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        
R5.0
interpolation
error
avg. Mequon
(Hidden texture)
im0   GT   im1
Schefflera
(Hidden texture)
im0   GT   im1
Urban
(Synthetic)
im0   GT   im1
Teddy
(Stereo)
im0   GT   im1
Backyard
(High-speed camera)
im0   GT   im1
Basketball
(High-speed camera)
im0   GT   im1
Dumptruck
(High-speed camera)
im0   GT   im1
Evergreen
(High-speed camera)
im0   GT   im1
rank all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext all disc untext
PMMST [114]12.3 4.93 3 13.9 3 0.13 4 8.97 27 17.1 12 0.43 10 6.00 10 13.4 4 0.27 1 17.6 3 26.2 6 5.24 20 43.0 14 57.7 8 5.17 20 10.3 5 39.1 8 0.87 12 9.75 17 41.0 16 0.44 17 21.5 27 51.9 34 0.47 15
MDP-Flow2 [68]13.2 4.89 2 14.4 5 0.12 3 8.58 11 16.9 10 0.39 3 5.95 7 13.6 6 0.28 3 17.7 4 26.7 14 5.32 34 42.9 6 57.6 5 5.13 15 10.6 19 40.1 25 0.92 21 9.75 17 41.0 16 0.43 10 21.6 39 51.9 34 0.46 9
SepConv-v1 [127]14.9 3.41 1 11.0 1 0.08 1 8.39 7 16.7 9 1.04 99 2.81 1 7.63 1 0.74 99 18.0 17 25.2 1 5.82 97 42.9 6 57.4 2 4.74 2 9.03 1 34.1 1 0.60 1 9.34 2 38.6 1 0.42 4 20.1 1 48.6 1 0.35 1
NN-field [71]17.9 5.14 19 16.1 48 0.13 4 8.21 3 15.7 3 0.38 2 6.39 43 13.6 6 0.30 7 18.4 35 28.7 70 5.33 38 42.9 6 57.6 5 5.08 11 10.7 26 40.2 30 0.94 28 9.62 7 40.5 7 0.44 17 21.2 4 51.2 7 0.45 3
NNF-Local [87]17.9 5.11 15 15.7 26 0.11 2 8.18 2 15.8 4 0.39 3 6.01 13 13.5 5 0.27 1 18.3 33 28.3 54 5.29 27 43.0 14 57.6 5 5.11 13 10.8 41 40.9 55 1.01 45 9.67 9 40.7 9 0.46 33 21.2 4 51.2 7 0.46 9
Layers++ [37]21.5 5.25 32 15.9 34 0.17 35 8.27 4 15.5 2 0.37 1 6.16 22 14.3 12 0.38 39 18.0 17 26.9 18 5.32 34 43.1 25 57.9 21 5.24 38 10.7 26 40.6 48 0.97 40 9.70 10 40.7 9 0.39 1 21.3 10 51.3 9 0.48 28
PH-Flow [101]24.2 5.32 44 16.4 59 0.16 26 8.28 5 15.9 5 0.44 13 6.12 19 13.9 9 0.33 16 17.5 1 25.8 2 5.15 7 42.8 4 57.5 3 5.03 8 11.0 66 41.6 84 1.09 67 9.71 11 41.0 16 0.46 33 21.3 10 51.4 11 0.50 62
COFM [59]25.9 5.08 13 15.1 13 0.19 53 8.86 19 17.4 17 0.48 25 6.37 39 14.2 11 0.40 46 17.7 4 26.2 6 5.11 2 42.9 6 57.8 12 5.02 7 10.9 52 41.6 84 1.11 70 9.24 1 38.8 2 0.50 69 21.5 27 51.9 34 0.46 9
nLayers [57]26.0 5.26 35 15.8 32 0.16 26 8.54 9 16.6 8 0.45 16 5.89 4 13.1 2 0.30 7 18.1 23 27.1 23 5.35 44 43.3 51 58.0 30 5.36 70 10.8 41 40.9 55 1.11 70 9.65 8 40.1 3 0.48 56 21.2 4 51.1 5 0.45 3
Sparse-NonSparse [56]26.7 5.31 43 16.3 57 0.17 35 8.74 14 17.2 16 0.48 25 6.19 23 14.7 24 0.34 20 17.9 12 26.3 8 5.23 17 43.1 25 57.8 12 5.25 41 11.0 66 41.2 64 1.04 53 9.71 11 40.9 13 0.46 33 21.2 4 51.3 9 0.47 15
IROF++ [58]28.0 5.37 55 16.8 75 0.14 8 8.87 21 17.4 17 0.45 16 6.41 49 14.6 21 0.43 57 17.5 1 25.8 2 5.22 12 42.9 6 57.8 12 5.19 25 10.5 11 39.4 13 0.87 12 10.0 55 42.4 61 0.47 49 21.4 19 51.5 12 0.50 62
TV-L1-MCT [64]28.5 5.74 103 18.1 109 0.18 45 9.50 41 19.1 40 0.58 39 5.73 2 14.5 19 0.38 39 17.8 8 26.0 5 5.28 25 43.0 14 57.9 21 5.22 32 10.4 7 39.1 8 0.94 28 9.78 23 41.1 22 0.44 17 21.2 4 51.1 5 0.48 28
HAST [109]29.5 5.12 16 15.2 15 0.16 26 8.74 14 17.1 12 0.43 10 6.62 73 15.3 47 0.39 43 17.7 4 26.4 10 4.98 1 43.0 14 58.0 30 5.05 9 11.0 66 41.4 73 1.06 60 9.53 3 40.4 5 0.42 4 22.0 80 52.8 77 0.47 15
ComponentFusion [96]30.8 5.15 20 16.1 48 0.14 8 8.86 19 17.9 25 0.41 6 6.38 40 15.4 48 0.33 16 17.8 8 27.0 21 5.15 7 43.2 41 58.0 30 5.24 38 10.6 19 39.8 17 0.94 28 10.0 55 42.7 80 0.57 95 21.5 27 51.8 28 0.47 15
ProbFlowFields [128]31.2 5.03 8 15.6 24 0.17 35 8.55 10 17.1 12 0.41 6 6.00 10 14.4 15 0.32 13 18.1 23 27.1 23 5.38 49 43.3 51 58.1 47 5.49 105 10.9 52 41.2 64 1.20 88 9.61 6 40.7 9 0.47 49 21.0 3 50.8 3 0.49 44
FMOF [94]31.4 5.62 91 17.2 86 0.21 62 8.71 13 17.0 11 0.44 13 6.38 40 14.7 24 0.46 62 18.6 48 28.0 41 5.31 32 43.1 25 57.9 21 5.15 18 10.8 41 40.5 44 0.87 12 9.60 5 40.4 5 0.40 3 21.5 27 51.7 20 0.46 9
2DHMM-SAS [92]34.4 5.62 91 17.6 102 0.18 45 10.1 63 19.7 54 0.64 54 5.73 2 14.4 15 0.37 37 17.7 4 25.9 4 5.30 29 43.0 14 57.8 12 5.26 44 10.7 26 40.0 23 0.82 5 9.83 29 41.3 25 0.48 56 21.6 39 52.0 38 0.47 15
CombBMOF [113]35.1 5.46 69 16.2 55 0.22 72 8.89 22 18.0 27 0.45 16 6.29 30 14.7 24 0.40 46 18.5 42 28.0 41 5.24 20 43.0 14 57.7 8 5.08 11 10.8 41 40.2 30 0.82 5 11.7 124 42.9 85 0.47 49 21.2 4 50.9 4 0.45 3
LSM [39]36.0 5.49 73 17.4 96 0.18 45 8.93 24 17.7 22 0.48 25 6.32 34 15.4 48 0.35 28 18.1 23 27.1 23 5.22 12 43.1 25 57.9 21 5.28 54 11.0 66 41.3 69 1.03 51 9.72 13 40.9 13 0.46 33 21.4 19 51.7 20 0.48 28
Ramp [62]36.9 5.46 69 17.1 83 0.18 45 8.84 17 17.4 17 0.58 39 6.14 20 14.7 24 0.34 20 17.8 8 26.4 10 5.23 17 43.2 41 58.0 30 5.27 49 11.2 86 42.0 92 1.15 78 9.72 13 40.9 13 0.42 4 21.6 39 52.1 43 0.48 28
DeepFlow [86]37.5 5.06 12 14.6 7 0.19 53 9.80 55 19.5 45 0.75 67 6.45 52 16.6 81 0.35 28 18.7 56 27.6 34 5.41 55 43.4 68 58.0 30 5.37 72 10.3 5 38.3 4 0.99 41 9.83 29 41.8 41 0.43 10 21.3 10 51.6 18 0.48 28
NNF-EAC [103]37.9 5.52 77 15.7 26 0.34 113 9.27 37 18.1 29 0.48 25 6.53 58 13.8 8 0.40 46 18.2 29 27.0 21 5.71 86 43.0 14 57.7 8 5.11 13 10.4 7 39.1 8 0.83 7 9.89 37 41.6 36 0.52 80 21.7 50 52.2 50 0.49 44
SuperFlow [81]38.0 4.99 7 14.3 4 0.22 72 10.3 69 19.9 59 0.90 82 6.61 68 15.5 51 0.51 69 18.5 42 27.2 28 5.52 73 43.3 51 58.1 47 5.37 72 10.1 3 38.0 3 0.73 3 9.73 16 41.4 30 0.46 33 21.3 10 51.5 12 0.46 9
DeepFlow2 [108]38.2 5.16 21 14.9 12 0.21 62 9.81 56 19.7 54 0.65 56 6.38 40 16.3 71 0.34 20 18.6 48 28.1 46 5.29 27 43.4 68 58.0 30 5.37 72 10.2 4 38.4 5 0.85 10 9.96 52 42.1 54 0.44 17 21.4 19 51.8 28 0.49 44
LME [70]38.3 5.13 18 15.8 32 0.14 8 9.15 34 18.4 38 0.51 30 6.32 34 15.7 55 0.34 20 17.9 12 27.1 23 5.34 40 43.8 110 58.8 109 5.79 123 10.8 41 41.2 64 0.93 23 9.86 33 41.3 25 0.43 10 21.3 10 51.5 12 0.47 15
WLIF-Flow [93]38.9 5.25 32 16.0 41 0.15 15 9.14 33 18.1 29 0.59 45 6.29 30 14.3 12 0.34 20 17.9 12 26.3 8 5.65 82 43.1 25 57.9 21 5.26 44 11.2 86 41.9 91 1.22 94 9.82 28 41.3 25 0.44 17 21.7 50 52.2 50 0.49 44
PGM-C [120]39.3 5.18 24 16.0 41 0.15 15 8.97 27 18.2 31 0.46 22 6.51 55 16.4 76 0.33 16 18.4 35 28.5 61 5.36 46 43.4 68 58.1 47 5.40 87 10.7 26 40.5 44 0.96 37 9.92 42 41.9 43 0.45 25 21.4 19 51.8 28 0.48 28
FlowFields+ [130]39.3 5.23 31 16.6 67 0.15 15 8.91 23 18.3 32 0.45 16 6.28 29 15.9 59 0.34 20 18.2 29 28.1 46 5.34 40 43.4 68 58.2 58 5.35 67 10.9 52 41.6 84 1.10 69 9.79 24 41.5 32 0.46 33 21.3 10 51.5 12 0.48 28
FlowFields [110]40.0 5.22 29 16.5 62 0.16 26 8.95 25 18.3 32 0.42 8 6.29 30 15.9 59 0.35 28 18.4 35 28.5 61 5.41 55 43.4 68 58.1 47 5.33 59 10.9 52 41.3 69 1.08 63 9.79 24 41.5 32 0.45 25 21.3 10 51.6 18 0.49 44
Classic+NL [31]40.1 5.56 83 17.4 96 0.22 72 8.99 29 17.6 21 0.54 32 6.02 14 14.7 24 0.36 33 18.1 23 26.8 15 5.41 55 43.1 25 58.0 30 5.23 35 11.1 83 41.5 78 1.06 60 9.72 13 41.0 16 0.46 33 21.6 39 52.0 38 0.47 15
DF-Auto [115]41.2 5.03 8 13.8 2 0.17 35 10.2 64 19.3 42 0.79 71 6.09 16 14.4 15 0.34 20 18.7 56 28.1 46 5.24 20 43.2 41 57.9 21 5.31 57 10.4 7 39.3 11 0.93 23 10.1 64 42.3 58 0.49 62 21.9 72 52.9 83 0.53 96
FC-2Layers-FF [74]42.2 5.40 60 17.0 81 0.17 35 8.15 1 15.3 1 0.42 8 6.14 20 14.9 32 0.35 28 18.1 23 27.2 28 5.31 32 43.3 51 58.2 58 5.36 70 11.2 86 42.2 96 1.20 88 9.75 17 41.0 16 0.49 62 21.7 50 52.1 43 0.48 28
S2F-IF [123]42.3 5.22 29 16.5 62 0.15 15 8.84 17 18.0 27 0.44 13 6.27 28 15.7 55 0.33 16 18.3 33 28.3 54 5.14 6 43.4 68 58.2 58 5.41 90 11.0 66 41.5 78 1.11 70 9.91 41 41.9 43 0.47 49 21.3 10 51.5 12 0.51 75
AGIF+OF [85]42.4 5.60 88 17.4 96 0.15 15 8.95 25 17.7 22 0.59 45 6.20 25 14.5 19 0.43 57 17.9 12 26.6 13 5.22 12 43.4 68 58.3 79 5.38 79 11.1 83 42.0 92 1.01 45 9.87 36 40.7 9 0.42 4 21.5 27 52.0 38 0.48 28
OFLAF [77]42.8 5.16 21 15.9 34 0.14 8 8.28 5 16.1 6 0.40 5 6.34 38 14.9 32 0.30 7 18.0 17 27.3 30 5.11 2 43.3 51 58.1 47 5.39 81 11.2 86 42.4 97 1.21 91 10.1 64 42.4 61 0.60 101 21.9 72 52.6 68 0.45 3
MDP-Flow [26]43.7 5.03 8 15.4 17 0.14 8 8.68 12 17.4 17 0.47 23 5.97 8 14.3 12 0.32 13 18.9 72 28.5 61 5.50 70 43.2 41 58.0 30 5.39 81 11.2 86 42.6 100 1.31 102 10.3 82 43.1 90 0.49 62 21.4 19 51.7 20 0.47 15
S2D-Matching [84]44.4 5.56 83 17.3 90 0.18 45 9.96 60 19.9 59 0.66 57 5.99 9 14.7 24 0.41 51 17.9 12 26.4 10 5.40 54 43.2 41 58.0 30 5.17 20 11.2 86 42.0 92 1.17 83 9.93 45 41.1 22 0.43 10 21.5 27 51.8 28 0.48 28
TF+OM [100]45.5 4.98 5 14.6 7 0.20 58 9.03 31 17.9 25 0.55 34 6.29 30 16.2 68 0.39 43 18.5 42 28.0 41 5.50 70 43.3 51 58.1 47 5.47 102 10.6 19 39.8 17 1.03 51 9.86 33 42.0 49 0.51 76 21.7 50 52.3 55 0.52 87
Brox et al. [5]46.2 5.33 50 15.4 17 0.19 53 10.2 64 20.1 64 0.64 54 6.61 68 17.2 96 0.46 62 18.7 56 28.2 50 5.21 9 43.4 68 58.1 47 5.27 49 10.7 26 40.1 25 0.99 41 9.90 39 42.0 49 0.45 25 21.6 39 52.1 43 0.47 15
ALD-Flow [66]46.2 5.37 55 16.1 48 0.23 80 9.53 42 19.2 41 0.57 37 6.51 55 16.7 85 0.34 20 18.2 29 27.9 37 5.32 34 43.4 68 58.3 79 5.46 100 10.7 26 39.9 20 0.99 41 9.76 22 41.2 24 0.44 17 21.8 61 52.7 73 0.47 15
SVFilterOh [111]46.9 5.32 44 15.7 26 0.21 62 8.78 16 17.1 12 0.49 29 6.40 46 14.6 21 0.38 39 18.4 35 27.1 23 5.80 95 43.8 110 58.6 105 5.65 117 10.9 52 41.0 61 1.04 53 9.54 4 40.1 3 0.43 10 21.7 50 52.2 50 0.50 62
CPM-Flow [116]47.1 5.20 28 16.1 48 0.16 26 8.99 29 18.3 32 0.47 23 6.42 50 16.0 64 0.30 7 18.8 64 29.2 88 5.43 61 43.4 68 58.2 58 5.44 98 10.6 19 40.1 25 1.02 47 10.0 55 42.6 72 0.45 25 21.4 19 51.8 28 0.53 96
AggregFlow [97]47.4 5.64 94 17.2 86 0.22 72 9.81 56 19.5 45 0.59 45 6.11 18 14.4 15 0.28 3 18.9 72 29.0 79 5.30 29 43.4 68 58.2 58 5.33 59 10.7 26 40.2 30 0.96 37 9.89 37 41.7 38 0.50 69 21.4 19 51.7 20 0.50 62
RNLOD-Flow [121]48.7 5.32 44 16.6 67 0.16 26 9.70 51 19.6 51 0.60 49 6.57 62 15.5 51 0.51 69 18.2 29 27.4 31 5.22 12 43.1 25 58.0 30 5.28 54 11.0 66 41.4 73 1.08 63 9.85 32 41.3 25 0.50 69 21.9 72 52.7 73 0.49 44
Second-order prior [8]48.9 5.29 40 15.3 16 0.27 97 10.8 79 21.1 78 0.78 70 7.14 94 17.8 103 0.62 92 18.6 48 28.3 54 5.21 9 42.9 6 57.7 8 5.16 19 10.5 11 39.6 15 0.93 23 10.2 75 42.8 82 0.44 17 21.6 39 52.3 55 0.49 44
IROF-TV [53]50.2 5.35 54 16.6 67 0.21 62 9.10 32 17.8 24 0.57 37 6.61 68 16.8 87 0.44 59 17.8 8 26.9 18 5.37 48 43.5 90 58.4 91 5.50 107 10.5 11 40.1 25 0.90 18 9.98 54 42.2 56 0.46 33 21.6 39 52.1 43 0.51 75
DPOF [18]50.8 5.51 76 17.9 107 0.22 72 8.45 8 16.5 7 0.43 10 6.87 80 15.1 41 0.59 84 18.9 72 29.5 93 5.43 61 42.9 6 57.8 12 5.05 9 11.0 66 40.9 55 0.84 9 10.3 82 42.5 68 0.45 25 21.9 72 52.8 77 0.48 28
TC-Flow [46]52.6 5.19 25 15.9 34 0.21 62 9.57 43 19.6 51 0.63 51 6.78 78 17.0 93 0.36 33 18.1 23 27.4 31 5.61 78 43.3 51 58.2 58 5.46 100 11.0 66 41.6 84 1.18 84 9.93 45 41.7 38 0.45 25 21.5 27 52.0 38 0.49 44
Aniso. Huber-L1 [22]53.0 5.41 62 16.0 41 0.23 80 11.2 89 21.1 78 0.90 82 6.72 75 15.4 48 0.46 62 18.5 42 28.1 46 5.39 53 43.0 14 57.8 12 5.23 35 10.5 11 40.1 25 0.81 4 10.2 75 42.6 72 0.46 33 21.9 72 52.7 73 0.52 87
OAR-Flow [125]53.2 5.28 38 15.5 20 0.18 45 9.71 53 19.5 45 0.67 58 6.43 51 16.3 71 0.28 3 18.0 17 27.6 34 5.23 17 43.5 90 58.4 91 5.48 103 10.9 52 41.3 69 1.13 75 10.2 75 42.9 85 0.51 76 21.7 50 52.3 55 0.45 3
EpicFlow [102]53.9 5.19 25 16.1 48 0.15 15 9.60 44 19.8 58 0.58 39 6.40 46 16.4 76 0.35 28 18.6 48 29.1 86 5.47 67 43.4 68 58.2 58 5.42 93 10.8 41 41.2 64 1.08 63 10.1 64 42.5 68 0.54 86 21.5 27 52.0 38 0.49 44
ComplOF-FED-GPU [35]54.0 5.30 42 16.1 48 0.19 53 9.39 39 19.3 42 0.58 39 7.21 98 16.9 90 0.66 94 18.4 35 28.6 67 5.32 34 43.1 25 58.0 30 5.27 49 10.8 41 40.9 55 0.99 41 10.1 64 42.8 82 0.47 49 21.8 61 52.3 55 0.50 62
FESL [72]55.7 5.65 97 17.3 90 0.17 35 9.18 35 18.3 32 0.55 34 6.22 26 15.0 38 0.44 59 18.8 64 28.4 57 5.38 49 43.4 68 58.2 58 5.41 90 11.3 93 42.8 104 1.19 86 9.92 42 41.5 32 0.42 4 21.8 61 52.3 55 0.48 28
Classic+CPF [83]55.9 5.59 87 17.3 90 0.16 26 9.22 36 18.3 32 0.58 39 6.00 10 14.9 32 0.40 46 18.0 17 26.8 15 5.22 12 43.5 90 58.5 99 5.38 79 11.4 98 43.0 110 1.15 78 10.1 64 41.9 43 0.45 25 22.0 80 53.1 90 0.49 44
PMF [73]56.3 5.32 44 16.6 67 0.14 8 9.67 50 19.9 59 0.45 16 6.89 85 18.2 107 0.49 66 18.4 35 27.9 37 5.21 9 43.5 90 58.4 91 5.22 32 11.0 66 40.5 44 1.27 99 9.86 33 41.8 41 0.46 33 22.1 88 53.1 90 0.50 62
RFlow [90]57.6 5.19 25 16.1 48 0.23 80 10.8 79 21.2 82 0.85 77 6.59 66 16.0 64 0.51 69 18.8 64 28.8 73 5.47 67 43.1 25 58.0 30 5.21 30 10.5 11 40.0 23 0.93 23 10.0 55 42.6 72 0.49 62 22.1 88 53.2 94 0.51 75
Local-TV-L1 [65]57.8 5.29 40 14.6 7 0.35 115 11.5 96 21.1 78 1.23 107 6.39 43 14.9 32 0.37 37 19.0 78 27.9 37 6.64 112 43.3 51 58.3 79 5.33 59 10.9 52 39.0 6 1.58 123 9.79 24 41.6 36 0.48 56 21.3 10 51.5 12 0.53 96
CLG-TV [48]59.0 5.32 44 15.7 26 0.26 94 11.0 85 21.2 82 0.83 76 6.75 77 16.6 81 0.56 78 18.9 72 28.4 57 5.50 70 43.3 51 58.1 47 5.25 41 10.5 11 39.8 17 0.87 12 10.1 64 42.5 68 0.44 17 22.0 80 53.1 90 0.51 75
Classic++ [32]59.7 5.33 50 16.0 41 0.28 98 10.2 64 20.3 68 0.69 61 6.87 80 16.6 81 0.50 67 18.7 56 27.7 36 5.64 80 43.2 41 58.0 30 5.26 44 11.0 66 40.7 51 1.34 105 9.93 45 41.9 43 0.47 49 21.7 50 52.4 64 0.50 62
EPPM w/o HM [88]59.7 5.34 52 17.3 90 0.13 4 9.73 54 20.1 64 0.53 31 7.33 105 18.7 113 0.63 93 18.5 42 29.1 86 5.33 38 43.1 25 58.0 30 5.20 28 11.0 66 41.4 73 0.96 37 10.3 82 42.3 58 0.56 92 21.8 61 52.4 64 0.49 44
TriFlow [95]59.7 5.42 63 17.0 81 0.24 86 10.9 82 21.2 82 0.91 84 6.61 68 16.8 87 0.36 33 18.9 72 29.0 79 5.28 25 43.2 41 58.2 58 5.37 72 11.0 66 40.9 55 0.95 32 9.96 52 41.7 38 0.49 62 21.7 50 52.2 50 0.47 15
SIOF [67]60.3 5.64 94 16.5 62 0.28 98 11.3 91 21.6 90 0.91 84 6.32 34 15.9 59 0.42 52 18.7 56 28.4 57 5.36 46 43.0 14 57.9 21 5.17 20 10.7 26 40.2 30 0.95 32 10.1 64 42.4 61 0.50 69 22.2 97 53.2 94 0.53 96
LDOF [28]60.9 5.53 81 15.6 24 0.32 110 11.1 87 20.3 68 1.45 121 6.89 85 17.3 98 0.59 84 19.0 78 28.9 75 5.63 79 43.4 68 58.2 58 5.40 87 10.4 7 39.0 6 0.83 7 9.92 42 42.4 61 0.46 33 21.6 39 52.3 55 0.46 9
Efficient-NL [60]61.3 5.54 82 17.1 83 0.16 26 9.60 44 18.9 39 0.56 36 6.99 90 15.1 41 0.75 100 18.8 64 28.2 50 5.26 24 43.1 25 57.9 21 5.25 41 11.6 104 43.4 118 1.04 53 10.1 64 42.5 68 0.48 56 22.6 108 53.8 106 0.48 28
p-harmonic [29]61.6 5.17 23 15.5 20 0.16 26 11.2 89 21.4 86 0.94 87 6.55 59 17.4 101 0.55 77 19.2 86 28.6 67 5.45 65 43.3 51 58.2 58 5.27 49 10.7 26 40.2 30 1.04 53 10.4 90 43.4 94 0.50 69 21.8 61 52.6 68 0.49 44
F-TV-L1 [15]61.8 5.56 83 16.0 41 0.36 118 11.4 94 21.5 88 0.94 87 6.88 82 17.0 93 0.66 94 18.7 56 27.9 37 5.79 94 42.6 2 57.8 12 5.01 6 10.6 19 39.3 11 1.02 47 10.0 55 41.9 43 0.55 89 22.0 80 52.8 77 0.51 75
Complementary OF [21]61.8 5.28 38 16.7 73 0.15 15 9.39 39 19.5 45 0.58 39 7.53 109 16.3 71 1.10 118 18.7 56 29.0 79 5.35 44 43.2 41 58.2 58 5.26 44 10.9 52 41.2 64 1.16 81 10.3 82 43.4 94 0.55 89 21.5 27 52.2 50 0.51 75
CostFilter [40]62.2 5.44 65 17.7 103 0.13 4 9.64 47 20.1 64 0.45 16 6.96 88 19.1 115 0.47 65 18.5 42 28.9 75 5.13 5 43.6 104 58.5 99 5.32 58 11.1 83 40.5 44 1.48 116 9.94 49 42.1 54 0.45 25 21.8 61 52.6 68 0.49 44
OFH [38]62.8 5.49 73 16.6 67 0.25 90 10.3 69 20.2 67 0.77 69 6.88 82 17.8 103 0.36 33 18.4 35 28.9 75 5.24 20 43.1 25 58.0 30 5.26 44 10.9 52 41.5 78 1.18 84 10.3 82 43.0 88 0.58 97 21.6 39 52.1 43 0.50 62
HBM-GC [105]63.2 5.52 77 17.1 83 0.22 72 9.64 47 19.3 42 0.59 45 5.93 6 13.2 3 0.31 12 18.8 64 28.0 41 5.83 99 44.3 121 59.2 115 5.71 119 11.5 101 43.3 116 1.32 103 9.75 17 40.6 8 0.39 1 22.0 80 52.9 83 0.50 62
CBF [12]63.4 4.98 5 14.8 11 0.18 45 10.2 64 19.9 59 0.71 63 6.63 74 15.2 45 0.42 52 19.0 78 28.5 61 6.39 109 43.4 68 58.3 79 5.49 105 10.7 26 40.4 40 0.95 32 10.1 64 42.6 72 0.50 69 22.3 102 53.5 103 0.53 96
TC/T-Flow [76]63.5 5.73 101 17.3 90 0.22 72 9.66 49 19.7 54 0.63 51 6.24 27 14.9 32 0.32 13 18.6 48 28.7 70 5.38 49 43.5 90 58.4 91 5.50 107 11.0 66 41.4 73 0.89 17 10.2 75 43.0 88 0.58 97 21.9 72 53.0 88 0.45 3
Steered-L1 [118]63.6 5.12 16 16.0 41 0.17 35 9.62 46 19.5 45 0.88 79 7.15 95 15.6 53 1.00 110 19.4 90 28.5 61 6.39 109 43.5 90 58.5 99 5.19 25 10.8 41 40.8 54 1.20 88 9.95 51 42.6 72 0.52 80 21.7 50 52.6 68 0.48 28
GraphCuts [14]64.2 5.98 111 17.5 100 0.24 86 10.0 61 19.5 45 0.76 68 8.24 120 14.6 21 1.06 113 19.7 94 29.0 79 5.69 84 42.9 6 57.9 21 4.97 4 10.5 11 40.3 35 0.87 12 10.0 55 42.4 61 0.58 97 22.1 88 53.2 94 0.51 75
AdaConv-v1 [126]64.7 6.72 120 21.8 124 0.25 90 12.8 112 22.4 107 1.80 126 8.18 119 18.4 109 1.46 125 24.3 125 34.7 126 7.39 120 41.5 1 56.1 1 4.28 1 9.57 2 36.9 2 0.71 2 9.75 17 41.0 16 0.60 101 20.5 2 49.7 2 0.42 2
MLDP_OF [89]65.7 5.44 65 17.2 86 0.17 35 9.84 58 19.9 59 0.62 50 6.19 23 14.8 30 0.28 3 18.6 48 27.4 31 5.71 86 43.3 51 58.2 58 5.34 64 11.9 113 43.3 116 1.57 122 10.4 90 42.6 72 0.56 92 21.7 50 52.3 55 0.59 120
BlockOverlap [61]66.0 5.34 52 14.6 7 0.41 122 11.4 94 20.6 71 1.42 117 6.49 53 14.1 10 0.61 90 18.9 72 26.9 18 7.34 119 44.2 119 58.9 111 5.91 125 11.0 66 39.9 20 1.39 111 9.81 27 41.3 25 0.46 33 21.5 27 51.7 20 0.51 75
SRR-TVOF-NL [91]66.6 5.70 99 16.9 79 0.23 80 10.3 69 21.0 77 0.88 79 6.57 62 16.1 66 0.39 43 19.2 86 28.7 70 5.12 4 43.2 41 58.3 79 5.27 49 10.8 41 40.9 55 0.86 11 10.6 100 42.3 58 0.46 33 22.5 104 53.8 106 0.54 107
Sparse Occlusion [54]67.7 5.43 64 16.8 75 0.23 80 10.3 69 20.8 75 0.63 51 6.51 55 15.0 38 0.44 59 19.0 78 29.0 79 5.42 59 43.4 68 58.2 58 5.41 90 11.3 93 42.9 108 1.14 76 10.1 64 42.2 56 0.42 4 22.1 88 53.2 94 0.49 44
CRTflow [80]68.2 5.48 72 16.5 62 0.34 113 10.7 77 20.7 72 0.86 78 7.25 100 18.6 112 0.60 89 18.8 64 28.8 73 5.98 102 43.4 68 58.2 58 5.43 95 10.7 26 40.4 40 0.95 32 9.93 45 42.0 49 0.49 62 21.7 50 52.3 55 0.49 44
SimpleFlow [49]70.1 5.52 77 17.5 100 0.18 45 10.2 64 19.7 54 0.73 65 7.32 104 15.8 57 1.05 112 18.0 17 26.8 15 5.44 63 43.3 51 58.1 47 5.33 59 11.3 93 42.9 108 1.22 94 10.3 82 44.6 106 1.04 126 21.8 61 52.6 68 0.47 15
FlowNet2 [122]70.6 6.90 121 21.5 123 0.25 90 10.6 76 20.7 72 0.82 73 7.10 93 17.3 98 0.54 74 19.4 90 31.8 117 5.57 75 43.4 68 58.3 79 5.39 81 10.7 26 40.3 35 0.90 18 10.0 55 42.0 49 0.46 33 21.6 39 51.9 34 0.51 75
IAOF [50]71.1 5.97 110 16.8 75 0.29 103 14.1 125 24.8 125 1.41 116 6.05 15 16.2 68 0.61 90 20.1 101 29.5 93 5.47 67 43.0 14 57.8 12 5.19 25 10.7 26 40.3 35 0.94 28 10.4 90 43.3 92 0.46 33 22.0 80 52.8 77 0.54 107
Modified CLG [34]71.4 5.05 11 15.1 13 0.19 53 12.3 109 22.2 102 1.30 110 6.81 79 18.3 108 0.66 94 19.3 89 29.7 96 5.34 40 43.4 68 58.2 58 5.29 56 10.8 41 40.6 48 1.15 78 10.2 75 43.6 96 0.47 49 21.9 72 52.7 73 0.53 96
Aniso-Texture [82]72.5 5.09 14 15.7 26 0.15 15 11.1 87 21.7 91 1.00 92 7.30 103 15.9 59 0.59 84 18.7 56 28.6 67 5.90 100 43.6 104 58.4 91 5.53 110 11.6 104 44.0 123 1.44 114 9.90 39 41.4 30 0.43 10 22.1 88 53.1 90 0.49 44
FlowNetS+ft+v [112]73.0 5.40 60 15.5 20 0.29 103 11.7 102 21.7 91 1.62 123 6.88 82 17.1 95 0.56 78 19.0 78 29.2 88 5.73 90 43.5 90 58.4 91 5.56 112 10.5 11 39.9 20 0.95 32 10.1 64 42.9 85 0.52 80 21.8 61 52.5 67 0.48 28
Occlusion-TV-L1 [63]74.0 5.32 44 16.2 55 0.28 98 11.3 91 21.9 96 0.96 91 6.60 67 16.9 90 0.58 82 19.1 84 28.9 75 5.72 88 43.4 68 58.2 58 5.24 38 10.9 52 40.3 35 1.26 98 10.9 107 42.6 72 0.81 118 21.8 61 52.4 64 0.49 44
Shiralkar [42]74.8 5.73 101 18.1 109 0.21 62 11.6 98 22.0 98 0.88 79 6.74 76 19.9 119 0.73 98 20.3 104 30.1 101 5.46 66 42.6 2 57.5 3 4.99 5 11.3 93 41.5 78 1.35 106 11.0 109 44.9 108 0.67 106 21.5 27 51.7 20 0.48 28
CNN-flow-warp+ref [117]75.7 4.95 4 14.4 5 0.22 72 10.9 82 21.2 82 1.23 107 7.43 107 18.0 105 0.79 102 20.9 112 29.8 97 6.84 115 43.5 90 58.3 79 5.57 113 10.7 26 40.3 35 1.22 94 10.3 82 44.4 105 0.67 106 21.6 39 52.1 43 0.47 15
TCOF [69]75.9 5.56 83 16.8 75 0.17 35 11.8 103 22.1 100 1.02 96 6.09 16 15.0 38 0.30 7 19.0 78 29.4 92 5.67 83 43.4 68 58.3 79 5.17 20 11.4 98 43.1 112 1.02 47 11.0 109 43.9 98 0.48 56 23.1 119 55.1 123 0.52 87
HBpMotionGpu [43]76.1 5.80 105 16.3 57 0.42 123 13.1 115 23.8 117 1.34 112 6.32 34 14.9 32 0.38 39 19.9 95 30.4 104 5.80 95 43.1 25 58.3 79 5.39 81 11.3 93 41.0 61 1.21 91 9.94 49 41.9 43 0.43 10 22.1 88 52.9 83 0.53 96
Fusion [6]77.0 5.37 55 16.9 79 0.21 62 9.33 38 18.3 32 0.54 32 6.39 43 15.1 41 0.54 74 20.0 99 29.8 97 5.41 55 43.5 90 59.2 115 5.14 16 11.5 101 43.7 121 1.21 91 10.5 98 44.1 100 0.52 80 23.1 119 55.4 124 0.52 87
Adaptive [20]77.1 5.50 75 16.7 73 0.30 105 11.8 103 22.2 102 1.02 96 6.58 65 16.5 80 0.53 73 18.6 48 28.0 41 5.60 77 43.5 90 58.3 79 5.21 30 11.0 66 41.3 69 1.09 67 10.4 90 42.8 82 0.46 33 22.2 97 53.5 103 0.54 107
Nguyen [33]78.8 5.63 93 15.9 34 0.23 80 13.8 119 23.8 117 1.37 114 6.89 85 18.7 113 0.59 84 20.8 111 30.8 108 5.44 63 43.1 25 58.1 47 5.14 16 10.6 19 40.4 40 0.93 23 11.9 126 45.9 115 0.73 114 22.0 80 52.8 77 0.52 87
BriefMatch [124]78.8 5.45 67 16.5 62 0.31 109 9.84 58 19.6 51 1.43 118 7.55 110 15.6 53 1.08 115 20.3 104 29.2 88 7.97 127 43.3 51 58.3 79 5.43 95 12.0 116 41.5 78 2.37 129 9.84 31 41.5 32 0.56 92 21.4 19 51.7 20 0.52 87
2D-CLG [1]81.4 5.27 37 15.7 26 0.21 62 13.1 115 22.8 109 1.37 114 7.29 101 17.3 98 0.94 108 20.3 104 30.2 102 5.34 40 43.5 90 58.4 91 5.37 72 10.8 41 40.7 51 1.22 94 10.5 98 44.3 103 0.59 100 22.0 80 52.3 55 0.50 62
TV-L1-improved [17]81.9 5.26 35 16.0 41 0.28 98 11.6 98 22.0 98 1.06 101 7.21 98 16.3 71 0.79 102 18.8 64 28.5 61 5.70 85 43.5 90 58.5 99 5.22 32 11.0 66 41.5 78 1.05 58 10.4 90 44.6 106 0.74 116 22.1 88 53.2 94 0.53 96
SegOF [10]82.1 5.25 32 15.9 34 0.20 58 10.9 82 20.8 75 0.82 73 8.07 118 18.4 109 1.18 119 20.0 99 32.3 118 5.52 73 43.3 51 58.2 58 5.35 67 11.4 98 43.1 112 1.38 110 10.7 102 46.3 116 0.96 124 21.5 27 51.7 20 0.53 96
SPSA-learn [13]82.2 5.45 67 15.4 17 0.25 90 11.6 98 21.4 86 1.15 105 7.65 112 16.6 81 1.26 120 20.1 101 28.2 50 5.30 29 43.3 51 58.2 58 5.42 93 10.9 52 41.0 61 1.14 76 11.6 121 50.4 131 1.71 131 22.2 97 53.3 102 0.49 44
IIOF-NLDP [131]83.3 5.65 97 17.8 105 0.15 15 10.5 74 21.5 88 0.72 64 6.98 89 15.2 45 0.42 52 19.5 92 29.3 91 6.15 106 43.1 25 58.0 30 5.20 28 12.2 122 44.1 125 1.54 120 11.9 126 49.2 129 1.34 130 22.2 97 53.0 88 0.50 62
TriangleFlow [30]84.2 5.85 107 18.2 111 0.26 94 11.0 85 21.8 94 0.79 71 7.17 96 16.3 71 0.58 82 19.6 93 30.7 106 5.74 91 42.8 4 57.8 12 4.95 3 11.6 104 42.8 104 1.05 58 10.8 104 45.8 113 0.73 114 22.8 113 54.3 117 0.51 75
Black & Anandan [4]85.1 5.71 100 15.5 20 0.35 115 12.7 111 22.3 106 1.12 103 7.89 114 18.1 106 1.06 113 20.5 109 30.3 103 5.42 59 43.6 104 58.6 105 5.35 67 10.6 19 39.7 16 0.91 20 10.9 107 44.1 100 0.50 69 22.2 97 52.9 83 0.53 96
Rannacher [23]85.7 5.39 58 16.6 67 0.30 105 11.6 98 22.2 102 1.01 94 7.17 96 16.9 90 0.92 107 18.6 48 28.4 57 5.74 91 43.6 104 58.5 99 5.33 59 11.0 66 41.6 84 1.11 70 10.4 90 44.3 103 0.72 113 21.9 72 52.8 77 0.54 107
ROF-ND [107]86.1 6.15 113 16.4 59 0.14 8 10.4 73 21.1 78 0.70 62 7.09 92 15.9 59 0.40 46 20.7 110 32.9 121 5.82 97 43.4 68 58.2 58 5.37 72 11.6 104 43.4 118 1.16 81 11.6 121 46.4 117 0.55 89 22.6 108 53.8 106 0.54 107
Ad-TV-NDC [36]87.5 6.08 112 15.9 34 0.60 125 13.0 113 22.8 109 1.36 113 6.55 59 16.4 76 0.56 78 20.9 112 30.6 105 6.29 107 44.1 115 59.0 113 5.43 95 10.7 26 39.4 13 1.11 70 10.4 90 43.3 92 0.51 76 22.1 88 52.9 83 0.53 96
IAOF2 [51]89.4 6.17 114 18.3 112 0.30 105 12.0 105 23.3 114 0.93 86 5.90 5 16.1 66 0.42 52 20.4 108 31.2 115 5.75 93 43.7 109 58.9 111 5.39 81 11.2 86 42.0 92 1.08 63 10.3 82 42.7 80 0.48 56 22.7 110 54.2 114 0.52 87
Correlation Flow [75]89.6 5.61 89 17.8 105 0.15 15 10.8 79 21.7 91 0.82 73 6.40 46 14.8 30 0.42 52 19.1 84 29.0 79 6.04 104 43.9 112 58.6 105 6.05 127 12.0 116 43.9 122 1.29 101 11.0 109 45.3 110 0.70 111 22.5 104 54.1 112 0.51 75
Filter Flow [19]90.6 5.64 94 16.4 59 0.32 110 12.2 108 22.2 102 1.08 102 6.61 68 16.2 68 0.57 81 20.3 104 29.0 79 6.32 108 44.1 115 59.1 114 5.74 121 10.9 52 40.7 51 1.04 53 10.2 75 43.2 91 0.54 86 22.7 110 54.3 117 0.54 107
Bartels [41]92.2 5.52 77 17.2 86 0.40 121 10.0 61 20.7 72 0.94 87 6.50 54 15.8 57 0.54 74 19.9 95 30.0 100 7.79 124 44.8 125 59.2 115 6.72 130 12.8 129 42.4 97 3.06 131 10.0 55 42.0 49 0.54 86 22.1 88 53.2 94 0.54 107
Dynamic MRF [7]93.2 5.39 58 17.4 96 0.20 58 10.5 74 21.8 94 0.74 66 7.60 111 20.3 123 0.99 109 21.3 115 31.1 113 7.06 117 43.0 14 58.1 47 5.34 64 11.6 104 43.0 110 1.49 117 10.7 102 45.8 113 0.85 119 22.5 104 53.2 94 0.55 114
LocallyOriented [52]93.3 5.79 104 17.9 107 0.26 94 12.1 107 23.2 113 1.01 94 7.05 91 17.6 102 0.51 69 19.9 95 30.9 111 5.72 88 43.3 51 58.2 58 5.23 35 11.9 113 42.6 100 1.52 119 10.8 104 44.0 99 0.53 84 22.5 104 54.0 110 0.52 87
StereoOF-V1MT [119]96.0 5.94 109 18.8 115 0.20 58 11.3 91 22.6 108 0.94 87 7.95 115 19.6 117 1.00 110 21.6 116 30.7 106 6.76 113 43.3 51 58.3 79 5.37 72 12.1 119 42.6 100 1.82 126 11.6 121 46.7 119 0.90 121 21.8 61 51.8 28 0.50 62
ACK-Prior [27]96.7 5.46 69 17.7 103 0.15 15 9.70 51 20.3 68 0.67 58 7.76 113 16.4 76 1.08 115 19.9 95 31.0 112 6.01 103 44.7 124 59.6 121 5.78 122 12.1 119 44.2 127 1.33 104 10.6 100 44.2 102 0.53 84 23.4 124 56.1 128 0.52 87
TI-DOFE [24]98.2 6.39 116 18.7 114 0.36 118 14.8 126 25.5 128 1.66 124 7.45 108 20.2 121 0.78 101 22.8 122 32.5 119 6.04 104 43.2 41 58.4 91 5.17 20 10.9 52 40.4 40 0.92 21 11.2 114 45.6 112 0.65 105 23.2 121 54.2 114 0.65 125
UnFlow [129]98.8 6.39 116 20.9 119 0.21 62 13.0 113 24.4 124 1.15 105 8.06 117 21.1 124 0.82 105 19.2 86 29.6 95 5.64 80 43.1 25 58.0 30 5.40 87 11.8 112 42.8 104 1.36 108 11.0 109 42.4 61 0.70 111 24.3 130 54.8 121 0.70 127
2bit-BM-tele [98]99.1 5.61 89 15.9 34 0.50 124 11.5 96 21.9 96 1.04 99 6.57 62 15.1 41 0.79 102 20.1 101 29.8 97 7.50 121 44.8 125 59.6 121 6.26 128 12.2 122 42.8 104 2.11 128 11.2 114 49.2 129 1.26 128 21.8 61 52.1 43 0.55 114
Horn & Schunck [3]99.2 5.81 106 17.3 90 0.21 62 13.1 115 23.5 115 1.26 109 8.03 116 19.7 118 1.08 115 22.6 120 32.7 120 5.59 76 43.6 104 58.7 108 5.39 81 10.9 52 40.6 48 1.02 47 11.7 124 46.5 118 0.60 101 22.8 113 53.9 109 0.55 114
StereoFlow [44]99.3 10.4 131 27.1 131 0.35 115 16.3 130 28.4 131 1.03 98 6.55 59 16.8 87 0.50 67 18.8 64 28.2 50 5.38 49 45.7 130 62.1 130 5.58 114 13.6 130 50.3 131 1.28 100 10.0 55 42.4 61 0.49 62 23.0 115 55.5 125 0.56 118
NL-TV-NCC [25]111.1 6.44 118 20.3 118 0.24 86 10.7 77 22.1 100 0.68 60 7.38 106 17.2 96 0.59 84 22.2 119 34.7 126 6.82 114 45.5 129 60.2 128 6.68 129 12.3 126 44.6 129 1.19 86 14.4 131 48.1 126 0.67 106 24.0 129 56.4 129 0.55 114
SILK [79]111.7 6.21 115 19.3 117 0.39 120 13.8 119 24.0 119 1.73 125 8.85 123 20.2 121 1.41 122 21.8 117 31.1 113 7.10 118 43.5 90 58.5 99 5.45 99 11.9 113 41.4 73 2.03 127 10.8 104 45.5 111 0.77 117 22.4 103 53.2 94 0.60 121
HCIC-L [99]111.8 8.84 130 25.2 129 1.06 130 14.0 123 24.1 121 1.43 118 9.42 126 19.3 116 0.69 97 24.3 125 34.1 124 6.48 111 45.1 128 60.1 126 5.86 124 12.1 119 44.1 125 1.06 60 10.2 75 42.6 72 0.51 76 23.6 126 56.0 127 0.51 75
Adaptive flow [45]112.0 7.18 125 19.2 116 0.69 126 15.0 127 25.0 126 2.11 128 7.29 101 16.7 85 0.87 106 22.6 120 31.3 116 7.85 126 44.8 125 60.2 128 5.63 116 11.7 111 43.4 118 1.36 108 10.4 90 43.7 97 0.57 95 23.0 115 54.7 120 0.50 62
Learning Flow [11]114.7 5.91 108 18.6 113 0.30 105 12.0 105 22.9 111 1.00 92 8.30 121 20.0 120 1.33 121 21.9 118 32.9 121 6.94 116 44.5 123 59.7 124 5.97 126 11.5 101 42.6 100 1.35 106 11.3 116 46.8 120 0.69 110 23.7 127 55.9 126 0.62 123
GroupFlow [9]115.0 7.04 123 22.5 125 0.28 98 12.5 110 24.0 119 1.13 104 9.10 124 22.0 126 1.45 123 21.0 114 33.6 123 5.93 101 44.1 115 59.3 119 5.50 107 12.2 122 44.4 128 1.42 113 11.1 113 45.2 109 0.61 104 22.7 110 54.1 112 0.56 118
SLK [47]116.4 6.55 119 21.1 121 0.32 110 13.5 118 23.1 112 1.44 120 9.16 125 21.2 125 1.49 127 24.9 127 34.2 125 7.81 125 43.5 90 58.8 109 5.34 64 12.2 122 43.1 112 1.45 115 11.9 126 48.9 128 0.96 124 23.0 115 54.0 110 0.64 124
Heeger++ [104]117.9 7.79 128 25.2 129 0.17 35 13.9 122 24.2 122 1.33 111 11.8 129 28.7 130 1.49 127 23.4 123 30.8 108 7.63 122 44.4 122 59.9 125 5.62 115 12.6 128 43.1 112 1.77 125 12.6 130 46.9 121 0.87 120 23.2 121 53.5 103 0.60 121
FFV1MT [106]118.5 6.93 122 22.8 126 0.24 86 14.0 123 23.5 115 1.48 122 11.2 128 27.7 129 1.52 129 23.4 123 30.8 108 7.63 122 44.0 113 59.2 115 5.69 118 12.0 116 41.6 84 1.56 121 12.1 129 47.3 124 0.95 123 23.4 124 54.2 114 0.79 129
FOLKI [16]121.3 7.10 124 21.1 121 0.94 129 15.3 128 25.5 128 2.28 129 8.49 122 22.2 127 1.47 126 26.3 129 35.2 128 10.6 130 44.0 113 59.6 121 5.54 111 11.6 104 41.8 90 1.49 117 11.4 118 47.7 125 0.90 121 23.3 123 54.9 122 0.67 126
Pyramid LK [2]122.7 7.19 126 21.0 120 0.93 128 16.2 129 25.1 127 2.91 130 14.0 130 18.5 111 2.57 130 32.5 131 46.2 131 13.7 131 44.2 119 60.1 126 5.48 103 11.6 104 42.5 99 1.40 112 11.4 118 47.2 123 1.28 129 23.7 127 56.7 130 1.08 130
PGAM+LK [55]123.1 7.51 127 23.5 128 0.73 127 13.8 119 24.2 122 1.92 127 9.44 127 22.7 128 1.45 123 26.4 130 36.9 129 10.5 129 44.1 115 59.5 120 5.72 120 12.4 127 44.0 123 1.75 124 11.3 116 47.0 122 0.68 109 23.0 115 54.5 119 0.76 128
Periodicity [78]129.4 8.05 129 23.2 127 1.34 131 20.5 131 27.4 130 3.39 131 15.2 131 30.5 131 4.22 131 26.2 128 43.5 130 9.47 128 46.4 131 62.7 131 6.92 131 13.7 131 44.6 129 2.88 130 11.4 118 48.3 127 1.18 127 25.7 131 59.2 131 1.29 131
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.