 17 79.0 5 87.2 3 42.9 11 36.3 9 67.1 50 24.6 7 31.6 2 63.7 18 4.64 11 39.0 17 78.6 16 3.10 28
NN-field [71]15.1 13.5 8 36.9 25 1.67 11 24.2 2 35.4 4 2.54 1 18.7 36 30.6 9 1.52 13 59.3 29 68.5 55 41.7 25 79.1 11 87.3 5 43.2 37 36.4 14 66.2 4 25.0 21 31.6 2 63.6 12 4.64 11 38.9 9 78.1 5 3.05 13
MDP-Flow2 [68]17.1 13.3 2 35.1 3 1.62 5 24.6 13 36.5 13 2.63 10 18.5 23 30.5 7 1.42 2 59.0 9 67.8 26 41.4 4 79.1 11 87.3 5 43.4 61 36.5 20 66.4 9 25.0 21 32.0 49 63.9 24 4.64 11 39.3 42 78.7 21 3.08 20
PMMST [114]17.8 13.4 3 35.0 2 1.70 15 25.1 32 37.1 25 2.73 19 18.5 23 30.5 7 1.39 1 58.9 4 67.4 13 41.5 11 79.2 30 87.4 12 43.4 61 36.3 9 66.2 4 24.9 15 31.8 20 63.8 20 4.67 21 39.2 36 78.7 21 3.09 24
COFM [59]18.1 13.6 15 36.0 5 1.89 48 24.6 13 36.4 11 2.71 17 18.5 23 30.3 4 1.59 25 58.8 1 66.8 4 41.1 2 79.0 5 87.4 12 42.6 6 35.8 3 67.2 59 24.1 2 31.2 1 61.6 1 4.89 85 38.5 3 78.1 5 3.34 85
Layers++ [37]18.7 14.0 47 37.5 42 1.91 51 24.3 5 35.3 3 2.75 21 18.3 19 31.0 18 1.56 20 59.2 19 67.5 16 41.7 25 79.2 30 87.4 12 43.1 21 36.4 14 66.5 13 25.0 21 31.6 2 63.2 4 4.60 1 38.7 6 77.7 4 3.12 34
AGIF+OF [85]19.9 13.9 40 37.5 42 1.67 11 24.6 13 36.5 13 2.68 13 18.1 15 31.0 18 1.61 30 58.9 4 66.9 6 41.4 4 79.2 30 87.5 37 43.1 21 36.6 31 67.2 59 25.0 21 31.8 20 63.6 12 4.60 1 39.0 17 78.6 16 2.98 3
Sparse-NonSparse [56]20.3 13.8 29 37.3 35 1.81 28 24.4 8 36.0 8 2.61 6 18.0 13 31.2 23 1.52 13 59.0 9 67.1 9 42.0 45 79.2 30 87.4 12 43.1 21 36.7 45 66.7 20 25.3 61 31.7 11 63.6 12 4.63 6 38.9 9 78.5 14 3.08 20
HAST [109]20.4 13.7 20 36.2 12 1.93 57 24.7 21 37.0 23 2.77 30 18.8 39 32.2 43 1.66 38 59.1 13 67.9 34 41.4 4 79.0 5 87.4 12 42.6 6 36.3 9 66.9 32 24.6 7 31.6 2 63.3 6 4.71 37 39.0 17 78.4 8 3.06 14
nLayers [57]20.8 13.9 40 36.7 21 1.85 39 24.5 10 36.1 9 2.76 24 17.7 6 30.0 3 1.44 4 59.2 19 67.6 18 41.6 17 79.3 54 87.5 37 43.3 46 36.4 14 66.8 27 25.1 35 31.7 11 63.2 4 4.72 42 38.7 6 77.6 3 3.03 9
ProbFlowFields [128]23.3 13.5 8 36.6 16 1.82 30 24.4 8 36.4 11 2.68 13 18.5 23 31.2 23 1.49 8 59.2 19 67.2 10 42.1 49 79.3 54 87.5 37 43.6 85 36.5 20 67.0 40 25.2 45 31.6 2 63.5 9 4.64 11 39.0 17 78.4 8 3.06 14
2DHMM-SAS [92]24.8 14.1 57 38.9 86 1.82 30 25.5 50 38.0 39 2.77 30 17.2 2 30.9 15 1.56 20 58.9 4 66.5 1 41.7 25 79.1 11 87.4 12 42.9 11 36.5 20 66.6 16 24.9 15 31.7 11 63.9 24 4.68 29 39.2 36 79.0 33 3.07 17
LSM [39]24.9 13.9 40 38.0 59 1.78 27 24.6 13 36.5 13 2.61 6 18.1 15 32.0 37 1.55 18 59.2 19 67.6 18 42.1 49 79.2 30 87.4 12 43.1 21 36.7 45 66.9 32 25.3 61 31.7 11 63.6 12 4.65 18 38.9 9 78.6 16 3.07 17
OFLAF [77]25.1 13.5 8 36.1 7 1.62 5 24.3 5 35.8 7 2.62 9 18.7 36 31.5 30 1.47 6 59.1 13 67.8 26 41.2 3 79.3 54 87.4 12 43.4 61 36.6 31 67.4 73 25.0 21 31.9 35 64.3 35 4.79 67 38.9 9 78.7 21 3.10 28
FMOF [94]26.3 14.2 66 38.6 74 1.91 51 24.5 10 36.2 10 2.70 16 18.4 21 31.2 23 1.77 54 59.5 38 68.0 36 41.5 11 79.2 30 87.4 12 43.1 21 36.6 31 66.8 27 25.0 21 31.6 2 63.3 6 4.61 4 39.1 26 78.4 8 3.11 33
CombBMOF [113]28.2 13.6 15 36.4 14 1.71 17 24.5 10 36.9 22 2.58 3 18.1 15 31.5 30 1.81 62 59.5 38 68.2 41 41.6 17 79.1 11 87.3 5 43.0 16 36.8 57 66.5 13 25.0 21 33.9 124 65.2 81 4.68 29 39.1 26 78.4 8 2.92 1
ComponentFusion [96]28.4 13.4 3 36.1 7 1.72 19 24.6 13 36.8 21 2.57 2 18.9 46 32.9 56 1.69 40 59.1 13 67.8 26 41.4 4 79.2 30 87.4 12 43.6 85 36.5 20 66.3 6 25.1 35 32.0 49 64.8 57 4.76 62 39.1 26 78.7 21 3.10 28
IROF++ [58]29.3 13.8 29 37.8 51 1.72 19 24.6 13 36.6 18 2.61 6 18.6 32 31.3 26 1.64 36 58.8 1 66.7 3 41.8 32 79.0 5 87.3 5 42.7 9 36.5 20 66.6 16 25.0 21 32.0 49 65.0 65 4.74 54 39.5 67 79.2 46 3.30 81
SepConv-v1 [127]30.8 9.23 1 28.0 1 1.08 1 20.5 1 32.4 1 3.35 81 8.95 1 20.5 1 2.08 82 60.8 96 66.9 6 44.2 109 79.1 11 87.1 2 43.2 37 35.6 1 62.4 1 25.1 35 32.2 74 62.3 2 5.34 116 37.6 1 76.4 1 3.28 78
Ramp [62]31.2 14.1 57 38.7 78 1.92 55 24.6 13 36.6 18 2.69 15 17.9 10 31.0 18 1.47 6 58.9 4 67.0 8 41.9 37 79.2 30 87.5 37 43.1 21 37.0 73 67.4 73 25.5 74 31.6 2 63.5 9 4.63 6 39.1 26 78.9 29 3.19 49
S2F-IF [123]32.2 13.5 8 36.6 16 1.70 15 24.9 26 37.9 35 2.77 30 18.8 39 32.7 52 1.54 16 59.1 13 67.7 22 41.6 17 79.3 54 87.5 37 43.3 46 36.5 20 67.1 50 25.0 21 31.9 35 64.7 53 4.74 54 39.3 42 79.1 43 3.10 28
TV-L1-MCT [64]33.2 14.5 87 39.7 106 1.86 42 25.2 35 37.8 33 2.78 33 17.3 3 31.1 22 1.59 25 58.9 4 66.6 2 41.6 17 79.1 11 87.4 12 42.9 11 36.8 57 66.4 9 25.6 79 31.8 20 64.0 29 4.73 46 39.1 26 79.0 33 3.20 56
FlowFields+ [130]33.7 13.5 8 37.0 28 1.69 14 25.0 29 38.2 45 2.78 33 18.9 46 33.3 67 1.55 18 59.1 13 67.6 18 41.9 37 79.3 54 87.5 37 43.3 46 36.6 31 67.2 59 25.1 35 31.8 20 64.5 45 4.67 21 39.3 42 79.2 46 3.07 17
RNLOD-Flow [121]33.9 13.9 40 37.9 56 1.86 42 25.2 35 37.9 35 2.78 33 19.0 52 32.1 39 1.78 56 59.2 19 67.8 26 41.5 11 79.1 11 87.4 12 43.1 21 36.7 45 66.8 27 25.2 45 31.9 35 64.2 32 4.75 58 39.2 36 79.0 33 3.06 14
FC-2Layers-FF [74]34.6 14.0 47 38.6 74 1.84 35 24.2 2 35.1 2 2.82 43 17.9 10 31.3 26 1.51 11 59.3 29 67.7 22 42.1 49 79.3 54 87.6 71 43.3 46 36.7 45 67.4 73 25.3 61 31.6 2 63.6 12 4.67 21 39.1 26 78.7 21 3.19 49
Classic+NL [31]35.0 14.2 66 38.8 80 1.98 60 24.6 13 36.5 13 2.65 11 17.7 6 30.9 15 1.51 11 59.2 19 67.5 16 42.2 60 79.2 30 87.5 37 43.3 46 37.0 73 67.1 50 25.5 74 31.7 11 63.6 12 4.67 21 39.2 36 79.0 33 3.18 46
Classic+CPF [83]35.8 14.1 57 38.3 68 1.74 22 24.9 26 37.1 25 2.73 19 17.6 5 31.4 28 1.60 28 59.0 9 67.3 11 41.4 4 79.3 54 87.6 71 43.3 46 36.9 64 67.9 98 25.2 45 31.9 35 64.3 35 4.64 11 39.3 42 79.2 46 3.04 10
FlowFields [110]36.2 13.6 15 37.1 30 1.74 22 25.0 29 38.1 40 2.75 21 18.8 39 33.2 65 1.53 15 59.4 33 68.0 36 42.3 68 79.3 54 87.5 37 43.2 37 36.5 20 67.0 40 25.0 21 31.8 20 64.7 53 4.69 32 39.4 54 79.3 54 3.13 35
NNF-EAC [103]38.5 14.2 66 37.3 35 2.09 71 25.3 40 37.6 31 2.76 24 18.9 46 30.6 9 1.61 30 59.8 59 68.5 55 43.3 102 79.1 11 87.3 5 43.1 21 36.5 20 66.5 13 25.0 21 32.1 61 64.3 35 4.73 46 39.4 54 79.0 33 3.14 37
LME [70]39.1 13.5 8 36.1 7 1.62 5 25.3 40 37.8 33 3.44 86 19.0 52 32.8 54 1.63 34 59.0 9 67.8 26 41.5 11 79.7 115 87.9 110 44.4 116 36.5 20 67.0 40 24.9 15 32.0 49 64.2 32 4.66 19 39.0 17 78.6 16 3.09 24
S2D-Matching [84]39.5 14.2 66 38.9 86 1.96 58 25.3 40 37.9 35 2.76 24 17.5 4 31.0 18 1.60 28 59.3 29 67.4 13 42.8 87 79.2 30 87.5 37 43.2 37 36.9 64 67.3 68 25.4 71 31.8 20 63.8 20 4.64 11 39.1 26 78.6 16 3.21 60
WLIF-Flow [93]39.6 13.8 29 37.4 39 1.73 21 24.9 26 37.1 25 2.81 40 18.5 23 30.9 15 1.49 8 59.4 33 67.8 26 42.5 80 79.2 30 87.4 12 43.8 107 37.2 86 67.5 80 25.9 96 31.8 20 63.9 24 4.64 11 39.4 54 78.9 29 3.14 37
FESL [72]42.1 14.4 83 39.1 92 1.83 33 25.0 29 37.4 29 2.76 24 18.2 18 31.6 32 1.70 41 59.7 50 68.5 55 41.7 25 79.3 54 87.6 71 43.3 46 36.9 64 67.9 98 25.2 45 31.8 20 63.8 20 4.61 4 39.3 42 78.8 26 3.04 10
PGM-C [120]45.9 13.8 29 37.7 50 1.85 39 25.1 32 38.1 40 2.90 49 19.1 62 33.6 70 1.59 25 59.3 29 68.2 41 41.9 37 79.3 54 87.5 37 43.5 68 36.6 31 67.2 59 25.2 45 31.9 35 64.8 57 4.67 21 39.5 67 79.4 62 3.22 62
MDP-Flow [26]46.7 13.4 3 36.1 7 1.67 11 24.8 24 37.2 28 2.79 37 18.8 39 32.0 37 1.70 41 59.8 59 68.9 83 42.1 49 79.3 54 87.6 71 43.5 68 36.7 45 67.7 90 25.2 45 32.5 91 65.5 91 4.77 65 39.1 26 79.0 33 3.09 24
PMF [73]46.8 13.7 20 37.1 30 1.66 10 25.5 50 39.3 66 2.71 17 19.0 52 34.9 98 1.74 51 59.4 33 68.4 50 41.8 32 79.4 84 87.6 71 43.3 46 37.3 90 66.9 32 26.2 104 31.9 35 64.3 35 4.73 46 39.3 42 78.8 26 2.93 2
SuperFlow [81]47.7 13.8 29 36.2 12 2.27 87 26.3 76 38.7 56 4.39 98 19.1 62 33.1 62 1.99 77 59.6 43 67.7 22 42.2 60 79.4 84 87.5 37 43.7 100 36.1 7 65.9 3 24.8 13 31.7 11 64.5 45 4.80 73 38.9 9 78.9 29 3.19 49
Efficient-NL [60]48.4 14.3 78 38.7 78 1.77 25 25.2 35 37.6 31 2.76 24 19.0 52 31.8 33 2.08 82 59.8 59 68.7 73 41.4 4 79.1 11 87.4 12 43.0 16 36.9 64 68.4 111 24.6 7 32.1 61 64.7 53 4.69 32 40.1 98 79.8 86 3.14 37
SVFilterOh [111]48.6 14.1 57 37.3 35 1.96 58 24.7 21 36.6 18 2.87 47 18.3 19 30.8 13 1.63 34 59.9 68 68.5 55 43.1 100 79.5 109 87.7 96 44.5 117 36.6 31 66.7 20 25.3 61 31.6 2 62.8 3 5.05 99 38.6 4 78.2 7 3.37 92
TC-Flow [46]49.0 13.7 20 36.9 25 1.91 51 25.3 40 38.5 51 3.05 62 19.3 79 34.1 87 1.73 47 59.2 19 67.8 26 42.2 60 79.3 54 87.5 37 43.5 68 37.1 79 68.0 101 25.6 79 31.9 35 64.3 35 4.71 37 39.0 17 79.0 33 3.13 35
Second-order prior [8]50.8 14.0 47 37.1 30 2.11 72 26.2 73 39.3 66 2.93 51 19.4 85 35.1 101 2.16 92 59.4 33 67.8 26 41.8 32 79.1 11 87.3 5 43.1 21 36.5 20 66.7 20 25.0 21 32.3 80 65.4 87 4.74 54 39.5 67 79.6 77 3.19 49
AggregFlow [97]50.9 14.5 87 38.3 68 2.20 81 25.7 63 38.5 51 3.23 75 18.6 32 30.8 13 1.44 4 59.7 50 68.4 50 41.7 25 79.4 84 87.6 71 43.8 107 37.5 95 66.9 32 26.4 109 31.8 20 64.2 32 4.70 36 38.9 9 78.4 8 3.08 20
EPPM w/o HM [88]51.0 13.4 3 36.6 16 1.61 3 25.5 50 39.3 66 2.76 24 19.4 85 35.7 107 1.99 77 59.6 43 69.3 92 41.9 37 79.2 30 87.4 12 43.1 21 37.0 73 67.5 80 25.3 61 32.8 101 65.0 65 4.85 81 39.4 54 79.0 33 3.04 10
IROF-TV [53]51.5 14.0 47 38.1 61 1.99 61 24.7 21 36.5 13 2.65 11 19.1 62 34.2 88 1.78 56 59.1 13 67.4 13 42.4 75 79.4 84 87.7 96 43.6 85 36.0 4 66.4 9 24.4 5 32.1 61 64.6 49 4.75 58 39.8 86 79.9 90 3.35 88
TF+OM [100]51.8 13.7 20 36.5 15 2.17 74 25.2 35 37.4 29 3.76 88 17.9 10 32.7 52 1.76 53 59.8 59 68.5 55 42.3 68 79.3 54 87.5 37 43.7 100 36.9 64 66.7 20 25.7 89 31.8 20 64.3 35 4.79 67 39.3 42 79.3 54 3.47 104
DeepFlow2 [108]51.9 13.9 40 36.6 16 2.07 69 25.6 58 38.4 48 3.08 64 19.1 62 33.6 70 1.70 41 59.6 43 68.5 55 41.9 37 79.4 84 87.5 37 43.7 100 36.7 45 66.3 6 25.4 71 31.9 35 64.7 53 4.67 21 39.4 54 79.4 62 3.26 75
CPM-Flow [116]52.5 13.8 29 37.8 51 1.87 46 25.1 32 38.2 45 2.93 51 19.0 52 33.4 69 1.61 30 59.6 43 68.7 73 42.1 49 79.3 54 87.5 37 43.5 68 36.8 57 66.9 32 25.5 74 32.0 49 65.2 81 4.68 29 39.5 67 79.5 70 3.25 71
TriFlow [95]53.3 14.2 66 39.0 90 2.20 81 26.6 81 39.3 66 4.59 102 19.0 52 33.7 73 1.71 46 59.9 68 68.7 73 41.4 4 79.2 30 87.5 37 43.5 68 36.7 45 67.1 50 25.2 45 31.8 20 63.9 24 4.69 32 39.1 26 79.0 33 3.23 67
SimpleFlow [49]53.6 14.1 57 38.9 86 1.92 55 25.5 50 37.9 35 2.85 45 19.0 52 32.3 45 2.26 97 59.2 19 67.3 11 42.4 75 79.2 30 87.5 37 43.2 37 36.7 45 67.6 86 25.1 35 32.0 49 66.1 101 5.29 112 39.3 42 79.2 46 3.15 40
EpicFlow [102]53.7 13.8 29 37.6 45 1.87 46 25.5 50 38.9 59 2.96 53 18.9 46 33.7 73 1.64 36 59.5 38 68.5 55 42.3 68 79.4 84 87.6 71 43.5 68 36.5 20 67.5 80 24.9 15 32.0 49 65.1 72 4.74 54 39.4 54 79.4 62 3.22 62
SRR-TVOF-NL [91]53.9 14.2 66 37.6 45 2.07 69 26.1 69 39.8 79 3.30 79 19.4 85 33.9 80 1.82 63 59.8 59 68.6 67 41.0 1 79.1 11 87.5 37 42.9 11 36.0 4 66.9 32 24.1 2 32.9 104 64.8 57 4.81 75 39.6 74 79.4 62 3.22 62
DeepFlow [86]54.1 13.7 20 35.7 4 2.03 66 25.6 58 38.2 45 3.30 79 19.2 72 33.9 80 1.74 51 59.7 50 68.0 36 42.2 60 79.4 84 87.5 37 43.7 100 37.3 90 66.4 9 26.2 104 31.8 20 64.8 57 4.63 6 39.3 42 79.3 54 3.26 75
CostFilter [40]55.1 13.6 15 37.4 39 1.63 9 25.5 50 39.7 77 2.75 21 19.0 52 36.0 110 1.79 58 59.4 33 68.8 79 42.0 45 79.4 84 87.6 71 43.7 100 38.6 113 67.1 50 28.1 122 31.9 35 64.6 49 4.81 75 39.0 17 78.5 14 3.00 4
OFH [38]55.2 14.1 57 38.2 66 2.03 66 25.6 58 38.4 48 3.01 58 19.4 85 35.1 101 1.79 58 59.5 38 68.8 79 42.3 68 79.1 11 87.4 12 43.1 21 36.7 45 67.6 86 25.2 45 32.1 61 65.1 72 4.79 67 39.2 36 79.2 46 3.15 40
Complementary OF [21]55.5 13.7 20 37.8 51 1.71 17 25.2 35 38.6 54 2.81 40 19.8 106 33.7 73 2.38 102 59.9 68 69.2 91 42.8 87 79.2 30 87.5 37 43.1 21 36.6 31 67.4 73 25.2 45 32.3 80 65.4 87 4.79 67 38.8 8 78.9 29 3.29 79
RFlow [90]55.8 13.8 29 37.8 51 2.02 64 26.0 66 39.1 64 2.85 45 19.0 52 33.1 62 1.86 66 59.7 50 68.4 50 42.2 60 79.2 30 87.6 71 43.4 61 36.1 7 66.8 27 24.5 6 32.2 74 65.1 72 4.82 80 39.7 80 79.8 86 3.34 85
DPOF [18]55.9 14.2 66 39.1 92 2.19 80 24.8 24 37.0 23 2.80 38 19.3 79 31.9 34 2.01 79 60.2 83 69.5 101 42.3 68 79.1 11 87.4 12 43.1 21 36.7 45 67.1 50 24.6 7 32.4 86 65.3 85 4.81 75 39.5 67 79.5 70 3.18 46
Aniso. Huber-L1 [22]56.1 14.3 78 38.5 72 2.17 74 26.6 81 39.5 75 3.21 74 19.2 72 32.5 50 1.83 65 59.7 50 68.7 73 41.9 37 79.2 30 87.4 12 43.2 37 36.3 9 67.1 50 24.6 7 32.2 74 64.9 63 4.71 37 39.7 80 79.6 77 3.24 70
OAR-Flow [125]56.7 14.0 47 36.9 25 2.05 68 25.3 40 38.1 40 3.11 67 19.1 62 34.0 86 1.70 41 59.2 19 68.6 67 41.9 37 79.4 84 87.6 71 43.5 68 36.9 64 67.8 93 25.3 61 32.0 49 65.1 72 4.75 58 39.3 42 79.3 54 3.18 46
TC/T-Flow [76]56.9 14.3 78 38.8 80 1.84 35 25.3 40 38.6 54 2.81 40 18.9 46 32.4 49 1.58 23 59.9 68 69.5 101 42.1 49 79.3 54 87.5 37 43.5 68 37.1 79 68.0 101 25.2 45 32.1 61 65.2 81 4.81 75 39.2 36 79.4 62 3.00 4
Brox et al. [5]57.2 14.0 47 37.4 39 1.90 49 26.4 78 40.1 87 3.08 64 19.3 79 35.0 100 1.97 74 59.7 50 68.2 41 41.7 25 79.4 84 87.6 71 43.6 85 36.6 31 66.9 32 25.1 35 31.9 35 64.8 57 4.73 46 39.4 54 79.5 70 3.15 40
Sparse Occlusion [54]58.1 14.2 66 38.6 74 1.99 61 25.8 65 39.2 65 2.78 33 19.3 79 32.3 45 1.80 61 59.8 59 68.8 79 41.7 25 79.3 54 87.5 37 43.2 37 37.1 79 68.4 111 25.3 61 32.1 61 64.4 44 4.60 1 39.7 80 79.6 77 3.15 40
ComplOF-FED-GPU [35]58.5 14.0 47 38.0 59 1.91 51 25.3 40 38.5 51 2.90 49 20.2 110 34.6 94 2.16 92 59.5 38 68.5 55 42.5 80 79.2 30 87.4 12 43.2 37 36.6 31 67.4 73 25.0 21 32.2 74 65.4 87 4.75 58 39.7 80 79.8 86 3.19 49
GraphCuts [14]58.9 15.1 103 39.3 96 2.68 99 26.4 78 39.4 73 4.50 100 19.2 72 30.7 12 2.69 108 60.7 94 68.6 67 42.8 87 79.0 5 87.4 12 42.5 5 35.6 1 66.7 20 23.7 1 32.0 49 65.0 65 5.04 98 39.0 17 79.2 46 3.48 105
Aniso-Texture [82]58.9 13.6 15 36.6 16 1.82 30 26.2 73 39.3 66 3.20 73 19.6 95 33.0 59 1.96 73 59.7 50 68.5 55 42.6 83 79.4 84 87.6 71 43.6 85 37.0 73 68.4 111 25.7 89 31.9 35 63.8 20 4.63 6 39.4 54 79.3 54 3.16 44
Fusion [6]59.7 13.8 29 38.4 71 1.84 35 25.3 40 38.1 40 2.88 48 19.1 62 32.2 43 1.90 69 60.9 98 69.8 105 41.8 32 79.1 11 87.9 110 42.1 2 36.0 4 67.8 93 24.1 2 32.7 99 66.3 105 4.88 84 39.5 67 80.4 109 3.26 75
DF-Auto [115]60.1 14.2 66 36.7 21 2.25 85 26.5 80 39.0 62 4.23 94 18.8 39 31.4 28 1.58 23 60.1 79 69.3 92 41.6 17 79.3 54 87.5 37 43.6 85 36.6 31 67.0 40 25.1 35 32.3 80 65.1 72 4.81 75 39.9 87 80.1 98 3.22 62
Classic++ [32]60.3 14.0 47 38.1 61 2.17 74 25.7 63 38.8 57 2.96 53 19.3 79 33.9 80 1.93 70 59.7 50 67.9 34 42.8 87 79.2 30 87.5 37 43.3 46 37.4 94 67.0 40 26.6 111 31.8 20 64.3 35 4.78 66 39.4 54 79.5 70 3.36 89
Steered-L1 [118]60.6 13.7 20 37.5 42 1.84 35 25.5 50 38.9 59 3.17 72 19.7 102 33.1 62 2.40 103 60.2 83 68.5 55 42.8 87 79.4 84 87.7 96 43.5 68 36.6 31 67.0 40 25.6 79 31.8 20 64.6 49 4.96 92 38.6 4 79.0 33 3.36 89
ALD-Flow [66]61.6 14.1 57 37.9 56 2.17 74 25.4 48 38.4 48 3.14 69 19.1 62 33.9 80 1.73 47 59.6 43 69.0 87 42.6 83 79.4 84 87.6 71 43.6 85 37.0 73 67.5 80 25.6 79 31.7 11 64.0 29 4.69 32 39.4 54 79.5 70 3.20 56
p-harmonic [29]61.9 13.5 8 36.7 21 1.85 39 26.7 88 39.9 83 3.25 77 19.4 85 35.2 103 2.10 85 60.1 79 68.7 73 42.2 60 79.3 54 87.5 37 43.3 46 36.7 45 66.7 20 25.3 61 32.6 95 65.8 95 4.76 62 39.4 54 79.5 70 3.17 45
Shiralkar [42]64.8 14.2 66 39.0 90 2.02 64 26.8 89 40.3 91 2.98 55 18.5 23 38.0 122 2.48 106 60.1 79 67.7 22 41.8 32 78.8 2 87.2 3 42.3 4 37.7 101 67.2 59 26.2 104 33.2 110 67.1 109 4.94 89 39.4 54 79.3 54 3.10 28
HBM-GC [105]67.8 14.7 93 39.4 101 2.41 94 25.4 48 38.1 40 3.07 63 18.0 13 29.8 2 1.56 20 59.8 59 68.2 41 42.8 87 80.1 121 88.0 116 45.9 124 37.5 95 68.2 107 26.1 102 31.9 35 63.3 6 4.99 94 39.3 42 79.1 43 3.30 81
FlowNet2 [122]68.0 15.9 115 41.4 114 2.76 102 27.1 93 40.2 89 4.29 96 19.6 95 34.3 90 1.88 67 60.0 73 70.2 109 42.0 45 79.4 84 87.7 96 43.3 46 36.4 14 66.3 6 24.9 15 32.1 61 64.5 45 4.71 37 39.6 74 79.2 46 3.08 20
SIOF [67]69.0 14.7 93 39.5 103 2.23 84 27.1 93 40.3 91 4.25 95 19.1 62 32.9 56 1.82 63 59.8 59 68.6 67 42.1 49 79.1 11 87.4 12 43.0 16 37.1 79 67.1 50 25.5 74 32.4 86 64.9 63 4.79 67 40.1 98 79.9 90 3.40 96
CLG-TV [48]69.2 14.3 78 38.8 80 2.17 74 26.6 81 39.8 79 3.24 76 19.5 92 33.9 80 2.11 87 60.0 73 69.0 87 42.4 75 79.3 54 87.6 71 43.5 68 36.6 31 66.9 32 25.1 35 32.1 61 65.1 72 4.71 37 39.9 87 80.0 94 3.20 56
MLDP_OF [89]70.0 13.9 40 38.1 61 1.81 28 25.6 58 38.9 59 2.80 38 18.8 39 32.3 45 1.61 30 59.6 43 68.3 47 42.3 68 79.3 54 87.6 71 43.9 110 39.6 125 68.7 117 28.5 124 33.0 108 65.3 85 5.09 102 39.6 74 79.2 46 3.51 107
Local-TV-L1 [65]71.8 14.9 98 37.3 35 3.21 114 27.3 98 39.5 75 4.67 103 18.9 46 32.3 45 1.70 41 61.3 109 68.6 67 47.1 123 79.3 54 87.6 71 43.6 85 39.0 117 66.7 20 28.9 126 31.7 11 64.3 35 4.79 67 39.3 42 79.1 43 3.41 99
F-TV-L1 [15]72.4 15.0 99 39.3 96 2.88 109 27.2 96 40.2 89 3.69 87 19.2 72 34.5 93 2.19 94 59.7 50 68.4 50 42.8 87 78.9 3 87.4 12 42.7 9 37.3 90 67.0 40 25.6 79 32.1 61 64.5 45 4.89 85 40.1 98 80.0 94 3.42 100
IAOF [50]72.4 15.5 111 39.2 95 2.93 111 29.4 113 43.0 115 5.18 112 17.8 8 33.0 59 2.04 80 60.8 96 68.9 83 42.2 60 79.2 30 87.4 12 43.3 46 36.8 57 67.2 59 25.1 35 32.7 99 65.6 94 4.67 21 40.0 92 80.0 94 3.20 56
TCOF [69]73.5 14.4 83 39.3 96 1.83 33 27.3 98 40.9 101 3.35 81 18.7 36 32.1 39 1.50 10 60.2 83 70.2 109 42.1 49 79.3 54 87.6 71 43.2 37 36.9 64 68.5 114 24.8 13 33.3 111 65.8 95 4.72 42 41.2 120 81.4 122 3.46 102
BriefMatch [124]74.5 14.0 47 37.0 28 2.17 74 25.6 58 38.8 57 3.98 92 19.7 102 33.0 59 2.69 108 61.1 105 69.0 87 46.4 120 79.3 54 87.6 71 43.8 107 40.5 128 67.9 98 30.6 128 31.8 20 64.0 29 4.94 89 39.0 17 78.8 26 3.34 85
IIOF-NLDP [131]75.9 14.1 57 38.2 66 1.62 5 26.1 69 39.9 83 2.98 55 19.3 79 32.1 39 1.77 54 60.6 93 69.4 97 43.2 101 79.3 54 87.5 37 43.6 85 37.8 105 68.6 115 25.6 79 34.1 127 69.5 125 5.66 126 39.9 87 79.6 77 3.01 6
CNN-flow-warp+ref [117]76.0 13.8 29 36.0 5 2.35 91 26.6 81 39.8 79 3.83 89 20.0 108 35.5 106 2.34 99 60.9 98 68.9 83 43.0 98 79.4 84 87.6 71 43.7 100 36.8 57 67.0 40 25.6 79 32.1 61 66.2 103 4.94 89 39.4 54 79.5 70 3.19 49
Adaptive [20]76.0 14.5 87 39.6 105 2.31 89 27.1 93 40.4 94 3.35 81 18.6 32 33.7 73 1.98 75 59.6 43 68.2 41 42.4 75 79.4 84 87.6 71 43.4 61 37.1 79 67.5 80 25.7 89 32.4 86 64.8 57 4.73 46 40.0 92 80.1 98 3.38 94
FlowNetS+ft+v [112]76.8 14.7 93 38.1 61 2.80 105 27.5 100 40.6 98 4.81 106 19.6 95 34.9 98 2.07 81 60.1 79 69.5 101 42.2 60 79.4 84 87.7 96 43.4 61 36.6 31 67.1 50 25.2 45 32.0 49 65.4 87 4.73 46 39.6 74 79.7 84 3.21 60
SPSA-learn [13]77.4 14.8 97 37.8 51 2.72 101 27.6 101 40.1 87 4.71 104 20.5 112 33.7 73 2.97 115 60.4 87 67.6 18 41.5 11 79.3 54 87.5 37 43.5 68 36.8 57 67.2 59 25.2 45 33.4 113 70.8 131 6.21 130 39.7 80 79.6 77 3.19 49
AdaConv-v1 [126]78.0 16.5 119 42.3 117 4.36 121 30.4 119 43.8 118 9.06 127 20.6 115 36.3 114 4.45 127 64.5 125 71.3 123 45.3 115 78.4 1 86.7 1 42.2 3 36.3 9 64.9 2 25.4 71 32.5 91 63.7 18 5.53 122 38.0 2 77.4 2 3.53 109
LDOF [28]78.6 15.0 99 38.8 80 2.92 110 28.0 106 41.1 103 5.03 109 19.7 102 34.8 97 2.15 90 60.0 73 68.9 83 42.6 83 79.4 84 87.6 71 43.5 68 36.9 64 66.8 27 25.5 74 31.9 35 65.1 72 4.73 46 39.5 67 79.6 77 3.23 67
ROF-ND [107]79.0 15.1 103 37.9 56 1.86 42 26.3 76 40.5 97 3.12 68 19.6 95 32.8 54 1.68 39 60.9 98 71.1 122 41.9 37 79.3 54 87.5 37 43.5 68 37.0 73 68.2 107 24.9 15 34.3 128 68.3 118 5.28 111 40.5 114 80.5 112 3.25 71
HBpMotionGpu [43]79.1 15.8 114 40.2 110 3.66 119 29.5 114 42.8 114 6.27 117 18.5 23 31.9 34 1.73 47 61.3 109 69.9 106 43.9 107 79.1 11 87.6 71 43.0 16 37.6 100 67.6 86 25.9 96 32.0 49 64.3 35 4.67 21 40.0 92 79.9 90 3.75 118
CRTflow [80]79.2 14.4 83 38.9 86 2.38 92 26.0 66 39.0 62 3.14 69 20.2 110 36.2 113 2.37 101 60.5 90 69.5 101 44.1 108 79.3 54 87.5 37 43.4 61 37.1 79 67.3 68 25.7 89 32.0 49 64.6 49 4.85 81 39.6 74 79.6 77 3.45 101
Modified CLG [34]79.5 14.1 57 37.6 45 2.33 90 28.5 110 41.4 107 5.68 113 19.6 95 35.8 109 2.31 98 60.2 83 68.6 67 42.1 49 79.4 84 87.5 37 43.5 68 36.7 45 67.2 59 25.2 45 32.3 80 66.0 99 4.76 62 40.2 102 80.4 109 3.40 96
Occlusion-TV-L1 [63]79.5 14.3 78 39.1 92 2.21 83 26.6 81 40.0 85 3.14 69 19.2 72 34.2 88 2.15 90 60.0 73 68.5 55 42.8 87 79.3 54 87.5 37 43.6 85 37.5 95 67.0 40 26.2 104 32.9 104 65.1 72 5.16 106 40.0 92 79.8 86 3.30 81
CBF [12]80.8 13.7 20 37.2 34 2.15 73 26.0 66 39.4 73 3.28 78 19.1 62 32.1 39 1.79 58 61.0 103 70.0 108 45.8 117 79.6 112 87.8 109 44.9 120 36.8 57 67.4 73 25.2 45 32.2 74 65.5 91 5.22 108 40.0 92 80.2 105 3.99 123
TriangleFlow [30]82.6 14.7 93 40.0 109 2.29 88 26.6 81 40.8 99 3.03 60 19.4 85 33.3 67 2.10 85 60.4 87 69.9 106 42.8 87 79.0 5 87.4 12 42.6 6 37.7 101 68.3 110 25.3 61 33.1 109 67.8 113 5.24 110 40.4 110 80.6 114 3.32 84
2D-CLG [1]82.8 14.5 87 37.6 45 2.76 102 29.8 116 42.4 111 6.69 121 19.7 102 35.2 103 2.74 111 60.7 94 68.7 73 41.5 11 79.4 84 87.7 96 43.5 68 36.6 31 67.0 40 25.1 35 32.5 91 66.7 107 4.90 87 40.2 102 80.1 98 3.25 71
Nguyen [33]83.2 15.6 112 38.5 72 3.62 118 30.1 118 43.2 116 6.04 115 19.6 95 36.3 114 2.25 96 61.1 105 69.4 97 42.0 45 79.2 30 87.5 37 43.1 21 36.4 14 67.2 59 24.7 12 34.3 128 67.4 112 5.00 95 40.2 102 80.3 106 3.29 79
BlockOverlap [61]83.3 15.1 103 37.6 45 3.31 116 27.7 103 39.3 66 5.73 114 18.6 32 30.3 4 2.09 84 60.9 98 68.2 41 47.1 123 80.2 122 87.9 110 46.5 125 39.0 117 67.3 68 28.4 123 31.9 35 63.9 24 5.09 102 39.7 80 79.3 54 3.55 110
SegOF [10]83.7 14.2 66 36.8 24 2.54 96 27.0 92 40.0 85 4.18 93 21.1 118 36.1 112 3.15 120 60.5 90 70.7 118 41.6 17 79.4 84 87.6 71 43.6 85 36.9 64 68.2 107 25.2 45 32.5 91 68.0 117 5.31 115 39.6 74 79.4 62 3.22 62
ACK-Prior [27]84.0 13.8 29 38.1 61 1.74 22 25.5 50 39.3 66 2.82 43 19.6 95 33.8 79 2.45 105 60.5 90 70.3 111 42.3 68 80.2 122 88.0 116 45.8 123 38.2 107 67.8 93 26.9 115 32.6 95 66.2 103 5.35 117 38.9 9 79.7 84 3.60 114
IAOF2 [51]84.7 15.6 112 41.3 113 2.58 98 27.6 101 41.4 107 4.29 96 17.8 8 33.6 70 1.94 71 61.2 108 70.8 119 42.8 87 79.4 84 87.7 96 43.3 46 37.2 86 67.5 80 25.6 79 32.3 80 65.0 65 4.63 6 40.6 115 80.4 109 3.40 96
StereoOF-V1MT [119]85.8 14.6 91 39.9 107 2.00 63 27.2 96 41.9 109 3.04 61 20.9 117 37.8 120 2.85 114 61.3 109 68.3 47 43.8 105 79.2 30 87.5 37 42.9 11 38.2 107 67.8 93 26.3 108 33.8 120 68.5 119 5.36 118 40.0 92 79.4 62 3.09 24
Dynamic MRF [7]86.2 13.9 40 38.6 74 1.90 49 26.1 69 40.4 94 3.08 64 20.0 108 37.7 119 2.73 110 61.3 109 69.3 92 44.6 110 79.1 11 87.6 71 43.0 16 37.7 101 68.0 101 25.9 96 32.6 95 67.2 110 5.08 101 40.4 110 80.5 112 3.49 106
TV-L1-improved [17]86.3 14.2 66 38.8 80 2.25 85 26.9 90 40.3 91 3.40 85 19.5 92 33.9 80 2.44 104 59.9 68 69.0 87 42.7 86 79.4 84 87.7 96 43.5 68 37.2 86 67.6 86 25.8 93 32.1 61 66.1 101 5.05 99 39.9 87 80.0 94 3.46 102
Correlation Flow [75]86.4 14.0 47 38.3 68 1.61 3 26.2 73 39.8 79 2.98 55 19.1 62 31.9 34 1.73 47 60.4 87 69.4 97 43.6 104 80.2 122 87.9 110 47.8 128 38.0 106 68.7 117 26.0 100 33.4 113 67.2 110 5.29 112 40.1 98 80.3 106 3.39 95
Black & Anandan [4]89.3 15.3 107 38.8 80 2.96 112 28.4 108 40.9 101 4.78 105 20.5 112 35.2 103 2.74 111 60.9 98 69.3 92 42.1 49 79.4 84 87.7 96 43.6 85 37.1 79 66.6 16 25.6 79 32.9 104 65.9 98 4.72 42 40.3 105 80.3 106 3.25 71
Rannacher [23]89.6 14.4 83 39.3 96 2.38 92 26.9 90 40.4 94 3.36 84 19.5 92 34.6 94 2.58 107 59.8 59 68.8 79 42.8 87 79.4 84 87.7 96 43.6 85 37.2 86 67.8 93 25.8 93 32.2 74 66.0 99 5.02 96 39.9 87 79.9 90 3.56 111
LocallyOriented [52]91.4 15.0 99 40.3 111 2.53 95 27.7 103 41.3 105 3.86 90 19.4 85 34.4 91 1.95 72 61.1 105 70.6 115 43.3 102 79.2 30 87.5 37 43.3 46 39.1 121 68.1 105 27.6 120 32.9 104 65.8 95 4.72 42 40.6 115 80.6 114 3.37 92
UnFlow [129]92.8 16.0 116 42.8 119 2.87 108 30.6 121 45.2 126 4.52 101 21.3 121 39.4 125 2.81 113 60.0 73 68.3 47 42.1 49 79.2 30 87.4 12 43.5 68 37.5 95 68.0 101 25.2 45 33.8 120 65.1 72 4.98 93 43.2 130 81.8 125 3.67 116
Filter Flow [19]96.7 15.0 99 39.4 101 2.78 104 28.4 108 40.8 99 6.31 118 18.5 23 32.9 56 2.14 88 61.7 113 69.3 92 45.3 115 79.7 115 88.0 116 44.5 117 37.3 90 67.7 90 26.1 102 32.1 61 65.2 81 4.93 88 40.3 105 80.7 117 3.97 122
StereoFlow [44]98.3 22.8 130 51.1 131 4.80 122 36.2 130 51.1 131 6.57 120 19.2 72 34.6 94 1.89 68 60.0 73 68.5 55 42.4 75 80.3 125 89.1 130 43.9 110 39.0 117 74.1 131 25.3 61 32.1 61 65.0 65 4.73 46 40.3 105 80.9 118 3.36 89
Ad-TV-NDC [36]101.8 17.2 121 39.9 107 5.26 124 29.6 115 42.1 110 6.18 116 19.2 72 33.7 73 1.98 75 62.4 115 70.3 111 45.2 114 79.6 112 87.9 110 43.9 110 38.3 110 67.3 68 27.2 119 32.3 80 65.5 91 4.80 73 40.3 105 80.1 98 3.58 113
Bartels [41]103.0 14.6 91 39.3 96 2.80 105 26.1 69 39.7 77 4.45 99 19.0 52 33.2 65 2.14 88 62.1 114 70.9 120 48.9 125 80.7 129 88.1 119 49.2 130 43.7 130 69.0 123 34.8 130 32.4 86 65.0 65 5.76 127 40.4 110 80.1 98 4.26 125
TI-DOFE [24]104.3 17.9 124 43.0 120 5.41 125 32.3 126 46.2 128 7.98 125 20.5 112 38.1 123 2.97 115 63.1 122 70.6 115 43.8 105 79.1 11 87.6 71 43.1 21 37.7 101 67.4 73 25.8 93 33.4 113 67.8 113 5.09 102 41.6 124 81.5 124 3.68 117
Horn & Schunck [3]105.2 15.3 107 40.4 112 2.69 100 29.0 111 42.7 112 5.10 110 21.1 118 37.9 121 3.33 121 62.5 117 70.3 111 43.0 98 79.3 54 87.7 96 43.6 85 37.5 95 67.3 68 25.9 96 33.9 124 68.5 119 5.03 97 41.2 120 81.2 121 3.57 112
GroupFlow [9]107.5 16.8 120 43.4 121 3.43 117 29.1 112 43.9 119 5.11 111 22.2 124 39.3 124 3.53 122 61.0 103 70.6 115 42.5 80 79.7 115 88.1 119 44.0 113 39.0 117 69.4 127 26.8 114 32.8 101 66.8 108 4.87 83 40.4 110 80.1 98 3.01 6
2bit-BM-tele [98]109.3 15.3 107 39.5 103 3.22 115 27.8 105 41.2 104 4.90 107 18.8 39 32.5 50 2.34 99 62.4 115 71.0 121 49.0 126 80.6 127 88.2 124 47.9 129 42.8 129 69.3 126 32.9 129 33.4 113 70.0 129 6.77 131 40.3 105 79.4 62 4.33 128
SLK [47]109.8 17.4 122 43.9 123 4.90 123 30.5 120 44.0 120 7.18 123 22.5 125 39.8 126 4.15 126 64.5 125 70.5 114 46.7 121 78.9 3 87.7 96 41.6 1 38.5 112 68.8 119 26.0 100 33.8 120 70.1 130 5.50 121 41.6 124 81.4 122 3.91 120
SILK [79]110.6 16.3 117 42.0 116 4.01 120 29.9 117 43.5 117 6.44 119 21.6 122 37.4 118 3.55 123 62.6 118 69.4 97 47.0 122 79.3 54 87.7 96 43.6 85 39.9 126 68.1 105 29.2 127 32.8 101 67.8 113 5.14 105 40.6 115 80.6 114 3.52 108
NL-TV-NCC [25]111.0 15.1 103 41.6 115 1.86 42 26.6 81 41.3 105 3.02 59 20.8 116 35.7 107 2.24 95 63.2 123 73.9 127 45.9 118 81.3 131 88.7 129 49.9 131 38.6 113 69.8 129 25.6 79 37.6 131 69.5 125 5.62 124 42.4 129 82.1 128 4.00 124
HCIC-L [99]112.2 23.2 131 49.0 130 11.0 131 32.1 125 44.4 121 9.93 128 23.2 127 36.4 116 3.02 117 64.4 124 72.1 124 44.9 111 80.6 127 88.5 127 46.6 126 39.1 121 68.9 121 27.1 117 32.4 86 65.0 65 5.53 122 39.1 26 79.3 54 3.65 115
Heeger++ [104]113.3 17.5 123 47.2 129 2.80 105 31.1 123 44.9 124 4.93 108 26.6 129 47.7 130 4.79 128 62.6 118 68.0 36 45.1 112 79.8 119 88.4 126 44.1 114 39.1 121 68.9 121 26.5 110 34.8 130 67.9 116 5.23 109 41.5 123 80.1 98 3.23 67
FFV1MT [106]115.3 16.4 118 44.7 125 3.13 113 31.9 124 44.7 122 7.15 122 25.4 128 45.6 129 5.04 129 62.6 118 68.0 36 45.1 112 79.6 112 87.9 110 44.1 114 38.9 116 67.7 90 27.1 117 34.0 126 68.5 119 5.29 112 41.8 127 81.0 119 4.48 130
Learning Flow [11]115.6 15.3 107 42.7 118 2.55 97 28.0 106 42.7 112 3.95 91 21.1 118 37.0 117 3.03 119 63.0 121 73.3 126 46.2 119 80.0 120 88.2 124 45.1 121 38.2 107 68.6 115 26.7 112 33.8 120 68.5 119 5.21 107 41.9 128 82.3 129 3.95 121
Adaptive flow [45]118.7 19.6 126 44.1 124 6.76 126 32.8 127 45.7 127 10.2 129 19.8 106 34.4 91 3.02 117 64.7 127 72.1 124 49.4 127 80.3 125 88.6 128 45.6 122 38.3 110 69.2 124 26.7 112 32.6 95 66.5 106 5.45 120 41.0 118 81.1 120 3.75 118
Pyramid LK [2]120.4 21.2 129 43.7 122 10.7 130 33.1 129 45.1 125 11.9 130 27.3 130 36.0 110 6.46 130 70.7 131 78.5 131 57.7 131 79.5 109 88.1 119 43.3 46 38.6 113 68.8 119 27.0 116 33.5 118 68.8 123 6.00 128 41.0 118 81.8 125 4.31 127
FOLKI [16]123.7 20.9 127 46.0 126 9.48 129 32.8 127 47.4 129 8.75 126 21.6 122 40.7 127 4.10 125 67.2 130 74.2 129 53.7 130 79.5 109 88.1 119 43.7 100 39.2 124 69.2 124 27.9 121 33.4 113 69.5 125 5.65 125 41.7 126 82.3 129 4.28 126
PGAM+LK [55]123.8 19.4 125 46.4 127 6.81 127 30.9 122 44.8 123 7.52 124 22.7 126 40.9 128 3.99 124 66.6 129 73.9 127 52.4 129 79.7 115 88.1 119 44.5 117 40.2 127 69.7 128 28.8 125 33.3 111 69.3 124 5.42 119 41.4 122 81.8 125 4.36 129
Periodicity [78]129.3 21.0 128 47.0 128 9.32 128 38.1 131 48.1 130 14.7 131 29.8 131 47.9 131 9.27 131 66.0 128 77.1 130 50.7 128 80.8 130 89.3 131 46.8 127 45.1 131 70.6 130 35.5 131 33.5 118 69.6 128 6.07 129 43.5 131 84.0 131 6.51 131
Move the mouse over the numbers in the table to see the corresponding images. Click to compare with the ground truth.

References

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