Optical flow evaluation results Statistics:     Average   SD   R0.5   R1.0   R2.0   A90   A95   A99  
Error type:   endpoint   angle   interpolation   normalized interpolation  
Show images: below table   above table        
R0.5
normalized 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
MDP-Flow2 [68]9.9 35.7 2 30.6 2 47.8 9 25.9 10 30.5 10 36.9 3 28.6 3 29.8 5 38.5 2 51.9 7 46.5 15 80.3 10 71.9 4 66.6 3 87.2 8 68.6 5 53.9 23 82.1 35 28.1 2 43.6 8 42.4 10 36.6 27 55.6 28 50.0 7
PMMST [114]11.0 35.8 4 30.8 3 47.9 19 26.5 32 31.0 20 37.3 36 28.6 3 29.9 6 38.4 1 51.7 3 46.0 5 80.2 5 72.0 9 66.7 8 87.3 16 68.5 1 53.3 7 82.0 13 28.1 2 43.7 13 42.4 10 36.5 18 55.5 23 50.0 7
PH-Flow [101]11.7 36.1 21 32.5 29 47.8 9 25.6 4 29.6 4 36.9 3 28.7 9 30.0 8 38.5 2 51.6 1 45.5 1 80.2 5 71.9 4 66.7 8 87.3 16 68.8 34 54.8 65 81.9 5 28.1 2 43.6 8 42.4 10 36.4 12 55.3 14 50.0 7
NNF-Local [87]12.1 35.7 2 31.4 6 47.6 3 25.5 2 29.6 4 36.9 3 28.6 3 29.9 6 38.5 2 52.4 31 48.0 62 80.3 10 72.0 9 66.6 3 87.4 34 68.7 19 54.4 44 82.0 13 28.1 2 43.5 6 42.4 10 36.2 4 55.0 6 50.0 7
NN-field [71]17.2 36.0 16 32.2 18 47.9 19 25.5 2 29.3 3 36.8 1 29.4 60 29.7 4 39.0 43 52.4 31 48.1 68 80.3 10 72.0 9 66.7 8 87.3 16 68.7 19 54.0 28 82.0 13 28.1 2 43.4 4 42.4 10 36.4 12 55.2 10 50.0 7
IROF++ [58]20.9 36.2 28 33.0 43 47.8 9 26.1 15 30.9 17 36.9 3 29.1 30 31.0 29 38.9 28 51.6 1 45.6 2 80.4 19 72.0 9 66.8 17 87.2 8 68.6 5 53.4 8 82.2 56 28.3 17 44.6 39 42.4 10 36.5 18 55.3 14 50.4 76
ProbFlowFields [128]21.1 35.9 9 32.4 23 48.0 29 25.8 8 30.5 10 37.2 31 28.6 3 30.3 11 38.5 2 52.1 20 46.4 11 80.7 55 72.3 65 67.1 66 87.5 77 68.6 5 53.8 15 82.1 35 28.0 1 42.8 1 42.3 1 36.1 2 54.6 2 50.1 25
Sparse-NonSparse [56]22.5 36.2 28 32.8 37 48.0 29 25.9 10 30.4 8 37.0 11 29.0 22 30.9 26 38.8 13 52.0 13 46.1 7 80.6 41 72.1 24 66.8 17 87.3 16 68.9 44 54.6 55 82.1 35 28.3 17 44.0 21 42.4 10 36.4 12 55.4 18 50.1 25
CombBMOF [113]23.8 35.9 9 31.0 4 47.8 9 25.8 8 30.5 10 36.8 1 29.2 37 30.8 23 39.5 80 52.4 31 47.4 41 80.3 10 72.1 24 66.8 17 87.4 34 68.9 44 54.5 48 82.1 35 28.5 57 44.6 39 42.3 1 36.0 1 54.6 2 50.0 7
nLayers [57]25.7 36.4 53 32.0 13 48.2 57 26.0 13 30.4 8 37.3 36 28.7 9 29.4 2 38.8 13 52.2 27 46.8 22 80.4 19 72.3 65 67.1 66 87.4 34 68.8 34 54.7 59 82.0 13 28.3 17 43.7 13 42.4 10 36.4 12 55.4 18 49.9 3
AGIF+OF [85]25.7 36.2 28 32.8 37 47.9 19 26.1 15 30.8 14 37.1 18 29.0 22 30.7 19 38.9 28 51.8 5 46.2 9 80.1 3 72.3 65 67.2 79 87.3 16 68.9 44 55.2 81 81.9 5 28.3 17 43.6 8 42.4 10 36.6 27 56.0 45 49.9 3
2DHMM-SAS [92]27.4 36.4 53 33.9 80 47.9 19 27.1 56 32.6 43 37.0 11 28.5 2 30.4 14 38.9 28 51.8 5 45.6 2 80.4 19 72.1 24 66.9 29 87.4 34 68.8 34 54.5 48 82.0 13 28.3 17 44.2 27 42.3 1 36.7 39 56.1 53 50.0 7
NNF-EAC [103]28.0 36.3 41 32.4 23 48.0 29 26.6 35 31.7 30 37.1 18 29.3 47 30.2 9 39.0 43 52.4 31 46.9 25 81.1 96 72.0 9 66.7 8 87.4 34 68.7 19 53.7 14 82.1 35 28.2 11 43.9 16 42.4 10 36.7 39 55.9 42 50.0 7
Layers++ [37]28.5 36.3 41 32.4 23 48.2 57 25.7 5 29.2 2 37.3 36 28.9 18 30.6 16 38.9 28 52.0 13 46.4 11 80.4 19 72.2 36 67.0 42 87.5 77 68.9 44 55.2 81 82.0 13 28.3 17 44.0 21 42.4 10 36.6 27 55.5 23 50.1 25
LSM [39]30.0 36.3 41 33.7 70 48.0 29 26.1 15 31.0 20 37.0 11 29.1 30 31.8 45 38.9 28 52.2 27 46.9 25 80.6 41 72.1 24 66.9 29 87.3 16 69.0 58 54.9 68 82.1 35 28.3 17 44.1 25 42.4 10 36.5 18 55.7 32 50.0 7
ComponentFusion [96]30.5 36.0 16 32.2 18 48.0 29 26.1 15 31.1 25 36.9 3 29.1 30 32.3 53 38.8 13 52.0 13 47.0 29 80.3 10 72.2 36 67.1 66 87.3 16 68.7 19 53.9 23 82.1 35 28.5 57 46.1 91 42.4 10 36.7 39 55.8 39 50.2 46
FlowFields [110]30.6 36.0 16 32.7 34 47.9 19 26.4 29 32.0 32 37.3 36 29.0 22 32.6 59 38.7 8 52.5 36 47.9 59 80.7 55 72.3 65 67.0 42 87.5 77 68.6 5 54.4 44 82.0 13 28.2 11 44.0 21 42.4 10 36.3 6 55.2 10 50.1 25
S2F-IF [123]31.4 35.9 9 32.5 29 47.8 9 26.2 22 31.6 29 37.2 31 29.0 22 31.9 50 38.6 7 52.3 29 47.6 46 80.4 19 72.4 91 67.2 79 87.5 77 68.7 19 54.5 48 81.9 5 28.4 38 44.7 44 42.4 10 36.3 6 55.2 10 50.1 25
TV-L1-MCT [64]31.5 36.8 84 34.7 100 48.2 57 26.7 36 32.4 40 37.3 36 28.6 3 30.9 26 39.0 43 51.9 7 45.7 4 80.5 35 72.2 36 67.0 42 87.3 16 68.6 5 53.0 4 82.3 71 28.3 17 44.4 32 42.4 10 36.1 2 54.9 5 50.2 46
FlowFields+ [130]31.7 35.9 9 32.6 33 47.9 19 26.4 29 32.2 35 37.4 47 29.0 22 32.6 59 38.7 8 52.3 29 47.7 51 80.6 41 72.3 65 67.1 66 87.5 77 68.7 19 54.6 55 82.0 13 28.2 11 44.0 21 42.4 10 36.3 6 55.2 10 50.1 25
WLIF-Flow [93]32.2 36.1 21 32.5 29 47.8 9 26.3 27 31.2 26 37.1 18 29.1 30 30.7 19 39.1 50 52.0 13 46.4 11 80.6 41 72.1 24 66.8 17 87.4 34 69.0 58 54.9 68 82.3 71 28.3 17 43.9 16 42.5 61 36.8 46 55.9 42 50.1 25
LME [70]34.6 35.8 4 31.0 4 47.8 9 26.9 46 32.2 35 38.4 85 29.2 37 32.6 59 38.8 13 51.9 7 46.7 20 80.4 19 72.6 111 67.4 103 87.7 119 68.8 34 54.9 68 82.0 13 28.1 2 43.5 6 42.4 10 36.3 6 55.3 14 50.0 7
COFM [59]34.7 36.1 21 32.0 13 48.1 43 26.1 15 30.8 14 37.1 18 28.8 12 30.3 11 38.8 13 51.7 3 46.0 5 80.0 1 72.2 36 67.2 79 87.2 8 68.9 44 56.1 107 81.7 1 28.1 2 42.8 1 43.1 116 37.1 77 56.9 87 50.7 106
FMOF [94]35.5 36.5 61 33.7 70 48.2 57 25.9 10 30.3 7 37.1 18 29.3 47 30.7 19 39.0 43 52.5 36 47.5 42 80.2 5 72.2 36 67.0 42 87.5 77 69.0 58 55.1 78 82.0 13 28.1 2 43.4 4 42.4 10 36.8 46 56.0 45 50.1 25
DeepFlow2 [108]35.7 36.2 28 32.4 23 48.2 57 27.1 56 32.9 47 37.8 70 29.2 37 32.9 67 39.0 43 52.5 36 47.5 42 80.5 35 72.2 36 66.9 29 87.5 77 68.5 1 52.9 3 82.1 35 28.3 17 44.4 32 42.4 10 36.4 12 55.4 18 50.2 46
OFLAF [77]36.0 35.8 4 31.5 7 47.8 9 25.7 5 29.8 6 37.0 11 29.0 22 31.2 32 38.7 8 52.0 13 46.8 22 80.1 3 72.4 91 67.3 93 87.4 34 68.9 44 55.3 86 82.0 13 28.6 74 45.4 77 42.4 10 37.1 77 57.1 97 50.1 25
RNLOD-Flow [121]36.0 36.3 41 33.5 60 48.0 29 26.8 41 32.6 43 37.1 18 29.2 37 31.8 45 38.8 13 52.1 20 46.9 25 80.2 5 72.2 36 67.0 42 87.3 16 69.0 58 55.2 81 82.1 35 28.3 17 44.2 27 42.4 10 37.1 77 56.9 87 49.8 1
DeepFlow [86]37.1 36.1 21 31.8 11 48.1 43 27.3 61 32.9 47 38.4 85 29.3 47 33.3 80 39.1 50 52.6 48 47.0 29 80.7 55 72.2 36 66.8 17 87.5 77 68.7 19 52.8 2 82.5 96 28.1 2 43.6 8 42.3 1 36.2 4 55.0 6 50.2 46
Ramp [62]37.8 36.5 61 34.0 85 48.2 57 26.0 13 30.8 14 37.1 18 28.9 18 30.8 23 38.8 13 51.9 7 46.1 7 80.4 19 72.2 36 67.0 42 87.4 34 69.1 68 55.4 92 82.2 56 28.4 38 44.7 44 42.4 10 36.8 46 56.2 61 50.2 46
MDP-Flow [26]38.0 35.8 4 31.5 7 48.0 29 26.2 22 31.4 28 37.4 47 29.0 22 31.1 30 38.9 28 52.7 57 47.8 56 80.7 55 72.2 36 66.9 29 87.5 77 68.9 44 55.2 81 82.1 35 28.5 57 45.3 76 42.5 61 36.3 6 55.4 18 50.0 7
IROF-TV [53]39.2 36.3 41 33.6 66 48.2 57 26.2 22 31.0 20 37.0 11 29.3 47 33.6 86 39.1 50 51.9 7 46.5 15 80.8 68 72.3 65 67.0 42 87.6 110 68.5 1 53.9 23 81.9 5 28.3 17 44.9 56 42.3 1 36.6 27 55.6 28 50.4 76
PGM-C [120]39.5 36.2 28 33.3 52 48.1 43 26.5 32 32.2 35 37.5 56 29.2 37 32.9 67 38.8 13 52.5 36 48.3 81 80.7 55 72.3 65 67.0 42 87.5 77 68.6 5 54.0 28 82.0 13 28.3 17 44.6 39 42.4 10 36.5 18 55.5 23 50.4 76
Classic+NL [31]39.5 36.5 61 34.0 85 48.2 57 26.2 22 30.9 17 37.1 18 28.8 12 30.6 16 38.8 13 52.1 20 46.5 15 80.6 41 72.2 36 67.0 42 87.4 34 69.2 78 55.3 86 82.2 56 28.4 38 44.6 39 42.4 10 36.8 46 56.2 61 50.2 46
DF-Auto [115]40.1 36.8 84 31.9 12 48.9 94 28.5 87 33.7 65 40.8 99 28.8 12 30.3 11 38.7 8 52.5 36 47.3 35 80.4 19 72.1 24 66.7 8 87.4 34 68.6 5 53.8 15 82.0 13 28.4 38 44.7 44 42.5 61 36.8 46 56.3 66 50.2 46
FC-2Layers-FF [74]42.3 36.4 53 33.8 76 48.1 43 25.7 5 29.1 1 37.4 47 28.9 18 30.9 26 38.8 13 52.1 20 46.8 22 80.6 41 72.3 65 67.2 79 87.4 34 69.1 68 55.5 94 82.1 35 28.4 38 44.7 44 42.5 61 36.9 60 56.3 66 50.0 7
SuperFlow [81]42.9 36.5 61 32.2 18 48.8 91 28.3 82 33.4 60 40.9 100 29.5 69 32.6 59 39.4 73 52.5 36 46.7 20 80.9 76 72.2 36 66.9 29 87.5 77 68.5 1 53.4 8 82.0 13 28.3 17 44.7 44 42.4 10 36.3 6 55.4 18 50.1 25
HAST [109]43.5 36.1 21 31.7 9 48.1 43 26.1 15 31.0 20 37.0 11 29.3 47 31.7 41 39.2 61 51.9 7 46.5 15 80.3 10 72.3 65 67.3 93 87.2 8 69.3 89 56.4 115 82.0 13 28.4 38 45.0 63 42.5 61 37.3 91 57.3 101 50.0 7
CPM-Flow [116]44.3 36.2 28 33.5 60 48.1 43 26.5 32 32.2 35 37.5 56 29.3 47 32.6 59 38.9 28 52.7 57 48.7 94 80.7 55 72.3 65 67.0 42 87.5 77 68.7 19 53.8 15 82.2 56 28.4 38 44.7 44 42.4 10 36.5 18 55.5 23 50.3 63
Second-order prior [8]44.4 36.2 28 32.1 16 48.1 43 27.9 76 34.1 71 37.4 47 29.9 86 34.6 101 39.7 90 52.4 31 47.2 33 80.6 41 71.9 4 66.6 3 87.5 77 68.7 19 54.0 28 82.1 35 28.5 57 45.2 74 42.4 10 36.5 18 55.7 32 50.2 46
Aniso. Huber-L1 [22]45.4 36.7 78 33.5 60 48.6 85 28.5 87 34.3 74 38.2 81 29.3 47 31.8 45 38.9 28 52.5 36 47.5 42 80.6 41 72.0 9 66.7 8 87.4 34 68.6 5 54.3 40 81.9 5 28.5 57 45.0 63 42.4 10 36.8 46 56.0 45 50.3 63
Classic+CPF [83]45.5 36.4 53 33.6 66 47.9 19 26.3 27 31.3 27 37.0 11 28.8 12 31.1 30 38.9 28 52.0 13 46.5 15 80.0 1 72.5 103 67.4 103 87.4 34 69.2 78 56.1 107 82.0 13 28.6 74 45.2 74 42.4 10 37.2 85 57.3 101 50.0 7
RFlow [90]46.6 36.2 28 33.0 43 48.2 57 27.6 67 33.7 65 37.1 18 29.3 47 32.5 58 39.2 61 52.6 48 47.8 56 80.6 41 72.0 9 66.8 17 87.3 16 68.6 5 53.8 15 81.9 5 28.5 57 45.5 82 42.6 89 37.2 85 56.9 87 50.3 63
EpicFlow [102]46.8 36.2 28 33.3 52 48.1 43 26.9 46 33.1 53 37.5 56 29.4 60 33.0 72 39.0 43 52.6 48 48.5 85 80.8 68 72.3 65 67.0 42 87.5 77 68.6 5 54.1 32 82.0 13 28.4 38 44.8 53 42.4 10 36.6 27 55.7 32 50.4 76
Brox et al. [5]47.6 36.3 41 32.4 23 48.2 57 27.8 74 34.1 71 38.0 78 29.8 81 33.9 92 39.6 88 52.5 36 47.0 29 80.4 19 72.2 36 66.9 29 87.5 77 68.7 19 53.8 15 82.1 35 28.4 38 44.9 56 42.5 61 36.5 18 55.5 23 50.2 46
S2D-Matching [84]48.3 36.6 70 34.2 91 48.2 57 26.9 46 32.5 42 37.2 31 28.8 12 30.7 19 38.9 28 52.1 20 46.4 11 80.9 76 72.3 65 67.1 66 87.5 77 69.1 68 55.3 86 82.2 56 28.5 57 44.7 44 42.4 10 36.7 39 55.9 42 50.2 46
FESL [72]49.0 36.6 70 33.9 80 48.0 29 26.4 29 31.7 30 37.3 36 29.1 30 31.3 33 38.9 28 52.6 48 47.6 46 80.3 10 72.4 91 67.3 93 87.4 34 69.3 89 55.9 102 82.1 35 28.4 38 44.9 56 42.3 1 37.0 67 56.6 77 50.1 25
p-harmonic [29]49.1 35.9 9 32.1 16 47.9 19 28.2 79 34.3 74 37.8 70 29.4 60 34.2 96 39.4 73 53.0 79 47.7 51 80.7 55 72.2 36 67.0 42 87.3 16 68.8 34 54.1 32 82.3 71 28.5 57 45.5 82 42.4 10 36.6 27 56.0 45 50.2 46
SepConv-v1 [127]49.7 27.1 1 27.5 1 36.4 1 24.9 1 31.0 20 40.3 94 27.6 1 28.4 1 48.9 128 54.0 104 47.3 35 83.0 119 72.0 9 66.7 8 87.1 3 69.1 68 52.6 1 83.6 124 32.2 130 43.9 16 55.7 130 37.0 67 53.8 1 55.4 130
ComplOF-FED-GPU [35]50.3 36.3 41 33.4 57 48.0 29 26.8 41 33.0 50 37.3 36 30.4 100 34.0 93 39.6 88 52.5 36 48.1 68 80.9 76 72.1 24 66.8 17 87.4 34 68.7 19 54.3 40 82.1 35 28.5 57 45.1 69 42.5 61 36.8 46 56.0 45 50.2 46
TC-Flow [46]51.0 36.2 28 33.2 49 48.2 57 26.9 46 33.5 62 37.5 56 29.5 69 33.6 86 38.9 28 52.1 20 47.1 32 80.6 41 72.3 65 67.2 79 87.5 77 69.0 58 54.8 65 82.3 71 28.4 38 44.4 32 42.5 61 36.6 27 56.1 53 50.1 25
EPPM w/o HM [88]51.5 35.8 4 32.3 22 47.6 3 26.7 36 33.0 50 36.9 3 30.0 89 35.5 111 39.4 73 52.6 48 48.9 100 80.4 19 72.2 36 67.1 66 87.4 34 69.3 89 55.9 102 82.3 71 28.4 38 44.9 56 42.5 61 36.8 46 56.1 53 50.1 25
DPOF [18]52.0 36.7 78 34.5 95 48.6 85 26.1 15 30.6 13 37.6 62 29.8 81 31.4 34 39.3 68 52.8 67 48.6 88 80.8 68 72.0 9 66.8 17 87.3 16 69.1 68 55.3 86 81.9 5 28.5 57 44.5 36 42.5 61 36.9 60 56.5 73 50.0 7
OFH [38]53.5 36.4 53 33.8 76 48.2 57 27.4 63 33.3 58 37.4 47 29.7 76 35.0 106 39.0 43 52.5 36 48.3 81 80.9 76 72.2 36 67.0 42 87.4 34 68.7 19 54.2 38 82.1 35 28.6 74 45.4 77 42.5 61 36.6 27 56.0 45 50.1 25
Efficient-NL [60]53.9 36.5 61 33.6 66 48.0 29 26.7 36 32.0 32 37.1 18 29.9 86 31.4 34 39.3 68 52.7 57 47.7 51 80.4 19 72.2 36 67.0 42 87.3 16 69.5 100 57.0 120 81.9 5 28.6 74 45.9 88 42.4 10 37.9 107 58.1 114 50.1 25
Local-TV-L1 [65]55.5 37.5 100 33.0 43 49.7 106 29.3 99 34.5 83 40.3 94 29.2 37 31.6 39 39.1 50 53.3 90 47.3 35 83.1 122 72.1 24 66.9 29 87.4 34 69.3 89 53.4 8 83.2 118 28.2 11 43.9 16 42.4 10 36.4 12 55.1 8 50.4 76
PMF [73]55.8 35.9 9 32.0 13 47.7 6 26.9 46 33.5 62 36.9 3 29.6 74 34.5 99 39.1 50 52.5 36 47.8 56 80.4 19 72.5 103 67.5 109 87.4 34 69.2 78 55.0 76 82.4 85 28.5 57 45.0 63 42.5 61 37.3 91 57.3 101 50.0 7
Sparse Occlusion [54]56.6 36.5 61 33.7 70 48.2 57 27.6 67 34.1 71 37.3 36 29.3 47 31.8 45 38.8 13 52.8 67 48.1 68 80.5 35 72.3 65 67.1 66 87.4 34 69.2 78 56.1 107 82.0 13 28.5 57 45.4 77 42.3 1 37.2 85 57.0 93 50.2 46
TC/T-Flow [76]56.6 36.6 70 33.7 70 47.9 19 26.8 41 32.9 47 37.1 18 29.1 30 31.9 50 38.8 13 52.7 57 48.5 85 80.4 19 72.5 103 67.4 103 87.5 77 69.1 68 55.2 81 82.1 35 28.6 74 45.4 77 42.5 61 37.0 67 56.9 87 50.0 7
OAR-Flow [125]56.6 36.5 61 33.0 43 48.4 75 27.0 52 33.0 50 37.8 70 29.2 37 33.3 80 38.9 28 52.1 20 47.6 46 80.5 35 72.4 91 67.3 93 87.6 110 68.9 44 54.5 48 82.2 56 28.5 57 44.7 44 42.4 10 36.9 60 56.5 73 50.4 76
TF+OM [100]57.0 36.3 41 33.0 43 48.5 78 26.9 46 32.2 35 39.2 89 28.6 3 32.4 56 38.9 28 52.8 67 48.2 79 80.7 55 72.3 65 67.1 66 87.4 34 69.0 58 54.5 48 82.3 71 28.4 38 45.1 69 42.5 61 37.0 67 56.4 70 50.6 100
SRR-TVOF-NL [91]57.4 36.6 70 33.5 60 48.2 57 27.7 70 34.3 74 37.9 74 29.5 69 33.2 76 39.1 50 53.1 83 48.1 68 80.2 5 72.2 36 67.1 66 87.3 16 68.9 44 55.7 98 81.8 3 28.5 57 44.9 56 42.4 10 37.5 98 58.0 113 50.1 25
SIOF [67]58.6 36.7 78 34.1 87 48.2 57 29.1 95 35.4 101 39.7 91 29.4 60 32.9 67 39.1 50 52.7 57 47.7 51 80.9 76 71.9 4 66.6 3 87.4 34 69.1 68 54.3 40 82.4 85 28.3 17 44.6 39 42.4 10 37.3 91 56.8 83 50.3 63
CLG-TV [48]58.8 36.6 70 33.4 57 48.5 78 28.2 79 34.4 80 38.2 81 29.7 76 33.6 86 39.4 73 52.8 67 48.0 62 80.9 76 72.2 36 66.9 29 87.5 77 68.7 19 54.0 28 82.1 35 28.4 38 45.1 69 42.4 10 37.0 67 56.5 73 50.2 46
ALD-Flow [66]59.0 36.7 78 33.9 80 48.6 85 27.0 52 33.2 55 37.9 74 29.3 47 33.4 83 38.9 28 52.5 36 48.0 62 80.9 76 72.4 91 67.2 79 87.6 110 68.9 44 54.4 44 82.2 56 28.2 11 43.6 8 42.4 10 37.0 67 56.6 77 50.3 63
SimpleFlow [49]59.5 36.5 61 34.2 91 48.2 57 27.2 59 32.8 46 37.3 36 30.1 95 31.7 41 39.4 73 52.0 13 46.3 10 80.7 55 72.3 65 67.2 79 87.4 34 69.0 58 55.4 92 82.0 13 28.7 85 47.1 102 42.6 89 37.0 67 56.8 83 50.1 25
Complementary OF [21]60.0 36.1 21 33.3 52 47.8 9 26.7 36 33.2 55 37.3 36 30.4 100 32.9 67 39.5 80 52.8 67 48.7 94 81.1 96 72.3 65 67.2 79 87.3 16 68.8 34 54.7 59 82.2 56 28.7 85 45.6 85 42.5 61 36.8 46 56.7 79 50.3 63
AggregFlow [97]60.2 37.1 93 34.8 103 48.5 78 27.3 61 33.2 55 38.1 79 28.7 9 30.2 9 38.5 2 52.9 75 48.6 88 80.3 10 72.4 91 67.2 79 87.6 110 69.3 89 54.5 48 82.6 102 28.3 17 44.2 27 42.5 61 36.7 39 56.0 45 50.4 76
LDOF [28]60.5 37.1 93 33.7 70 48.8 91 29.5 100 35.3 99 40.6 98 30.0 89 34.3 97 39.7 90 52.8 67 47.9 59 80.9 76 72.2 36 66.9 29 87.4 34 68.8 34 53.6 13 82.3 71 28.3 17 44.5 36 42.4 10 36.6 27 55.8 39 50.4 76
MLDP_OF [89]61.3 36.2 28 32.9 41 48.0 29 27.0 52 32.7 45 37.2 31 29.1 30 31.8 45 38.8 13 52.6 48 47.3 35 80.8 68 72.3 65 67.1 66 87.5 77 70.5 123 56.6 116 83.6 124 28.6 74 44.8 53 42.8 105 36.9 60 56.1 53 50.5 90
F-TV-L1 [15]61.3 37.4 98 34.6 97 49.2 97 28.8 93 34.9 93 38.3 83 29.7 76 34.1 95 39.5 80 52.7 57 47.6 46 81.0 88 71.7 2 66.5 2 87.4 34 68.8 34 53.5 12 82.4 85 28.3 17 44.3 30 42.4 10 37.1 77 56.3 66 50.6 100
IAOF [50]61.7 38.0 111 34.2 91 49.8 107 31.7 113 37.9 114 41.1 103 28.9 18 32.6 59 39.4 73 53.7 97 48.1 68 80.8 68 72.0 9 66.7 8 87.5 77 68.9 44 54.1 32 82.2 56 28.3 17 45.1 69 42.3 1 36.8 46 56.1 53 50.2 46
Aniso-Texture [82]62.2 36.1 21 32.4 23 48.2 57 28.0 77 34.4 80 37.6 62 30.0 89 32.8 66 39.3 68 52.7 57 48.1 68 80.7 55 72.4 91 67.3 93 87.3 16 69.2 78 56.2 113 82.3 71 28.4 38 44.5 36 42.4 10 37.2 85 57.0 93 50.2 46
Classic++ [32]62.8 36.4 53 33.5 60 48.4 75 27.4 63 33.7 65 37.6 62 29.6 74 33.6 86 39.2 61 52.7 57 47.3 35 80.9 76 72.2 36 67.0 42 87.5 77 69.1 68 54.5 48 82.5 96 28.5 57 44.9 56 42.6 89 36.8 46 56.2 61 50.3 63
Shiralkar [42]64.2 36.5 61 34.6 97 48.1 43 28.3 82 34.3 74 37.2 31 29.8 81 36.9 118 40.0 98 53.9 101 49.0 101 80.5 35 71.8 3 66.6 3 87.2 8 69.2 78 55.1 78 82.4 85 29.2 107 48.0 111 42.5 61 36.6 27 55.7 32 50.1 25
CostFilter [40]64.6 35.9 9 32.7 34 47.6 3 26.8 41 33.5 62 37.1 18 29.7 76 35.6 113 39.2 61 52.9 75 49.4 108 80.3 10 72.6 111 67.6 111 87.4 34 69.6 105 54.8 65 83.1 117 28.6 74 45.6 85 42.6 89 37.0 67 56.7 79 49.9 3
FlowNetS+ft+v [112]64.9 36.8 84 33.0 43 48.7 88 29.5 100 35.6 102 40.5 96 29.8 81 34.3 97 39.5 80 52.8 67 48.2 79 80.8 68 72.2 36 67.0 42 87.4 34 68.7 19 53.9 23 82.1 35 28.6 74 45.9 88 42.5 61 36.7 39 56.0 45 50.4 76
Fusion [6]65.0 36.0 16 32.7 34 47.8 9 26.8 41 32.1 34 37.5 56 29.5 69 31.5 38 39.5 80 53.5 93 48.6 88 80.7 55 72.6 111 68.0 119 87.1 3 69.3 89 57.6 126 81.8 3 28.7 85 47.1 102 42.5 61 38.2 114 59.9 126 50.0 7
TriFlow [95]65.4 37.0 90 35.3 109 48.8 91 28.7 91 34.5 83 41.0 101 29.2 37 33.4 83 38.8 13 53.0 79 48.8 99 80.4 19 72.3 65 67.3 93 87.4 34 69.2 78 55.5 94 82.1 35 28.5 57 44.8 53 42.4 10 36.9 60 56.4 70 50.1 25
SVFilterOh [111]65.4 36.3 41 32.2 18 48.1 43 26.2 22 30.9 17 37.4 47 29.2 37 30.6 16 39.3 68 52.6 48 47.5 42 81.0 88 72.6 111 67.6 111 87.6 110 69.3 89 55.9 102 82.3 71 28.5 57 43.7 13 43.3 120 37.3 91 57.1 97 51.0 110
Occlusion-TV-L1 [63]65.5 36.6 70 33.8 76 48.5 78 28.4 85 34.8 90 37.7 67 29.5 69 33.0 72 39.5 80 53.0 79 48.1 68 81.1 96 72.1 24 66.8 17 87.5 77 68.9 44 53.4 8 82.4 85 29.0 103 44.7 44 42.6 89 36.8 46 55.6 28 50.4 76
CNN-flow-warp+ref [117]66.1 36.3 41 31.7 9 48.7 88 28.5 87 34.7 86 39.5 90 30.4 100 35.0 106 39.8 93 54.0 104 48.1 68 81.2 100 72.3 65 67.0 42 87.4 34 68.6 5 53.2 5 82.4 85 28.8 93 47.1 102 42.5 61 36.6 27 55.7 32 50.3 63
CRTflow [80]66.2 36.7 78 33.8 76 48.5 78 27.7 70 33.8 68 37.4 47 30.7 106 35.3 108 40.9 115 52.9 75 48.1 68 81.8 111 72.2 36 66.9 29 87.4 34 68.9 44 54.1 32 82.3 71 28.4 38 44.9 56 42.5 61 36.8 46 56.1 53 50.5 90
FlowNet2 [122]68.4 39.4 116 38.2 120 50.4 113 29.2 98 34.8 90 41.9 110 30.0 89 34.6 101 39.4 73 53.3 90 51.0 120 80.6 41 72.5 103 67.4 103 87.4 34 68.8 34 54.3 40 82.0 13 28.4 38 45.0 63 42.3 1 36.5 18 55.7 32 49.8 1
Modified CLG [34]68.7 36.9 89 32.8 37 49.4 101 30.9 110 36.3 108 42.8 112 30.0 89 34.8 104 39.9 94 53.0 79 47.9 59 80.7 55 72.2 36 66.9 29 87.5 77 68.7 19 53.8 15 82.2 56 28.4 38 45.1 69 42.5 61 36.9 60 56.2 61 50.5 90
Adaptive [20]69.5 36.8 84 34.4 94 48.5 78 28.8 93 35.2 98 37.7 67 29.4 60 33.2 76 39.2 61 52.6 48 47.6 46 80.6 41 72.3 65 67.0 42 87.5 77 69.1 68 54.7 59 82.3 71 28.7 85 46.0 90 42.4 10 37.3 91 56.9 87 50.4 76
TCOF [69]69.6 36.6 70 33.9 80 48.1 43 29.1 95 35.7 103 38.3 83 29.0 22 31.4 34 38.7 8 52.8 67 48.7 94 80.6 41 72.2 36 67.1 66 87.4 34 69.3 89 56.0 105 82.1 35 28.7 85 46.2 93 42.5 61 38.2 114 58.7 122 50.5 90
IIOF-NLDP [131]70.8 36.3 41 33.3 52 47.7 6 27.6 67 34.3 74 37.4 47 29.8 81 31.7 41 39.2 61 53.3 90 48.7 94 81.2 100 72.2 36 67.0 42 87.5 77 69.8 109 56.8 117 82.2 56 29.4 112 50.8 127 42.8 105 37.1 77 56.8 83 49.9 3
Nguyen [33]71.6 39.6 117 33.9 80 52.6 121 32.5 118 37.9 114 43.3 115 30.0 89 35.5 111 40.2 103 54.1 107 49.0 101 80.9 76 72.0 9 66.8 17 87.4 34 68.6 5 53.8 15 82.0 13 28.8 93 47.8 109 42.4 10 36.8 46 56.1 53 50.3 63
Steered-L1 [118]71.8 36.0 16 32.9 41 47.9 19 27.0 52 33.3 58 37.7 67 30.3 99 32.3 53 39.9 94 53.2 85 48.0 62 81.0 88 72.5 103 67.5 109 87.5 77 68.9 44 55.0 76 82.2 56 28.8 93 46.7 99 42.7 100 37.0 67 57.3 101 50.3 63
BriefMatch [124]72.8 36.3 41 33.3 52 48.0 29 27.2 59 33.4 60 38.5 87 30.6 105 32.6 59 40.6 108 54.0 104 48.6 88 82.8 118 72.4 91 67.3 93 87.3 16 70.2 118 55.6 97 83.9 127 28.3 17 44.3 30 42.7 100 36.6 27 55.7 32 50.5 90
StereoOF-V1MT [119]72.9 36.8 84 35.3 109 48.1 43 28.3 82 35.1 95 36.9 3 31.4 114 36.6 115 40.5 106 54.6 115 48.6 88 81.3 102 72.0 9 66.8 17 87.2 8 69.5 100 54.9 68 82.6 102 29.7 121 48.8 118 42.7 100 36.5 18 55.1 8 50.1 25
SPSA-learn [13]76.4 37.4 98 33.6 66 49.4 101 29.8 104 35.1 95 41.4 107 30.9 109 33.2 76 40.7 109 53.5 93 47.2 33 80.4 19 72.2 36 67.0 42 87.4 34 68.8 34 54.1 32 82.2 56 29.5 116 52.2 130 42.9 111 37.1 77 57.0 93 50.3 63
GraphCuts [14]76.9 38.0 111 35.1 106 49.5 103 28.4 85 33.9 70 41.3 105 31.3 113 30.8 23 40.7 109 53.7 97 48.3 81 81.0 88 72.1 24 67.1 66 87.1 3 68.6 5 54.9 68 81.7 1 28.8 93 46.3 94 42.8 105 37.7 101 58.5 118 50.4 76
HBpMotionGpu [43]77.6 38.8 114 35.9 112 50.9 117 32.1 115 38.2 116 44.4 119 29.2 37 31.7 41 39.3 68 53.9 101 49.6 110 81.5 106 72.1 24 67.0 42 87.1 3 69.5 100 54.9 68 82.4 85 28.3 17 44.4 32 42.5 61 37.3 91 56.5 73 51.1 111
Dynamic MRF [7]77.7 36.2 28 34.1 87 48.0 29 27.5 65 34.6 85 37.4 47 30.9 109 36.8 117 40.4 105 54.5 113 49.3 107 81.9 112 71.9 4 66.8 17 87.2 8 69.4 98 55.5 94 82.5 96 29.0 103 47.8 109 42.5 61 37.5 98 56.8 83 50.5 90
2D-CLG [1]78.1 37.9 106 33.5 60 50.5 115 32.5 118 37.4 111 45.0 120 30.8 108 34.8 104 40.7 109 53.7 97 48.3 81 80.5 35 72.3 65 67.1 66 87.6 110 68.6 5 53.2 5 82.2 56 28.8 93 46.7 99 42.5 61 36.9 60 55.6 28 50.3 63
Black & Anandan [4]78.3 37.9 106 34.1 87 49.6 104 30.7 108 36.0 104 41.2 104 31.0 111 34.7 103 40.3 104 53.9 101 48.6 88 80.7 55 72.3 65 67.0 42 87.4 34 69.0 58 53.8 15 82.5 96 28.8 93 46.5 95 42.4 10 37.0 67 56.1 53 50.4 76
ROF-ND [107]78.6 37.0 90 32.8 37 48.1 43 27.7 70 34.7 86 37.6 62 29.7 76 32.3 53 39.1 50 54.2 109 51.4 121 80.4 19 72.4 91 67.3 93 87.4 34 69.5 100 56.9 118 82.0 13 29.7 121 49.0 121 43.2 118 37.8 105 57.7 110 50.2 46
TV-L1-improved [17]79.0 36.6 70 34.1 87 48.4 75 28.6 90 35.1 95 37.8 70 30.5 103 33.2 76 40.0 98 52.7 57 48.0 62 80.9 76 72.3 65 67.2 79 87.4 34 69.1 68 54.9 68 82.3 71 28.8 93 47.3 105 42.6 89 37.2 85 56.7 79 50.6 100
CBF [12]80.6 36.4 53 32.5 29 48.9 94 27.5 65 33.8 68 37.9 74 29.3 47 31.6 39 39.1 50 53.2 85 48.1 68 82.6 115 72.4 91 67.2 79 87.7 119 69.2 78 55.3 86 82.3 71 28.7 85 46.1 91 42.9 111 37.9 107 57.7 110 51.7 120
Rannacher [23]82.0 36.7 78 34.5 95 48.7 88 28.7 91 35.3 99 38.1 79 30.5 103 34.0 93 39.9 94 52.7 57 48.0 62 80.8 68 72.4 91 67.2 79 87.5 77 69.0 58 54.6 55 82.3 71 28.8 93 47.0 101 42.6 89 37.1 77 56.4 70 50.6 100
UnFlow [129]82.2 39.2 115 37.9 118 50.6 116 32.3 116 38.9 121 41.3 105 31.6 119 38.5 123 40.8 114 53.2 85 48.7 94 80.9 76 72.0 9 66.7 8 87.4 34 69.5 100 54.6 55 82.4 85 28.2 11 43.2 3 42.4 10 39.3 127 58.3 116 51.2 112
Correlation Flow [75]82.8 36.2 28 33.4 57 47.7 6 27.7 70 34.3 74 37.3 36 29.4 60 31.4 34 38.8 13 53.1 83 48.5 85 81.3 102 72.8 116 67.6 111 88.6 129 70.1 113 57.1 121 82.6 102 29.4 112 48.8 118 43.0 113 37.7 101 57.9 112 50.5 90
HBM-GC [105]83.8 37.7 103 34.7 100 49.8 107 27.1 56 32.4 40 37.9 74 28.8 12 29.6 3 39.2 61 52.6 48 47.3 35 80.8 68 73.2 122 68.1 120 88.2 124 70.0 111 57.3 123 82.7 106 28.9 102 45.0 63 43.5 121 37.6 100 57.2 99 51.3 114
SegOF [10]84.2 37.6 102 33.2 49 50.0 110 29.1 95 34.7 86 41.0 101 31.4 114 35.3 108 40.7 109 53.6 95 50.7 118 80.6 41 72.3 65 67.2 79 87.5 77 69.0 58 55.3 86 82.2 56 29.0 103 48.6 116 42.7 100 36.7 39 55.8 39 50.4 76
TriangleFlow [30]84.4 37.0 90 34.9 104 48.5 78 28.0 77 34.7 86 37.5 56 30.2 97 33.0 72 39.9 94 53.2 85 49.0 101 81.1 96 72.0 9 66.9 29 87.1 3 69.8 109 56.1 107 82.4 85 29.2 107 48.5 115 42.8 105 38.1 112 58.5 118 50.5 90
BlockOverlap [61]84.9 38.5 113 33.2 49 51.3 118 30.0 106 34.4 80 42.8 112 29.4 60 30.4 14 40.0 98 53.2 85 46.9 25 83.0 119 72.9 119 67.6 111 88.3 125 69.7 107 54.1 32 83.3 122 28.7 85 44.1 25 43.5 121 37.1 77 55.3 14 51.8 121
IAOF2 [51]87.2 37.9 106 35.9 112 49.1 96 29.6 102 36.1 106 40.0 93 29.3 47 33.4 83 40.0 98 54.1 107 50.2 115 81.0 88 72.4 91 67.4 103 87.4 34 69.2 78 54.9 68 82.4 85 28.6 74 45.5 82 42.4 10 37.9 107 57.6 107 50.6 100
Ad-TV-NDC [36]87.4 40.4 121 35.1 106 53.1 122 31.9 114 36.7 110 43.8 117 29.4 60 32.9 67 39.1 50 54.5 113 49.2 106 82.1 113 72.5 103 67.3 93 87.5 77 69.3 89 53.9 23 82.7 106 28.6 74 45.4 77 42.4 10 37.2 85 56.2 61 50.6 100
AdaConv-v1 [126]93.8 37.2 97 36.6 115 47.5 2 34.3 123 39.1 123 51.1 129 36.1 127 39.4 125 52.9 131 58.2 127 53.1 125 83.8 124 70.9 1 65.4 1 86.6 2 69.7 107 54.7 59 84.4 128 38.6 131 46.5 95 77.4 131 38.2 114 54.6 2 60.3 131
LocallyOriented [52]95.2 37.5 100 35.9 112 49.2 97 29.6 102 36.2 107 39.1 88 30.1 95 33.8 90 39.5 80 53.7 97 50.0 113 81.3 102 72.3 65 67.2 79 87.5 77 70.2 118 56.2 113 82.9 113 28.8 93 45.6 85 42.5 61 37.7 101 57.6 107 50.5 90
ACK-Prior [27]96.3 36.4 53 33.7 70 48.1 43 26.7 36 33.1 53 37.1 18 30.7 106 33.3 80 39.7 90 53.6 95 50.0 113 81.0 88 73.5 126 68.6 123 88.3 125 70.8 125 59.8 129 82.7 106 29.7 121 48.7 117 43.6 124 39.5 128 62.1 129 51.3 114
Horn & Schunck [3]96.5 37.9 106 35.1 106 49.6 104 31.4 111 37.7 113 41.8 109 31.7 120 37.4 120 41.5 117 55.8 119 50.6 116 81.3 102 72.2 36 67.0 42 87.4 34 69.2 78 54.2 38 82.7 106 29.5 116 48.9 120 42.6 89 37.8 105 57.2 99 50.9 109
StereoFlow [44]96.6 46.3 130 45.9 131 54.3 123 38.3 130 45.4 131 45.7 122 29.3 47 33.8 90 39.1 50 52.9 75 47.7 51 81.0 88 74.4 130 70.5 131 87.6 110 72.0 129 66.3 131 82.4 85 28.4 38 45.0 63 42.4 10 38.0 111 59.1 123 50.5 90
Filter Flow [19]97.7 37.8 105 34.6 97 49.8 107 30.8 109 36.0 104 44.3 118 29.4 60 32.4 56 39.5 80 54.2 109 48.1 68 82.2 114 72.7 115 67.7 117 87.6 110 69.2 78 55.1 78 82.5 96 28.7 85 46.5 95 42.6 89 38.3 120 58.4 117 51.4 117
TI-DOFE [24]98.5 42.0 124 37.5 117 54.8 126 35.2 125 41.1 128 46.8 125 31.4 114 37.7 121 41.6 119 56.1 121 50.6 116 81.6 107 72.0 9 66.9 29 87.2 8 69.4 98 54.4 44 82.6 102 29.2 107 47.6 108 42.6 89 38.2 114 57.5 105 50.8 108
SILK [79]100.1 39.6 117 38.1 119 51.5 120 32.4 117 38.5 119 43.6 116 32.4 121 37.2 119 41.5 117 55.4 117 49.7 111 83.0 119 72.2 36 67.0 42 87.4 34 70.0 111 54.7 59 83.4 123 29.0 103 46.5 95 42.8 105 37.4 97 56.7 79 50.7 106
Bartels [41]102.0 37.1 93 35.0 105 49.3 100 28.2 79 34.8 90 40.5 96 29.9 86 33.1 75 40.5 106 54.2 109 49.7 111 83.9 125 73.0 120 67.6 111 88.7 130 71.8 128 56.1 107 85.6 130 28.6 74 43.9 16 43.6 124 38.1 112 57.0 93 53.2 127
SLK [47]108.5 41.6 123 38.7 122 54.4 124 33.0 120 38.3 118 45.5 121 33.3 122 38.6 124 42.8 121 57.8 125 51.8 123 83.5 123 72.1 24 67.3 93 86.5 1 70.1 113 55.8 100 82.7 106 30.0 124 51.4 128 43.0 113 38.2 114 57.5 105 51.5 118
NL-TV-NCC [25]109.8 37.1 93 35.7 111 48.0 29 27.8 74 35.0 94 37.6 62 31.0 111 35.4 110 40.0 98 56.0 120 54.2 128 82.6 115 73.8 128 68.6 123 89.1 131 70.6 124 58.4 128 82.5 96 30.4 128 50.0 124 44.0 128 39.8 129 60.2 127 52.4 125
GroupFlow [9]110.5 40.3 120 40.1 124 51.3 118 31.5 112 38.9 121 42.6 111 33.5 124 39.5 126 43.8 123 54.7 116 52.3 124 81.0 88 73.2 122 68.6 123 87.6 110 70.4 122 57.3 123 83.0 115 29.3 110 48.1 112 42.5 61 37.9 107 58.2 115 50.1 25
FFV1MT [106]111.1 39.6 117 40.8 126 50.3 112 34.8 124 38.8 120 46.6 124 36.5 128 45.8 129 44.6 125 56.2 122 49.0 101 81.7 108 72.5 103 67.4 103 87.4 34 70.2 118 54.7 59 83.2 118 30.1 126 49.3 123 42.8 105 38.6 121 57.6 107 51.3 114
Learning Flow [11]111.4 37.7 103 37.0 116 49.2 97 29.9 105 37.5 112 39.7 91 31.5 118 36.3 114 40.7 109 55.4 117 51.6 122 82.6 115 72.8 116 67.8 118 87.8 121 69.6 105 55.7 98 82.8 111 29.3 110 48.4 113 42.7 100 39.2 125 59.8 125 51.2 112
Heeger++ [104]111.6 40.6 122 42.5 128 50.4 113 33.5 121 38.2 116 43.0 114 37.6 129 48.1 130 44.9 126 56.2 122 49.0 101 81.7 108 73.4 125 68.9 128 87.5 77 70.1 113 56.0 105 82.8 111 30.3 127 49.1 122 42.6 89 37.7 101 56.9 87 50.3 63
2bit-BM-tele [98]112.5 37.9 106 34.7 100 50.1 111 30.1 107 36.4 109 41.7 108 30.2 97 32.2 52 41.3 116 54.3 112 49.4 108 84.1 127 73.3 124 68.1 120 88.3 125 72.2 130 57.2 122 85.5 129 30.4 128 52.7 131 44.4 129 38.2 114 56.3 66 54.1 129
FOLKI [16]116.2 44.6 128 40.4 125 58.4 129 35.7 126 42.3 129 47.3 126 33.3 122 40.7 127 44.9 126 59.4 130 53.6 126 86.5 130 72.5 103 67.6 111 87.3 16 70.1 113 55.8 100 83.2 118 29.4 112 48.4 113 43.1 116 38.7 122 58.5 118 52.0 122
Pyramid LK [2]120.0 46.1 129 38.9 123 61.0 130 36.7 129 40.4 126 50.9 128 39.9 130 36.6 115 49.4 129 64.1 131 61.2 131 87.7 131 73.1 121 68.6 123 87.4 34 70.1 113 56.1 107 83.0 115 29.6 119 50.7 126 43.2 118 39.2 125 61.2 128 51.5 118
Adaptive flow [45]120.8 43.8 126 38.2 120 56.5 127 35.8 127 40.5 127 50.2 127 31.4 114 34.5 99 42.5 120 56.5 124 50.9 119 83.9 125 73.5 126 68.7 127 88.1 123 70.2 118 57.4 125 82.9 113 29.4 112 47.5 106 43.6 124 39.0 124 59.1 123 52.0 122
PGAM+LK [55]121.3 42.5 125 41.4 127 54.6 125 33.7 122 40.1 125 45.8 123 33.9 125 41.1 128 43.3 122 59.3 129 55.2 129 85.5 129 72.8 116 68.1 120 87.5 77 70.8 125 56.9 118 83.6 124 29.6 119 50.0 124 43.0 113 38.7 122 58.6 121 52.1 124
HCIC-L [99]125.0 49.1 131 42.6 129 63.0 131 35.8 127 39.4 124 52.5 130 34.6 126 37.7 121 43.9 124 58.0 126 53.9 127 81.7 108 74.0 129 69.3 129 88.5 128 71.7 127 60.5 130 83.2 118 29.5 116 47.5 106 43.9 127 40.5 130 62.9 130 52.8 126
Periodicity [78]128.8 44.4 127 43.3 130 56.9 128 42.8 131 43.4 130 56.2 131 40.9 131 49.1 131 49.5 130 58.9 128 58.6 130 84.9 128 74.4 130 70.2 130 88.0 122 73.1 131 57.9 127 86.2 131 30.0 124 51.5 129 43.5 121 41.8 131 63.4 131 53.7 128
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.