Optical flow evaluation results |
Statistics:
Average
SD
R2.5
R5.0
R10.0
A50
A75
A95
Error type: endpoint angle interpolation normalized interpolation |
Average angle error |
avg. |
Army (Hidden texture) GT im0 im1 |
Mequon (Hidden texture) GT im0 im1 |
Schefflera (Hidden texture) GT im0 im1 |
Wooden (Hidden texture) GT im0 im1 |
Grove (Synthetic) GT im0 im1 |
Urban (Synthetic) GT im0 im1 |
Yosemite (Synthetic) GT im0 im1 |
Teddy (Stereo) GT im0 im1 | ||||||||||||||||
rank | all | disc | untext | all | disc | untext | all | disc | untext | all | disc | untext | all | disc | untext | all | disc | untext | all | disc | untext | all | disc | untext | |
NNF-Local [87] | 5.0 | 2.69 3 | 7.56 4 | 1.98 3 | 1.97 4 | 7.01 4 | 1.59 4 | 2.18 2 | 5.36 3 | 1.53 4 | 1.87 2 | 9.14 5 | 1.06 4 | 2.28 2 | 2.94 1 | 1.57 2 | 2.39 5 | 6.78 2 | 2.15 9 | 2.00 20 | 3.36 17 | 1.62 14 | 0.99 1 | 2.16 2 | 0.57 2 |
NN-field [71] | 9.8 | 2.89 8 | 8.13 16 | 2.11 5 | 2.10 6 | 7.15 9 | 1.77 14 | 2.27 4 | 5.59 5 | 1.61 8 | 1.58 1 | 8.52 4 | 0.79 1 | 2.35 4 | 3.05 5 | 1.60 3 | 1.89 1 | 5.20 1 | 1.37 1 | 2.43 46 | 3.70 49 | 1.95 37 | 1.01 2 | 2.25 3 | 0.53 1 |
OFLAF [77] | 12.5 | 3.04 15 | 7.80 10 | 2.40 13 | 2.14 7 | 7.02 5 | 1.72 9 | 2.25 3 | 5.32 2 | 1.56 5 | 2.62 17 | 13.7 23 | 1.37 19 | 2.35 4 | 3.13 6 | 1.62 4 | 2.98 22 | 7.73 7 | 2.57 20 | 2.08 25 | 3.27 11 | 2.05 40 | 1.33 12 | 2.43 6 | 1.40 15 |
PMMST [114] | 13.5 | 3.42 41 | 7.60 5 | 2.65 27 | 2.32 11 | 6.39 1 | 2.20 31 | 2.63 11 | 6.08 8 | 2.03 25 | 2.06 4 | 6.07 1 | 1.44 26 | 2.60 10 | 3.27 8 | 1.91 10 | 2.56 7 | 6.78 2 | 2.09 5 | 2.06 22 | 3.53 36 | 1.63 15 | 1.27 9 | 2.29 4 | 1.02 6 |
nLayers [57] | 16.0 | 2.80 6 | 7.42 3 | 2.20 8 | 2.71 29 | 7.24 10 | 2.55 59 | 2.61 9 | 6.24 9 | 2.45 49 | 2.30 10 | 12.7 11 | 1.16 7 | 2.30 3 | 3.02 3 | 1.70 5 | 2.62 10 | 6.95 4 | 2.09 5 | 2.29 40 | 3.46 26 | 1.89 34 | 1.38 14 | 3.06 17 | 1.29 13 |
MDP-Flow2 [68] | 18.1 | 3.23 30 | 7.93 13 | 2.60 19 | 1.92 2 | 6.64 2 | 1.52 1 | 2.46 7 | 5.91 7 | 1.56 5 | 3.05 41 | 15.8 50 | 1.51 36 | 2.77 23 | 3.50 17 | 2.16 25 | 2.86 18 | 8.58 18 | 2.70 31 | 2.00 20 | 3.50 33 | 1.59 12 | 1.28 10 | 2.67 11 | 0.89 4 |
ComponentFusion [96] | 18.5 | 2.78 5 | 8.20 17 | 2.05 4 | 2.04 5 | 7.31 11 | 1.66 8 | 2.55 8 | 6.78 13 | 1.61 8 | 2.24 9 | 13.1 13 | 1.01 3 | 2.71 20 | 3.56 19 | 2.10 21 | 3.55 50 | 12.4 54 | 3.22 56 | 2.19 35 | 3.60 42 | 1.54 11 | 1.32 11 | 2.91 13 | 1.13 8 |
TC/T-Flow [76] | 20.8 | 2.69 3 | 7.75 9 | 1.87 2 | 2.76 32 | 10.2 45 | 1.73 10 | 3.33 24 | 9.01 30 | 1.49 2 | 2.86 32 | 16.7 60 | 1.21 9 | 2.60 10 | 3.49 16 | 1.90 9 | 2.21 2 | 7.65 5 | 2.04 4 | 1.84 10 | 3.23 8 | 3.14 85 | 2.03 37 | 4.53 37 | 1.49 19 |
FC-2Layers-FF [74] | 24.0 | 3.02 14 | 7.87 12 | 2.61 20 | 2.72 30 | 9.35 35 | 2.29 39 | 2.36 5 | 5.47 4 | 2.15 32 | 2.48 11 | 12.6 10 | 1.28 11 | 2.49 7 | 3.19 7 | 2.03 16 | 3.39 39 | 8.92 20 | 2.83 41 | 2.83 69 | 3.92 62 | 2.80 64 | 1.25 7 | 2.57 10 | 1.20 10 |
WLIF-Flow [93] | 25.1 | 2.96 10 | 7.67 6 | 2.40 13 | 2.41 16 | 7.70 15 | 2.10 25 | 2.98 17 | 7.63 18 | 1.97 24 | 2.71 24 | 13.5 19 | 1.33 14 | 3.01 40 | 4.00 46 | 2.40 42 | 3.03 25 | 8.32 12 | 2.44 16 | 2.09 27 | 3.36 17 | 2.04 39 | 2.26 44 | 4.97 44 | 2.59 49 |
Layers++ [37] | 25.5 | 3.11 17 | 8.22 20 | 2.79 39 | 2.43 19 | 7.02 5 | 2.24 34 | 2.43 6 | 5.77 6 | 2.18 35 | 2.13 6 | 9.71 7 | 1.15 6 | 2.35 4 | 3.02 3 | 1.96 11 | 3.81 56 | 11.4 41 | 3.22 56 | 2.74 64 | 4.01 67 | 2.35 50 | 1.45 15 | 3.05 16 | 1.79 28 |
HAST [109] | 25.6 | 2.58 1 | 7.12 1 | 1.81 1 | 2.41 16 | 7.05 7 | 2.10 25 | 1.83 1 | 4.19 1 | 1.17 1 | 2.84 31 | 15.5 45 | 1.08 5 | 2.23 1 | 2.97 2 | 1.40 1 | 3.72 55 | 10.0 31 | 3.92 77 | 3.40 88 | 4.90 93 | 5.66 114 | 1.20 6 | 2.09 1 | 1.24 11 |
FESL [72] | 27.2 | 2.96 10 | 7.70 7 | 2.54 16 | 3.26 67 | 10.4 46 | 2.56 60 | 3.25 22 | 8.39 22 | 2.17 33 | 2.56 13 | 13.2 14 | 1.31 13 | 2.57 9 | 3.40 11 | 2.12 24 | 2.60 9 | 7.65 5 | 2.30 10 | 2.64 60 | 4.22 75 | 2.47 52 | 1.75 26 | 3.49 25 | 1.71 24 |
AGIF+OF [85] | 27.3 | 3.06 16 | 8.20 17 | 2.55 18 | 3.17 56 | 10.6 49 | 2.46 52 | 3.46 29 | 8.97 29 | 2.24 38 | 2.61 15 | 13.7 23 | 1.33 14 | 2.63 14 | 3.46 14 | 2.11 22 | 2.88 20 | 8.34 14 | 2.35 12 | 2.10 29 | 3.56 38 | 2.09 42 | 1.80 28 | 3.68 28 | 2.24 38 |
Efficient-NL [60] | 27.4 | 2.99 13 | 8.23 21 | 2.28 9 | 2.72 30 | 8.95 31 | 2.25 37 | 3.81 38 | 9.87 36 | 2.07 29 | 2.77 28 | 14.3 30 | 1.46 31 | 2.61 12 | 3.48 15 | 1.96 11 | 3.31 35 | 8.33 13 | 2.59 22 | 2.60 55 | 3.75 50 | 2.54 55 | 1.60 21 | 3.02 14 | 1.66 21 |
LME [70] | 27.5 | 3.15 22 | 8.04 15 | 2.31 11 | 1.95 3 | 6.65 3 | 1.59 4 | 4.03 44 | 9.31 31 | 4.57 90 | 2.69 22 | 13.6 21 | 1.42 23 | 2.85 30 | 3.61 22 | 2.42 43 | 3.47 46 | 12.8 59 | 3.17 52 | 2.12 31 | 3.53 36 | 1.73 17 | 1.34 13 | 2.75 12 | 1.18 9 |
ALD-Flow [66] | 27.6 | 2.82 7 | 7.86 11 | 2.16 6 | 2.84 38 | 10.1 42 | 1.86 16 | 3.73 36 | 10.4 39 | 1.67 11 | 3.10 43 | 16.8 61 | 1.28 11 | 2.69 19 | 3.60 21 | 1.85 8 | 2.79 14 | 11.3 40 | 2.32 11 | 2.07 24 | 3.25 10 | 3.10 82 | 2.03 37 | 5.11 45 | 1.94 31 |
RNLOD-Flow [121] | 28.6 | 2.66 2 | 7.33 2 | 2.17 7 | 2.53 25 | 9.46 36 | 1.86 16 | 3.94 42 | 10.7 45 | 1.95 22 | 2.50 12 | 13.5 19 | 1.21 9 | 2.68 17 | 3.62 24 | 2.05 18 | 2.99 23 | 8.59 19 | 2.75 35 | 3.00 77 | 4.54 82 | 3.25 90 | 1.48 17 | 3.24 20 | 1.76 27 |
IROF++ [58] | 29.0 | 3.17 24 | 8.69 28 | 2.61 20 | 2.79 34 | 9.61 37 | 2.33 40 | 3.43 26 | 8.86 26 | 2.38 44 | 2.87 33 | 14.8 35 | 1.52 38 | 2.74 21 | 3.57 20 | 2.19 26 | 3.20 32 | 9.70 28 | 2.71 32 | 1.96 18 | 3.45 25 | 1.22 5 | 1.80 28 | 4.06 30 | 2.50 45 |
NNF-EAC [103] | 29.1 | 3.31 33 | 8.21 19 | 2.68 29 | 2.19 9 | 7.49 13 | 1.76 12 | 2.73 13 | 6.62 12 | 1.70 12 | 3.18 48 | 15.8 50 | 1.64 47 | 2.87 32 | 3.66 27 | 2.24 28 | 3.02 24 | 8.07 10 | 2.59 22 | 2.19 35 | 3.48 29 | 1.74 18 | 2.85 58 | 6.52 57 | 3.12 61 |
PH-Flow [101] | 29.5 | 3.19 27 | 8.87 33 | 2.71 30 | 2.84 38 | 9.33 34 | 2.37 42 | 2.85 14 | 7.20 15 | 2.36 41 | 2.92 36 | 15.4 42 | 1.51 36 | 2.63 14 | 3.42 12 | 2.04 17 | 3.03 25 | 8.52 17 | 2.49 18 | 2.69 62 | 3.60 42 | 3.13 84 | 1.25 7 | 2.53 8 | 1.34 14 |
Classic+CPF [83] | 30.0 | 3.14 20 | 8.60 26 | 2.63 24 | 3.03 54 | 10.6 49 | 2.33 40 | 3.66 33 | 9.58 32 | 2.20 36 | 2.61 15 | 14.1 28 | 1.34 17 | 2.68 17 | 3.53 18 | 2.21 27 | 2.85 17 | 7.95 9 | 2.38 13 | 2.44 48 | 3.49 31 | 2.90 75 | 1.67 24 | 3.40 23 | 2.43 44 |
Sparse-NonSparse [56] | 31.5 | 3.14 20 | 8.75 30 | 2.76 37 | 3.02 52 | 10.6 49 | 2.43 47 | 3.45 28 | 8.96 27 | 2.36 41 | 2.66 19 | 13.7 23 | 1.42 23 | 2.85 30 | 3.75 33 | 2.33 33 | 3.28 34 | 9.40 25 | 2.73 33 | 2.42 45 | 3.31 13 | 2.69 59 | 1.47 16 | 3.07 18 | 1.66 21 |
TC-Flow [46] | 32.8 | 2.91 9 | 8.00 14 | 2.34 12 | 2.18 8 | 8.77 26 | 1.52 1 | 3.84 40 | 10.7 45 | 1.49 2 | 3.13 44 | 16.6 59 | 1.46 31 | 2.78 24 | 3.73 32 | 1.96 11 | 3.08 28 | 11.4 41 | 2.66 26 | 1.94 16 | 3.43 22 | 3.20 89 | 3.06 62 | 7.04 60 | 4.08 86 |
LSM [39] | 33.8 | 3.12 18 | 8.62 27 | 2.75 36 | 3.00 50 | 10.5 48 | 2.44 49 | 3.43 26 | 8.85 25 | 2.35 40 | 2.66 19 | 13.6 21 | 1.44 26 | 2.82 26 | 3.68 28 | 2.36 35 | 3.38 38 | 9.41 26 | 2.81 39 | 2.69 62 | 3.52 34 | 2.84 68 | 1.59 20 | 3.38 22 | 1.80 29 |
SVFilterOh [111] | 34.3 | 3.63 47 | 8.82 31 | 2.86 41 | 2.60 27 | 8.06 18 | 2.05 24 | 2.95 15 | 7.09 14 | 2.03 25 | 2.80 30 | 13.8 26 | 1.41 22 | 2.63 14 | 3.42 12 | 1.75 7 | 3.49 47 | 10.3 33 | 3.23 58 | 3.63 96 | 5.75 114 | 4.47 107 | 1.09 4 | 2.45 7 | 0.92 5 |
Correlation Flow [75] | 34.4 | 3.38 39 | 8.40 23 | 2.64 25 | 2.23 10 | 7.54 14 | 1.56 3 | 5.14 65 | 13.1 64 | 1.60 7 | 2.09 5 | 8.15 3 | 1.35 18 | 3.12 47 | 4.09 52 | 2.34 34 | 4.01 66 | 11.5 45 | 4.00 78 | 2.59 54 | 3.61 44 | 3.00 80 | 1.49 18 | 3.04 15 | 1.42 17 |
Ramp [62] | 34.8 | 3.18 26 | 8.83 32 | 2.73 33 | 2.89 43 | 10.1 42 | 2.44 49 | 3.27 23 | 8.43 23 | 2.38 44 | 2.74 26 | 14.2 29 | 1.46 31 | 2.82 26 | 3.69 30 | 2.29 31 | 3.37 37 | 9.31 23 | 2.93 45 | 2.62 58 | 3.38 20 | 3.19 88 | 1.54 19 | 3.21 19 | 2.24 38 |
PMF [73] | 35.2 | 3.61 45 | 9.07 36 | 2.62 22 | 2.40 14 | 8.05 17 | 1.83 15 | 2.61 9 | 6.27 10 | 1.65 10 | 3.35 57 | 15.4 42 | 1.58 42 | 2.54 8 | 3.27 8 | 1.71 6 | 3.59 51 | 11.1 39 | 3.46 64 | 4.07 105 | 6.18 119 | 4.02 105 | 1.06 3 | 2.38 5 | 1.25 12 |
ProbFlowFields [128] | 35.5 | 4.18 65 | 12.4 78 | 3.40 72 | 2.43 19 | 8.16 20 | 2.19 30 | 3.65 32 | 9.72 34 | 2.86 64 | 2.22 7 | 9.42 6 | 1.42 23 | 3.01 40 | 3.96 43 | 2.36 35 | 2.73 13 | 10.9 34 | 2.51 19 | 1.89 15 | 3.39 21 | 1.82 23 | 2.59 51 | 6.21 55 | 2.75 52 |
COFM [59] | 35.6 | 3.17 24 | 9.90 51 | 2.46 15 | 2.41 16 | 8.34 23 | 1.92 19 | 3.77 37 | 10.5 40 | 2.54 52 | 2.71 24 | 14.9 37 | 1.19 8 | 3.08 45 | 3.92 41 | 3.25 83 | 3.83 58 | 10.9 34 | 3.15 51 | 2.20 38 | 3.35 15 | 2.91 77 | 1.62 23 | 2.56 9 | 2.09 35 |
FMOF [94] | 36.8 | 3.12 18 | 8.23 21 | 2.73 33 | 3.25 64 | 10.7 56 | 2.52 57 | 3.01 18 | 7.61 17 | 2.20 36 | 2.56 13 | 13.4 17 | 1.33 14 | 2.75 22 | 3.61 22 | 2.24 28 | 3.66 53 | 8.50 16 | 2.78 37 | 2.62 58 | 3.84 57 | 3.27 92 | 2.66 54 | 5.69 48 | 1.95 33 |
OAR-Flow [125] | 37.9 | 3.37 37 | 9.87 50 | 2.67 28 | 4.22 85 | 12.8 80 | 2.87 77 | 4.95 61 | 13.4 67 | 2.66 56 | 3.23 50 | 16.4 58 | 1.37 19 | 2.83 28 | 3.82 36 | 1.97 14 | 2.49 6 | 10.9 34 | 1.87 3 | 1.52 2 | 2.82 1 | 1.86 29 | 1.85 31 | 4.35 35 | 1.68 23 |
Classic+NL [31] | 38.0 | 3.20 29 | 8.72 29 | 2.81 40 | 3.02 52 | 10.6 49 | 2.44 49 | 3.46 29 | 8.84 24 | 2.38 44 | 2.78 29 | 14.3 30 | 1.46 31 | 2.83 28 | 3.68 28 | 2.31 32 | 3.40 40 | 9.09 22 | 2.76 36 | 2.87 71 | 3.82 56 | 2.86 72 | 1.67 24 | 3.53 26 | 2.26 41 |
TV-L1-MCT [64] | 39.1 | 3.16 23 | 8.48 25 | 2.71 30 | 3.28 68 | 10.8 60 | 2.60 67 | 3.95 43 | 10.5 40 | 2.38 44 | 2.69 22 | 13.9 27 | 1.45 30 | 2.94 36 | 3.79 34 | 2.63 61 | 3.50 48 | 9.75 29 | 3.06 49 | 2.08 25 | 3.35 15 | 2.29 48 | 1.95 34 | 3.89 29 | 2.71 51 |
CostFilter [40] | 42.8 | 3.84 50 | 9.64 46 | 3.06 50 | 2.55 26 | 8.09 19 | 2.03 22 | 2.69 12 | 6.47 11 | 1.88 18 | 3.66 68 | 16.8 61 | 1.88 59 | 2.62 13 | 3.34 10 | 1.99 15 | 4.05 67 | 11.0 38 | 3.65 71 | 4.16 107 | 7.18 126 | 4.66 109 | 1.16 5 | 3.36 21 | 0.87 3 |
SimpleFlow [49] | 43.0 | 3.35 34 | 9.20 39 | 2.98 48 | 3.18 59 | 10.7 56 | 2.71 70 | 5.06 63 | 12.6 62 | 2.70 58 | 2.95 38 | 15.1 39 | 1.58 42 | 2.91 35 | 3.79 34 | 2.47 45 | 3.59 51 | 9.49 27 | 2.99 47 | 2.39 43 | 3.46 26 | 2.24 47 | 1.60 21 | 3.56 27 | 1.57 20 |
2DHMM-SAS [92] | 44.9 | 3.19 27 | 8.89 34 | 2.71 30 | 3.20 61 | 11.5 67 | 2.38 43 | 5.19 66 | 12.2 57 | 2.73 60 | 2.92 36 | 15.2 40 | 1.53 39 | 2.79 25 | 3.65 26 | 2.27 30 | 3.45 44 | 9.34 24 | 2.78 37 | 2.66 61 | 3.56 38 | 3.07 81 | 2.34 47 | 5.12 46 | 2.97 59 |
MLDP_OF [89] | 45.9 | 4.13 61 | 10.3 58 | 3.60 80 | 2.34 12 | 7.70 15 | 1.88 18 | 4.23 49 | 10.9 48 | 1.87 17 | 2.74 26 | 14.6 34 | 1.37 19 | 3.10 46 | 3.91 40 | 2.48 49 | 3.40 40 | 9.00 21 | 3.79 74 | 3.46 90 | 4.20 73 | 5.55 113 | 2.31 45 | 4.64 40 | 1.98 34 |
S2D-Matching [84] | 46.1 | 3.36 35 | 9.66 47 | 2.86 41 | 3.19 60 | 11.1 63 | 2.46 52 | 4.86 60 | 12.9 63 | 2.47 50 | 2.67 21 | 13.2 14 | 1.44 26 | 2.87 32 | 3.72 31 | 2.38 38 | 3.45 44 | 9.76 30 | 2.95 46 | 3.05 78 | 3.79 54 | 3.30 94 | 1.95 34 | 4.16 33 | 3.00 60 |
MDP-Flow [26] | 46.2 | 3.48 43 | 9.46 43 | 3.10 52 | 2.45 21 | 7.36 12 | 2.41 44 | 3.21 21 | 8.31 21 | 2.78 62 | 3.18 48 | 17.8 66 | 1.70 52 | 3.03 42 | 3.87 37 | 2.60 57 | 3.43 42 | 12.6 57 | 2.81 39 | 2.19 35 | 3.88 60 | 1.60 13 | 4.13 78 | 9.96 83 | 3.86 82 |
FlowFields+ [130] | 46.3 | 4.57 83 | 13.7 92 | 3.35 64 | 2.94 48 | 10.1 42 | 2.58 64 | 4.05 45 | 10.6 42 | 3.26 73 | 2.90 35 | 13.2 14 | 1.81 57 | 3.18 51 | 4.20 59 | 2.54 51 | 2.68 12 | 11.4 41 | 2.40 15 | 1.84 10 | 3.62 45 | 1.77 19 | 2.48 48 | 5.86 49 | 2.77 53 |
AggregFlow [97] | 47.0 | 4.25 71 | 11.9 76 | 3.26 56 | 4.46 90 | 13.7 89 | 3.43 87 | 4.76 58 | 12.4 58 | 3.93 87 | 3.28 53 | 15.6 46 | 1.68 49 | 2.89 34 | 3.89 38 | 2.08 19 | 2.32 3 | 7.75 8 | 2.14 7 | 2.06 22 | 3.77 52 | 1.48 10 | 2.07 41 | 4.11 31 | 2.36 42 |
CombBMOF [113] | 47.2 | 3.94 54 | 10.6 62 | 2.74 35 | 2.80 35 | 8.55 25 | 2.16 28 | 3.10 20 | 7.99 20 | 1.76 13 | 2.99 39 | 13.4 17 | 1.95 64 | 3.04 43 | 3.89 38 | 2.49 50 | 5.64 94 | 12.3 52 | 6.74 108 | 3.54 92 | 5.16 101 | 2.81 65 | 1.85 31 | 4.60 39 | 1.10 7 |
IROF-TV [53] | 47.2 | 3.40 40 | 9.29 41 | 2.95 47 | 2.99 49 | 11.1 63 | 2.53 58 | 3.81 38 | 9.81 35 | 2.44 48 | 3.25 52 | 16.9 63 | 1.78 56 | 3.27 65 | 4.10 53 | 2.93 75 | 4.47 74 | 16.0 86 | 3.53 66 | 1.70 4 | 3.21 6 | 1.12 3 | 1.91 33 | 4.75 42 | 2.19 37 |
S2F-IF [123] | 48.8 | 4.51 81 | 13.6 90 | 3.31 60 | 2.90 44 | 10.4 46 | 2.48 55 | 4.07 47 | 10.8 47 | 3.15 70 | 3.31 54 | 15.7 49 | 1.90 60 | 3.17 49 | 4.19 57 | 2.55 54 | 2.81 16 | 11.6 47 | 2.60 24 | 1.86 13 | 3.67 47 | 1.87 30 | 2.11 43 | 4.64 40 | 2.54 48 |
NL-TV-NCC [25] | 51.1 | 3.89 52 | 9.16 38 | 2.98 48 | 2.87 42 | 9.69 38 | 1.99 20 | 4.44 53 | 11.6 52 | 1.76 13 | 2.64 18 | 11.8 9 | 1.48 35 | 3.49 80 | 4.60 87 | 2.47 45 | 4.67 81 | 13.5 64 | 4.26 86 | 2.83 69 | 4.57 84 | 2.84 68 | 2.62 52 | 6.00 53 | 2.25 40 |
FlowFields [110] | 51.4 | 4.57 83 | 13.7 92 | 3.38 67 | 3.01 51 | 10.6 49 | 2.59 65 | 4.19 48 | 11.1 49 | 3.30 74 | 3.17 47 | 15.0 38 | 1.96 65 | 3.21 58 | 4.24 66 | 2.61 60 | 2.91 21 | 12.4 54 | 2.66 26 | 1.84 10 | 3.46 26 | 1.84 27 | 2.50 49 | 6.15 54 | 2.79 54 |
Sparse Occlusion [54] | 51.7 | 3.62 46 | 9.12 37 | 2.90 43 | 2.92 46 | 9.08 32 | 2.56 60 | 4.49 55 | 11.8 55 | 2.11 31 | 3.14 45 | 15.8 50 | 1.57 41 | 3.26 63 | 4.22 62 | 2.36 35 | 3.52 49 | 10.9 34 | 2.66 26 | 5.10 122 | 6.32 120 | 3.15 86 | 2.02 36 | 4.92 43 | 1.71 24 |
EPPM w/o HM [88] | 52.0 | 4.25 71 | 11.1 66 | 3.13 53 | 2.36 13 | 8.35 24 | 1.76 12 | 3.72 35 | 10.2 38 | 1.81 15 | 3.24 51 | 14.5 33 | 1.94 62 | 3.16 48 | 3.94 42 | 2.82 70 | 4.78 84 | 12.9 60 | 4.32 87 | 3.64 98 | 4.54 82 | 5.73 115 | 1.76 27 | 4.11 31 | 1.94 31 |
OFH [38] | 52.2 | 3.90 53 | 9.77 49 | 3.62 83 | 2.84 38 | 11.0 62 | 2.04 23 | 5.52 71 | 14.4 72 | 1.89 19 | 3.52 60 | 20.5 83 | 1.60 45 | 3.18 51 | 4.06 51 | 2.82 70 | 3.86 59 | 14.1 72 | 3.59 68 | 1.77 6 | 3.62 45 | 1.81 22 | 2.64 53 | 7.08 62 | 2.15 36 |
PGM-C [120] | 53.2 | 4.62 88 | 14.0 97 | 3.39 69 | 3.29 70 | 12.3 72 | 2.70 69 | 4.39 52 | 11.7 53 | 3.43 77 | 4.00 76 | 19.8 74 | 2.15 70 | 3.19 53 | 4.23 63 | 2.54 51 | 2.79 14 | 11.9 49 | 2.45 17 | 1.83 8 | 3.21 6 | 1.83 25 | 2.31 45 | 5.87 50 | 1.82 30 |
Occlusion-TV-L1 [63] | 54.2 | 3.59 44 | 9.61 44 | 2.64 25 | 2.93 47 | 10.6 49 | 2.41 44 | 6.16 77 | 15.2 75 | 2.70 58 | 3.32 55 | 17.0 64 | 1.68 49 | 3.38 70 | 4.44 77 | 2.82 70 | 3.10 30 | 13.2 63 | 2.68 29 | 2.17 32 | 3.52 34 | 1.46 8 | 4.63 88 | 11.1 97 | 3.53 71 |
Complementary OF [21] | 55.1 | 4.44 77 | 11.2 69 | 4.04 90 | 2.51 24 | 9.77 40 | 1.74 11 | 3.93 41 | 10.6 42 | 2.04 27 | 3.87 72 | 18.8 69 | 2.19 73 | 3.17 49 | 4.00 46 | 2.92 74 | 4.64 79 | 13.8 69 | 3.64 70 | 2.17 32 | 3.36 17 | 2.51 53 | 3.08 63 | 7.04 60 | 3.65 75 |
Adaptive [20] | 56.1 | 3.29 31 | 9.43 42 | 2.28 9 | 3.10 55 | 11.4 66 | 2.46 52 | 6.58 81 | 15.7 81 | 2.52 51 | 3.14 45 | 15.6 46 | 1.56 40 | 3.67 89 | 4.46 79 | 3.48 92 | 3.32 36 | 13.0 62 | 2.38 13 | 2.76 67 | 4.39 79 | 1.93 36 | 3.58 70 | 8.18 69 | 2.88 56 |
Aniso-Texture [82] | 56.2 | 2.96 10 | 7.72 8 | 2.54 16 | 2.48 23 | 8.26 22 | 2.24 34 | 6.48 79 | 15.9 85 | 2.63 54 | 1.96 3 | 10.1 8 | 0.98 2 | 3.26 63 | 4.21 60 | 2.60 57 | 5.74 96 | 16.9 94 | 5.61 99 | 4.47 114 | 5.88 117 | 3.33 95 | 3.51 69 | 7.12 63 | 3.68 77 |
ACK-Prior [27] | 56.7 | 4.19 67 | 9.27 40 | 3.60 80 | 2.40 14 | 8.21 21 | 1.65 7 | 3.40 25 | 8.96 27 | 1.84 16 | 2.87 33 | 14.4 32 | 1.44 26 | 3.36 69 | 4.15 54 | 3.07 78 | 6.35 104 | 16.1 88 | 4.90 93 | 4.21 109 | 4.80 88 | 6.03 117 | 3.29 66 | 5.99 52 | 2.82 55 |
DPOF [18] | 58.9 | 4.67 92 | 12.6 83 | 3.30 58 | 3.57 76 | 10.6 49 | 3.12 84 | 3.09 19 | 7.50 16 | 2.32 39 | 3.06 42 | 14.8 35 | 1.82 58 | 3.21 58 | 4.18 56 | 2.79 69 | 4.47 74 | 12.5 56 | 3.33 59 | 4.09 106 | 3.92 62 | 6.96 119 | 2.09 42 | 4.39 36 | 1.74 26 |
CPM-Flow [116] | 58.9 | 4.63 89 | 14.1 100 | 3.39 69 | 3.33 71 | 12.5 76 | 2.73 71 | 4.37 50 | 11.7 53 | 3.43 77 | 4.00 76 | 19.9 76 | 2.14 69 | 3.19 53 | 4.23 63 | 2.54 51 | 3.08 28 | 12.0 50 | 2.88 43 | 1.87 14 | 3.44 23 | 1.84 27 | 2.91 60 | 7.48 67 | 2.91 58 |
EpicFlow [102] | 59.2 | 4.61 87 | 14.0 97 | 3.39 69 | 3.33 71 | 12.5 76 | 2.74 72 | 5.37 68 | 14.8 74 | 3.46 80 | 3.94 75 | 19.2 71 | 2.13 68 | 3.20 55 | 4.23 63 | 2.58 56 | 2.87 19 | 12.2 51 | 2.64 25 | 1.83 8 | 3.28 12 | 1.83 25 | 3.21 65 | 7.12 63 | 3.61 72 |
Kuang [131] | 59.6 | 4.36 74 | 13.6 90 | 3.21 55 | 3.21 62 | 12.5 76 | 2.51 56 | 4.46 54 | 12.4 58 | 3.07 67 | 3.54 61 | 17.8 66 | 1.94 62 | 3.29 66 | 4.34 70 | 2.69 65 | 4.16 71 | 14.2 73 | 4.09 80 | 1.77 6 | 3.34 14 | 1.82 23 | 2.73 56 | 6.78 58 | 3.40 68 |
DeepFlow2 [108] | 59.8 | 4.04 58 | 11.2 69 | 3.38 67 | 3.80 78 | 12.4 75 | 2.86 76 | 5.12 64 | 13.4 67 | 3.00 65 | 4.17 81 | 20.1 78 | 2.18 72 | 2.96 37 | 3.97 44 | 2.08 19 | 3.06 27 | 12.6 57 | 2.69 30 | 2.17 32 | 3.24 9 | 2.71 60 | 4.74 90 | 10.4 90 | 4.38 91 |
ROF-ND [107] | 60.3 | 4.12 59 | 10.0 52 | 3.37 66 | 2.78 33 | 8.82 28 | 2.12 27 | 4.61 57 | 11.9 56 | 2.09 30 | 2.23 8 | 6.56 2 | 1.69 51 | 3.60 86 | 4.75 97 | 2.85 73 | 4.92 87 | 13.6 67 | 3.75 72 | 4.59 116 | 5.18 102 | 4.10 106 | 2.67 55 | 5.19 47 | 3.46 70 |
TCOF [69] | 60.5 | 4.17 64 | 10.4 60 | 3.71 85 | 3.17 56 | 10.7 56 | 2.59 65 | 6.58 81 | 15.7 81 | 3.82 85 | 3.69 70 | 16.1 55 | 2.37 80 | 3.78 92 | 4.95 106 | 2.47 45 | 2.59 8 | 8.47 15 | 2.58 21 | 3.66 99 | 4.83 89 | 2.67 58 | 1.83 30 | 4.20 34 | 1.46 18 |
RFlow [90] | 61.5 | 3.82 49 | 10.0 52 | 3.44 75 | 2.61 28 | 9.73 39 | 2.02 21 | 5.66 73 | 14.5 73 | 2.05 28 | 3.93 74 | 23.1 97 | 1.90 60 | 3.24 60 | 4.19 57 | 2.66 64 | 4.12 70 | 15.2 82 | 3.34 61 | 2.61 56 | 3.56 38 | 2.65 57 | 4.48 84 | 10.5 93 | 3.93 85 |
Steered-L1 [118] | 62.1 | 3.30 32 | 8.44 24 | 2.91 44 | 1.89 1 | 7.14 8 | 1.60 6 | 3.61 31 | 9.91 37 | 1.89 19 | 3.45 58 | 19.4 73 | 1.64 47 | 3.42 73 | 4.30 68 | 3.39 86 | 5.18 89 | 14.5 75 | 4.37 89 | 5.09 121 | 5.05 97 | 10.1 123 | 5.56 97 | 10.2 88 | 6.24 104 |
HBM-GC [105] | 62.2 | 5.25 97 | 10.5 61 | 4.34 97 | 3.17 56 | 8.78 27 | 2.94 80 | 4.38 51 | 10.6 42 | 2.68 57 | 3.59 64 | 12.8 12 | 2.47 83 | 2.96 37 | 3.64 25 | 2.64 62 | 3.96 65 | 8.26 11 | 3.56 67 | 4.40 112 | 5.92 118 | 3.62 99 | 2.55 50 | 6.34 56 | 3.29 64 |
ComplOF-FED-GPU [35] | 64.2 | 4.28 73 | 11.3 71 | 3.70 84 | 3.25 64 | 13.0 82 | 2.16 28 | 4.06 46 | 11.2 50 | 1.95 22 | 3.91 73 | 19.2 71 | 2.01 66 | 3.20 55 | 4.15 54 | 2.64 62 | 4.61 78 | 16.1 88 | 3.90 76 | 2.98 76 | 3.77 52 | 3.69 100 | 2.85 58 | 7.44 66 | 2.53 47 |
SRR-TVOF-NL [91] | 64.4 | 4.47 79 | 10.9 64 | 3.32 62 | 4.04 82 | 13.2 85 | 2.90 78 | 4.81 59 | 12.5 60 | 3.15 70 | 3.33 56 | 15.3 41 | 1.61 46 | 3.24 60 | 4.03 50 | 2.70 67 | 3.94 63 | 11.8 48 | 3.33 59 | 4.16 107 | 5.21 105 | 3.44 98 | 2.06 40 | 3.48 24 | 2.42 43 |
TF+OM [100] | 66.5 | 3.97 55 | 10.2 55 | 2.94 46 | 2.91 45 | 9.12 33 | 2.57 63 | 5.22 67 | 11.5 51 | 6.92 95 | 3.59 64 | 16.1 55 | 2.28 77 | 3.20 55 | 3.97 44 | 3.11 79 | 4.70 82 | 14.5 75 | 4.32 87 | 3.06 80 | 4.84 90 | 2.71 60 | 3.93 74 | 8.79 74 | 4.32 90 |
Aniso. Huber-L1 [22] | 67.2 | 3.71 48 | 10.1 54 | 3.08 51 | 4.36 89 | 13.0 82 | 3.77 91 | 6.92 85 | 15.3 77 | 3.60 83 | 3.54 61 | 15.9 53 | 2.04 67 | 3.38 70 | 4.45 78 | 2.47 45 | 3.88 60 | 12.9 60 | 2.74 34 | 3.37 87 | 4.36 78 | 2.85 71 | 3.16 64 | 7.52 68 | 2.90 57 |
DeepFlow [86] | 68.5 | 4.49 80 | 11.7 73 | 4.14 92 | 4.26 86 | 12.8 80 | 3.36 85 | 5.96 74 | 14.2 71 | 5.10 91 | 4.89 92 | 23.1 97 | 2.67 86 | 2.98 39 | 4.00 46 | 2.11 22 | 3.26 33 | 13.5 64 | 2.84 42 | 2.09 27 | 3.10 3 | 2.77 62 | 5.83 99 | 11.4 99 | 5.45 101 |
TV-L1-improved [17] | 69.0 | 3.36 35 | 9.63 45 | 2.62 22 | 2.82 36 | 10.7 56 | 2.23 32 | 6.50 80 | 15.8 83 | 2.73 60 | 3.80 71 | 21.3 88 | 1.76 55 | 3.34 68 | 4.38 75 | 2.39 39 | 5.97 98 | 18.1 100 | 5.67 100 | 3.57 94 | 4.92 95 | 3.43 97 | 4.01 77 | 9.84 82 | 3.44 69 |
Classic++ [32] | 69.0 | 3.37 37 | 9.67 48 | 2.91 44 | 3.28 68 | 12.1 70 | 2.61 68 | 5.46 70 | 14.1 70 | 3.00 65 | 3.63 66 | 20.2 81 | 1.70 52 | 3.24 60 | 4.34 70 | 2.60 57 | 4.65 80 | 16.0 86 | 3.60 69 | 3.09 81 | 3.94 65 | 3.28 93 | 4.64 89 | 10.4 90 | 3.71 78 |
LocallyOriented [52] | 71.9 | 4.54 82 | 12.8 85 | 3.27 57 | 4.73 94 | 14.8 96 | 3.73 90 | 7.77 91 | 18.3 98 | 3.44 79 | 3.56 63 | 15.6 46 | 2.22 74 | 3.46 77 | 4.47 80 | 2.69 65 | 3.15 31 | 10.2 32 | 3.19 54 | 2.61 56 | 4.20 73 | 2.52 54 | 4.39 81 | 8.52 71 | 5.23 97 |
SIOF [67] | 72.1 | 4.23 69 | 10.2 55 | 3.31 60 | 3.97 80 | 14.5 94 | 2.97 81 | 7.81 93 | 16.4 88 | 7.48 96 | 4.82 88 | 20.1 78 | 2.96 89 | 3.54 83 | 4.49 81 | 3.12 80 | 4.31 72 | 13.5 64 | 4.13 82 | 2.36 42 | 3.59 41 | 1.68 16 | 3.46 68 | 7.39 65 | 3.37 66 |
TriangleFlow [30] | 73.9 | 4.12 59 | 10.6 62 | 3.47 76 | 3.47 75 | 13.1 84 | 2.41 44 | 6.00 75 | 15.2 75 | 2.17 33 | 2.99 39 | 16.0 54 | 1.58 42 | 4.46 113 | 5.79 118 | 4.15 105 | 5.42 92 | 13.9 71 | 5.24 94 | 3.10 83 | 5.47 110 | 2.90 75 | 3.02 61 | 6.82 59 | 3.64 74 |
CRTflow [80] | 74.5 | 4.18 65 | 11.8 75 | 3.20 54 | 3.22 63 | 10.8 60 | 2.43 47 | 6.20 78 | 15.5 79 | 2.63 54 | 4.21 82 | 22.0 91 | 2.24 75 | 3.32 67 | 4.34 70 | 2.44 44 | 7.43 111 | 19.3 106 | 8.15 114 | 2.55 52 | 4.09 69 | 2.59 56 | 4.60 86 | 11.2 98 | 4.45 92 |
Brox et al. [5] | 74.8 | 4.44 77 | 12.4 78 | 4.22 95 | 3.72 77 | 13.5 88 | 3.06 82 | 4.97 62 | 13.3 66 | 3.11 68 | 4.58 86 | 22.0 91 | 2.37 80 | 3.79 94 | 4.60 87 | 4.33 109 | 3.91 62 | 17.0 95 | 3.45 63 | 2.22 39 | 3.79 54 | 1.19 4 | 4.62 87 | 10.0 84 | 3.38 67 |
BriefMatch [124] | 75.9 | 3.44 42 | 9.01 35 | 2.77 38 | 2.85 41 | 9.93 41 | 2.23 32 | 2.97 16 | 7.65 19 | 1.94 21 | 3.64 67 | 20.1 78 | 1.75 54 | 4.10 107 | 4.90 104 | 5.82 119 | 7.95 113 | 17.8 98 | 8.08 113 | 4.73 118 | 5.20 103 | 12.2 125 | 7.88 115 | 12.0 102 | 13.7 121 |
Rannacher [23] | 76.8 | 4.13 61 | 11.0 65 | 3.61 82 | 3.39 73 | 12.3 72 | 2.80 75 | 7.26 87 | 17.4 94 | 3.59 82 | 4.40 84 | 23.1 97 | 2.24 75 | 3.43 75 | 4.54 84 | 2.56 55 | 5.41 91 | 18.5 101 | 4.23 84 | 2.92 73 | 3.91 61 | 2.82 66 | 3.45 67 | 9.14 75 | 3.27 63 |
F-TV-L1 [15] | 77.9 | 5.44 100 | 12.5 82 | 5.69 104 | 5.46 98 | 15.0 99 | 4.03 93 | 7.48 88 | 16.3 87 | 3.42 76 | 5.08 94 | 23.3 100 | 2.81 88 | 3.42 73 | 4.34 70 | 3.03 76 | 4.05 67 | 15.1 81 | 3.18 53 | 2.43 46 | 3.92 62 | 1.87 30 | 3.90 73 | 9.35 79 | 2.61 50 |
Local-TV-L1 [65] | 78.5 | 5.33 98 | 12.6 83 | 5.19 102 | 6.90 104 | 15.7 102 | 6.22 102 | 10.0 104 | 18.2 97 | 8.89 97 | 5.81 100 | 24.7 105 | 3.70 99 | 3.05 44 | 4.00 46 | 2.39 39 | 4.05 67 | 14.6 77 | 3.09 50 | 1.95 17 | 3.11 4 | 2.15 44 | 5.85 100 | 10.8 95 | 7.34 107 |
SuperFlow [81] | 78.5 | 4.16 63 | 11.1 66 | 3.32 62 | 4.80 95 | 12.2 71 | 4.68 97 | 7.80 92 | 16.0 86 | 10.6 105 | 5.16 96 | 22.4 95 | 3.24 96 | 3.39 72 | 4.24 66 | 3.71 96 | 3.44 43 | 13.7 68 | 2.91 44 | 3.19 84 | 4.62 86 | 1.87 30 | 4.74 90 | 10.6 94 | 4.24 88 |
DF-Auto [115] | 78.8 | 5.04 96 | 13.7 92 | 3.30 58 | 6.51 101 | 14.1 93 | 6.09 101 | 8.14 97 | 16.5 89 | 10.2 103 | 5.06 93 | 21.3 88 | 3.10 95 | 3.74 90 | 4.91 105 | 3.25 83 | 2.67 11 | 11.4 41 | 2.14 7 | 3.36 86 | 5.23 106 | 1.45 7 | 4.45 83 | 9.18 76 | 4.28 89 |
TriFlow [95] | 79.1 | 4.73 94 | 12.4 78 | 3.49 78 | 4.03 81 | 12.5 76 | 3.70 89 | 8.18 99 | 17.2 92 | 10.4 104 | 3.50 59 | 15.4 42 | 2.32 79 | 3.43 75 | 4.21 60 | 3.42 87 | 3.90 61 | 12.3 52 | 3.76 73 | 7.86 127 | 5.72 113 | 16.2 127 | 2.80 57 | 5.89 51 | 2.50 45 |
CLG-TV [48] | 79.5 | 4.00 56 | 10.3 58 | 3.40 72 | 4.33 88 | 12.3 72 | 4.08 94 | 6.78 83 | 15.5 79 | 3.64 84 | 4.07 78 | 17.7 65 | 2.39 82 | 3.79 94 | 4.86 100 | 3.23 82 | 4.48 76 | 16.5 92 | 3.80 75 | 3.55 93 | 4.65 87 | 2.89 74 | 4.00 76 | 10.1 86 | 3.18 62 |
CBF [12] | 81.0 | 3.88 51 | 10.2 55 | 3.50 79 | 4.60 92 | 11.3 65 | 5.06 98 | 5.43 69 | 13.1 64 | 3.39 75 | 4.09 79 | 21.2 87 | 2.16 71 | 3.80 97 | 4.72 96 | 3.52 93 | 4.33 73 | 14.4 74 | 3.01 48 | 4.97 119 | 5.51 111 | 4.93 111 | 3.99 75 | 9.27 78 | 3.91 84 |
Bartels [41] | 82.2 | 4.43 75 | 11.1 66 | 4.17 94 | 2.83 37 | 8.84 29 | 2.56 60 | 4.54 56 | 12.5 60 | 2.80 63 | 4.87 89 | 22.1 93 | 3.05 93 | 3.58 85 | 4.35 74 | 4.15 105 | 5.55 93 | 17.5 96 | 5.78 101 | 3.74 100 | 5.02 96 | 5.98 116 | 5.21 96 | 11.9 101 | 5.20 96 |
Fusion [6] | 82.5 | 4.43 75 | 13.7 92 | 4.08 91 | 2.47 22 | 8.91 30 | 2.24 34 | 3.70 34 | 9.68 33 | 3.12 69 | 3.68 69 | 19.8 74 | 2.54 85 | 4.26 110 | 5.16 111 | 4.31 108 | 6.32 101 | 16.8 93 | 6.15 105 | 4.55 115 | 5.78 115 | 3.10 82 | 7.12 110 | 13.6 111 | 7.86 111 |
p-harmonic [29] | 83.2 | 4.64 90 | 13.0 86 | 4.43 98 | 3.41 74 | 11.9 68 | 2.93 79 | 7.60 89 | 18.1 96 | 3.96 88 | 4.65 87 | 21.0 85 | 2.97 91 | 3.46 77 | 4.33 69 | 3.34 85 | 4.75 83 | 17.5 96 | 4.60 92 | 3.05 78 | 4.17 71 | 2.15 44 | 5.09 95 | 10.9 96 | 3.77 80 |
CNN-flow-warp+ref [117] | 84.2 | 4.93 95 | 14.5 104 | 4.29 96 | 4.18 84 | 11.9 68 | 4.24 95 | 8.23 100 | 19.7 105 | 6.35 94 | 5.13 95 | 24.4 104 | 2.96 89 | 3.55 84 | 4.40 76 | 3.85 98 | 3.82 57 | 15.0 79 | 3.39 62 | 1.96 18 | 3.44 23 | 2.14 43 | 10.0 119 | 14.8 116 | 10.8 117 |
Dynamic MRF [7] | 84.8 | 4.58 85 | 12.4 78 | 4.14 92 | 3.25 64 | 13.9 90 | 2.27 38 | 6.02 76 | 16.8 90 | 2.36 41 | 4.39 83 | 22.6 96 | 2.51 84 | 3.61 87 | 4.55 85 | 3.46 89 | 6.81 106 | 22.2 116 | 6.78 110 | 2.41 44 | 3.48 29 | 3.69 100 | 9.26 117 | 17.8 120 | 10.2 114 |
SegOF [10] | 85.2 | 5.85 101 | 13.5 89 | 3.98 89 | 7.40 105 | 14.9 97 | 8.13 110 | 8.55 102 | 17.3 93 | 9.01 98 | 6.50 104 | 18.1 68 | 5.14 106 | 3.90 101 | 4.53 83 | 4.81 113 | 6.57 105 | 21.7 114 | 6.81 111 | 1.65 3 | 3.49 31 | 1.08 2 | 3.71 71 | 9.23 77 | 3.63 73 |
FlowNetS+ft+v [112] | 86.0 | 4.22 68 | 12.1 77 | 3.48 77 | 4.50 91 | 13.4 86 | 3.85 92 | 8.29 101 | 18.4 99 | 6.20 93 | 4.87 89 | 21.6 90 | 3.01 92 | 3.93 102 | 5.04 108 | 3.47 91 | 3.71 54 | 15.3 83 | 3.21 55 | 3.32 85 | 5.12 99 | 3.87 102 | 3.76 72 | 9.44 80 | 3.74 79 |
LDOF [28] | 86.8 | 4.60 86 | 13.0 86 | 3.77 86 | 4.67 93 | 15.5 101 | 3.67 88 | 5.63 72 | 14.0 69 | 4.21 89 | 5.80 99 | 27.1 114 | 3.43 97 | 3.52 82 | 4.50 82 | 3.46 89 | 4.84 86 | 17.8 98 | 4.04 79 | 2.46 50 | 4.14 70 | 3.25 90 | 4.85 93 | 12.0 102 | 3.78 81 |
Second-order prior [8] | 86.8 | 4.03 57 | 11.6 72 | 3.35 64 | 3.88 79 | 14.0 92 | 3.08 83 | 7.21 86 | 17.6 95 | 3.57 81 | 4.14 80 | 19.9 76 | 2.31 78 | 3.66 88 | 4.86 100 | 2.73 68 | 7.32 109 | 21.2 112 | 6.76 109 | 4.02 103 | 4.58 85 | 4.01 104 | 4.27 79 | 10.4 90 | 5.12 93 |
FlowNet2 [122] | 90.2 | 8.58 115 | 18.6 113 | 6.31 106 | 9.39 112 | 17.6 107 | 9.09 113 | 8.06 96 | 15.8 83 | 9.81 101 | 5.61 98 | 16.2 57 | 4.12 101 | 4.04 105 | 4.88 102 | 3.79 97 | 4.92 87 | 16.2 90 | 4.50 90 | 4.28 110 | 6.73 122 | 2.84 68 | 2.05 39 | 4.54 38 | 1.41 16 |
StereoFlow [44] | 90.4 | 17.1 129 | 28.1 129 | 17.9 128 | 18.7 126 | 29.7 127 | 16.5 121 | 20.1 126 | 30.9 126 | 17.5 123 | 21.2 127 | 38.3 128 | 17.9 125 | 4.60 114 | 5.05 109 | 5.52 115 | 2.38 4 | 11.5 45 | 1.77 2 | 1.25 1 | 2.92 2 | 0.71 1 | 4.49 85 | 10.3 89 | 4.23 87 |
Ad-TV-NDC [36] | 92.0 | 8.36 114 | 14.0 97 | 11.1 122 | 12.9 119 | 19.9 113 | 12.8 119 | 14.4 115 | 23.1 108 | 12.1 109 | 7.40 107 | 20.6 84 | 6.33 107 | 3.47 79 | 4.66 92 | 2.39 39 | 3.95 64 | 13.8 69 | 3.51 65 | 2.48 51 | 3.75 50 | 2.05 40 | 9.75 118 | 12.1 104 | 16.7 124 |
Learning Flow [11] | 95.0 | 4.23 69 | 11.7 73 | 3.41 74 | 4.16 83 | 15.3 100 | 3.42 86 | 6.78 83 | 16.9 91 | 3.83 86 | 6.41 103 | 25.3 108 | 4.25 102 | 4.66 116 | 6.01 123 | 4.00 101 | 6.33 103 | 20.7 111 | 5.30 95 | 3.09 81 | 4.84 90 | 2.91 77 | 7.08 109 | 15.0 117 | 5.27 98 |
Shiralkar [42] | 95.2 | 4.64 90 | 14.1 100 | 3.94 87 | 4.29 87 | 16.9 105 | 2.77 73 | 7.75 90 | 18.8 101 | 3.19 72 | 5.54 97 | 25.0 107 | 3.56 98 | 3.51 81 | 4.55 85 | 3.04 77 | 7.41 110 | 20.1 110 | 6.41 106 | 3.76 101 | 4.35 77 | 5.28 112 | 6.56 106 | 14.4 115 | 5.30 99 |
StereoOF-V1MT [119] | 95.8 | 4.71 93 | 14.1 100 | 3.95 88 | 5.10 97 | 20.3 115 | 2.78 74 | 7.98 95 | 20.7 106 | 2.57 53 | 4.48 85 | 21.1 86 | 2.79 87 | 4.20 109 | 5.29 113 | 4.10 103 | 6.85 108 | 22.3 117 | 6.42 107 | 2.45 49 | 4.17 71 | 3.15 86 | 10.5 120 | 18.4 123 | 10.5 115 |
IAOF2 [51] | 96.6 | 5.38 99 | 13.7 92 | 4.50 99 | 5.95 100 | 14.6 95 | 5.61 100 | 8.80 103 | 18.8 101 | 9.40 99 | 12.2 117 | 23.8 103 | 13.1 120 | 3.86 98 | 4.89 103 | 3.12 80 | 5.21 90 | 14.9 78 | 4.54 91 | 4.33 111 | 5.15 100 | 3.93 103 | 4.39 81 | 8.57 72 | 3.87 83 |
Modified CLG [34] | 98.0 | 7.17 109 | 17.1 112 | 6.47 108 | 6.85 103 | 14.9 97 | 7.48 106 | 14.0 111 | 24.8 112 | 15.7 119 | 8.35 110 | 27.3 115 | 6.36 108 | 3.96 103 | 4.99 107 | 4.08 102 | 4.54 77 | 19.3 106 | 4.15 83 | 2.33 41 | 3.86 59 | 2.40 51 | 6.00 101 | 13.8 113 | 5.40 100 |
2D-CLG [1] | 98.3 | 10.1 117 | 22.6 121 | 7.59 113 | 9.84 114 | 16.9 105 | 11.1 118 | 16.9 121 | 28.2 121 | 18.8 125 | 14.1 120 | 31.1 119 | 13.1 120 | 3.86 98 | 4.62 90 | 4.53 110 | 5.98 99 | 21.2 112 | 5.97 103 | 1.76 5 | 3.14 5 | 1.46 8 | 6.29 103 | 12.9 110 | 5.81 102 |
GraphCuts [14] | 99.0 | 6.25 102 | 14.3 103 | 5.53 103 | 8.60 108 | 20.1 114 | 6.61 104 | 7.91 94 | 15.4 78 | 10.9 106 | 4.88 91 | 19.0 70 | 3.05 93 | 3.78 92 | 4.71 94 | 3.94 99 | 8.74 118 | 16.4 91 | 5.39 97 | 4.04 104 | 4.87 92 | 4.85 110 | 6.35 104 | 12.2 105 | 6.05 103 |
Filter Flow [19] | 99.0 | 6.48 103 | 14.6 105 | 4.96 100 | 5.73 99 | 15.7 102 | 5.07 99 | 10.1 105 | 18.6 100 | 14.3 115 | 9.04 112 | 23.3 100 | 7.80 112 | 3.98 104 | 4.71 94 | 4.21 107 | 5.86 97 | 15.0 79 | 5.41 98 | 4.98 120 | 6.87 123 | 2.78 63 | 4.82 92 | 8.66 73 | 3.65 75 |
SPSA-learn [13] | 99.2 | 6.84 108 | 16.7 110 | 6.74 109 | 8.47 107 | 19.4 111 | 7.49 107 | 12.5 107 | 23.1 108 | 13.1 113 | 8.40 111 | 25.8 111 | 7.08 110 | 3.87 100 | 4.66 92 | 4.10 103 | 6.32 101 | 18.8 102 | 6.89 112 | 2.56 53 | 3.85 58 | 1.79 20 | 7.29 111 | 12.5 107 | 7.47 109 |
HBpMotionGpu [43] | 100.8 | 6.57 105 | 15.0 107 | 5.17 101 | 8.29 106 | 18.0 108 | 8.29 111 | 14.1 112 | 26.5 115 | 13.2 114 | 6.12 102 | 25.3 108 | 3.94 100 | 3.79 94 | 4.62 90 | 3.97 100 | 4.80 85 | 15.7 84 | 4.11 81 | 4.40 112 | 5.20 103 | 2.87 73 | 6.28 102 | 11.7 100 | 7.31 106 |
GroupFlow [9] | 101.3 | 8.00 111 | 18.6 113 | 8.09 115 | 11.1 117 | 23.7 120 | 10.3 116 | 12.6 108 | 25.6 113 | 12.8 111 | 5.84 101 | 20.3 82 | 4.39 103 | 4.69 117 | 5.81 119 | 3.67 94 | 9.29 119 | 22.4 118 | 10.1 121 | 2.11 30 | 3.99 66 | 2.29 48 | 5.75 98 | 10.0 84 | 7.39 108 |
IAOF [50] | 101.5 | 6.49 104 | 14.6 105 | 6.42 107 | 9.22 111 | 18.5 109 | 7.94 109 | 16.4 120 | 27.4 119 | 13.0 112 | 8.22 108 | 22.2 94 | 7.73 111 | 3.77 91 | 4.76 98 | 3.42 87 | 6.84 107 | 18.8 102 | 4.23 84 | 3.59 95 | 4.46 80 | 2.83 67 | 7.51 113 | 10.1 86 | 10.6 116 |
Black & Anandan [4] | 101.9 | 6.81 107 | 15.4 108 | 7.43 111 | 8.77 109 | 19.5 112 | 7.35 105 | 13.0 109 | 22.9 107 | 12.5 110 | 8.29 109 | 26.1 112 | 6.77 109 | 4.18 108 | 5.28 112 | 3.69 95 | 6.19 100 | 20.0 109 | 5.34 96 | 3.63 96 | 5.05 97 | 1.79 20 | 6.45 105 | 12.2 105 | 5.17 95 |
BlockOverlap [61] | 105.1 | 6.67 106 | 13.1 88 | 5.87 105 | 6.62 102 | 13.9 90 | 6.53 103 | 10.6 106 | 19.5 104 | 10.1 102 | 6.97 106 | 24.9 106 | 5.13 105 | 4.38 111 | 4.61 89 | 6.37 122 | 7.47 112 | 15.7 84 | 6.05 104 | 6.23 123 | 6.41 121 | 13.0 126 | 6.92 108 | 9.60 81 | 12.2 119 |
Nguyen [33] | 105.2 | 7.88 110 | 16.8 111 | 7.02 110 | 13.4 120 | 19.0 110 | 15.3 120 | 17.6 122 | 28.9 122 | 17.2 122 | 12.0 116 | 26.9 113 | 11.6 118 | 4.38 111 | 5.07 110 | 5.58 118 | 5.69 95 | 19.7 108 | 5.93 102 | 2.75 65 | 4.02 68 | 1.91 35 | 6.59 107 | 12.5 107 | 6.52 105 |
UnFlow [129] | 105.9 | 14.6 127 | 25.8 126 | 9.09 119 | 9.40 113 | 16.8 104 | 9.89 115 | 14.2 113 | 26.9 116 | 11.2 107 | 10.0 113 | 25.4 110 | 8.67 114 | 5.43 123 | 5.90 120 | 6.72 123 | 8.64 116 | 24.0 120 | 9.41 119 | 3.51 91 | 4.90 93 | 1.37 6 | 4.37 80 | 12.6 109 | 3.33 65 |
2bit-BM-tele [98] | 107.5 | 8.00 111 | 15.8 109 | 8.40 117 | 4.91 96 | 13.4 86 | 4.67 96 | 8.14 97 | 19.0 103 | 5.12 92 | 6.62 105 | 23.5 102 | 5.04 104 | 4.08 106 | 4.78 99 | 4.61 112 | 8.68 117 | 18.8 102 | 8.31 115 | 6.46 125 | 7.08 125 | 9.47 122 | 7.36 112 | 14.1 114 | 9.62 113 |
Horn & Schunck [3] | 110.9 | 8.01 113 | 19.9 115 | 8.38 116 | 9.13 110 | 23.2 119 | 7.71 108 | 14.2 113 | 25.9 114 | 14.6 117 | 12.4 118 | 30.6 117 | 11.3 117 | 4.64 115 | 5.64 115 | 4.60 111 | 8.21 115 | 24.4 121 | 8.45 116 | 4.01 102 | 5.41 107 | 1.95 37 | 9.16 116 | 17.5 118 | 8.86 112 |
SILK [79] | 112.2 | 9.34 116 | 20.4 116 | 10.5 121 | 10.4 115 | 21.9 116 | 10.3 116 | 16.0 119 | 27.5 120 | 14.5 116 | 10.3 114 | 29.0 116 | 8.54 113 | 4.81 118 | 5.65 116 | 5.56 117 | 9.41 120 | 25.4 123 | 8.74 117 | 2.79 68 | 3.68 48 | 4.62 108 | 10.9 121 | 17.8 120 | 12.3 120 |
Heeger++ [104] | 113.5 | 11.9 122 | 21.8 119 | 8.08 114 | 12.5 118 | 29.7 127 | 9.42 114 | 14.8 116 | 27.1 117 | 9.68 100 | 14.3 121 | 31.0 118 | 12.7 119 | 4.98 120 | 5.74 117 | 4.97 114 | 17.5 127 | 34.1 128 | 18.4 127 | 2.75 65 | 5.44 108 | 2.15 44 | 12.3 123 | 18.8 124 | 14.8 122 |
TI-DOFE [24] | 114.4 | 13.4 125 | 23.2 122 | 16.5 127 | 16.5 123 | 24.1 121 | 18.2 125 | 20.2 127 | 31.1 127 | 20.6 126 | 19.9 126 | 32.9 122 | 20.8 127 | 4.89 119 | 5.90 120 | 5.54 116 | 8.04 114 | 23.9 119 | 8.81 118 | 2.97 75 | 4.34 76 | 1.88 33 | 10.9 121 | 17.7 119 | 11.9 118 |
HCIC-L [99] | 118.1 | 15.7 128 | 22.0 120 | 10.1 120 | 31.5 129 | 26.6 125 | 41.0 129 | 14.8 116 | 23.1 108 | 16.8 121 | 18.4 125 | 34.4 124 | 18.2 126 | 5.94 124 | 6.35 124 | 6.35 121 | 10.6 123 | 19.2 105 | 11.4 123 | 18.7 129 | 17.8 129 | 19.2 128 | 4.93 94 | 8.34 70 | 5.16 94 |
SLK [47] | 118.2 | 11.6 120 | 26.0 127 | 14.6 126 | 15.3 122 | 25.0 123 | 17.5 123 | 17.8 124 | 30.1 125 | 18.1 124 | 25.4 129 | 33.6 123 | 28.0 129 | 5.25 121 | 5.90 120 | 7.03 124 | 10.3 122 | 27.4 125 | 10.6 122 | 2.89 72 | 4.47 81 | 2.94 79 | 14.9 126 | 20.7 126 | 18.8 125 |
FFV1MT [106] | 119.2 | 12.0 123 | 23.3 123 | 8.83 118 | 10.7 116 | 26.6 125 | 8.71 112 | 15.6 118 | 29.0 123 | 12.0 108 | 16.6 124 | 36.3 127 | 15.5 123 | 6.51 127 | 6.40 125 | 10.4 127 | 16.2 126 | 30.7 127 | 17.7 126 | 3.41 89 | 5.44 108 | 3.35 96 | 12.3 123 | 18.8 124 | 14.8 122 |
Adaptive flow [45] | 120.8 | 13.2 124 | 20.8 117 | 14.0 125 | 17.1 125 | 22.0 117 | 17.9 124 | 18.1 125 | 27.1 117 | 22.8 128 | 11.8 115 | 31.1 119 | 10.5 115 | 6.35 126 | 7.13 127 | 6.25 120 | 9.87 121 | 21.8 115 | 9.44 120 | 12.6 128 | 11.4 128 | 20.0 129 | 7.75 114 | 13.6 111 | 7.73 110 |
PGAM+LK [55] | 122.0 | 11.8 121 | 25.6 124 | 13.9 124 | 14.8 121 | 24.4 122 | 16.7 122 | 13.2 110 | 24.0 111 | 15.0 118 | 16.2 123 | 41.2 129 | 15.3 122 | 5.40 122 | 5.45 114 | 8.10 125 | 12.3 125 | 26.5 124 | 12.1 124 | 7.42 126 | 8.24 127 | 7.87 120 | 13.2 125 | 18.3 122 | 19.4 126 |
Periodicity [78] | 122.8 | 11.2 119 | 27.0 128 | 7.46 112 | 16.6 124 | 29.8 129 | 18.2 125 | 25.3 129 | 31.2 129 | 24.9 129 | 12.7 119 | 35.7 126 | 11.1 116 | 31.7 129 | 41.4 129 | 25.1 129 | 23.8 129 | 41.5 129 | 23.8 129 | 2.92 73 | 5.62 112 | 6.90 118 | 18.6 128 | 33.1 129 | 22.3 127 |
FOLKI [16] | 123.6 | 10.5 118 | 25.6 124 | 11.9 123 | 20.9 127 | 26.2 124 | 26.1 127 | 17.6 122 | 31.1 127 | 16.5 120 | 15.4 122 | 32.6 121 | 16.0 124 | 6.16 125 | 6.53 126 | 9.07 126 | 12.2 124 | 29.7 126 | 13.0 125 | 4.67 117 | 5.83 116 | 9.41 121 | 18.2 127 | 22.8 127 | 25.1 128 |
Pyramid LK [2] | 126.1 | 13.9 126 | 20.9 118 | 21.4 129 | 24.1 128 | 23.1 118 | 30.2 128 | 20.9 128 | 29.5 124 | 21.9 127 | 22.2 128 | 34.6 125 | 25.0 128 | 18.7 128 | 23.1 128 | 20.2 128 | 21.2 128 | 24.5 122 | 21.0 128 | 6.41 124 | 7.02 124 | 10.8 124 | 25.6 129 | 31.5 128 | 34.5 129 |
AdaConv-v1 [126] | 130.0 | 39.2 130 | 39.9 130 | 41.8 130 | 73.0 130 | 74.5 130 | 71.1 130 | 70.1 130 | 67.3 130 | 71.8 130 | 64.4 130 | 66.2 130 | 65.9 130 | 76.5 130 | 78.1 130 | 72.0 130 | 68.2 130 | 64.9 130 | 66.5 130 | 52.3 130 | 45.1 130 | 70.9 130 | 81.8 130 | 81.6 130 | 82.3 130 |
SepConv-v1 [127] | 130.0 | 39.2 130 | 39.9 130 | 41.8 130 | 73.0 130 | 74.5 130 | 71.1 130 | 70.1 130 | 67.3 130 | 71.8 130 | 64.4 130 | 66.2 130 | 65.9 130 | 76.5 130 | 78.1 130 | 72.0 130 | 68.2 130 | 64.9 130 | 66.5 130 | 52.3 130 | 45.1 130 | 70.9 130 | 81.8 130 | 81.6 130 | 82.3 130 |
Method | time* | frames | color | Reference and notes | |
[1] 2D-CLG | 844 | 2 | gray | The 2D-CLG method by Bruhn et al. as implemented by Stefan Roth. [A. Bruhn, J. Weickert, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. IJCV 63(3), 2005.] Parameters were set to match the published performance on Yosemite sequence, which may not be optimal for other sequences. | |
[2] Pyramid LK | 12 | 2 | color | A modification of Bouguet's pyramidal implementation of Lucas-Kanade. | |
[3] Horn & Schunck | 49 | 2 | gray | A modern Matlab implementation of the Horn & Schunck method by Deqing Sun. Parameters set to optimize AAE on all training data. | |
[4] Black & Anandan | 328 | 2 | gray | A modern Matlab implementation of the Black & Anandan method by Deqing Sun. | |
[5] Brox et al. | 18 | 2 | color | T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004. (Improved using separate robust functions as proposed in A. Bruhn and J. Weickert, Towards ultimate motion estimation, ICCV 2005; improved by training on the training set.) | |
[6] Fusion | 2,666 | 2 | color | V. Lempitsky, S. Roth, and C. Rother. Discrete-continuous optimization for optical flow estimation. CVPR 2008. | |
[7] Dynamic MRF | 366 | 2 | gray | B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic MRFs. CVPR 2008. (Method improved since publication.) | |
[8] Second-order prior | 14 | 2 | gray | W. Trobin, T. Pock, D. Cremers, and H. Bischof. An unbiased second-order prior for high-accuracy motion estimation. DAGM 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.) | |
[9] GroupFlow | 600 | 2 | gray | X. Ren. Local Grouping for Optical Flow. CVPR 2008. | |
[10] SegOF | 60 | 2 | color | L. Xu, J. Chen, and J. Jia. Segmentation based variational model for accurate optical flow estimation. ECCV 2008. Code available. | |
[11] Learning Flow | 825 | 2 | gray | D. Sun, S. Roth, J.P. Lewis, and M. Black. Learning optical flow (SRF-LFC). ECCV 2008. | |
[12] CBF | 69 | 2 | color | W. Trobin, T. Pock, D. Cremers, and H. Bischof. Continuous energy minimization via repeated binary fusion. ECCV 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.) | |
[13] SPSA-learn | 200 | 2 | color | Y. Li and D. Huttenlocher. Learning for optical flow using stochastic optimization. ECCV 2008. | |
[14] GraphCuts | 1,200 | 2 | color | T. Cooke. Two applications of graph-cuts to image processing. DICTA 2008. | |
[15] F-TV-L1 | 8 | 2 | gray | A. Wedel, T. Pock, J. Braun, U. Franke, and D. Cremers. Duality TV-L1 flow with fundamental matrix prior. IVCNZ 2008. | |
[16] FOLKI | 1.4 | 2 | gray | G. Le Besnerais and F. Champagnat. Dense optical flow by iterative local window registration. ICIP 2005. | |
[17] TV-L1-improved | 2.9 | 2 | gray | A. Wedel, T. Pock, C. Zach, H. Bischof, and D. Cremers. An improved algorithm for TV-L1 optical flow computation. Proceedings of the Dagstuhl Visual Motion Analysis Workshop 2008. Code at GPU4Vision. | |
[18] DPOF | 287 | 2 | color | C. Lei and Y.-H. Yang. Optical flow estimation on coarse-to-fine region-trees using discrete optimization. ICCV 2009. (Method improved since publication.) | |
[19] Filter Flow | 34,000 | 2 | color | S. Seitz and S. Baker. Filter flow. ICCV 2009. | |
[20] Adaptive | 9.2 | 2 | gray | A. Wedel, D. Cremers, T. Pock, and H. Bischof. Structure- and motion-adaptive regularization for high accuracy optic flow. ICCV 2009. | |
[21] Complementary OF | 44 | 2 | color | H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009. | |
[22] Aniso. Huber-L1 | 2 | 2 | gray | M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009. Code at GPU4Vision. | |
[23] Rannacher | 0.12 | 2 | gray | J. Rannacher. Realtime 3D motion estimation on graphics hardware. Bachelor thesis, Heidelberg University, 2009. | |
[24] TI-DOFE | 260 | 2 | gray | C. Cassisa, S. Simoens, and V. Prinet. Two-frame optical flow formulation in an unwarped multiresolution scheme. CIARP 2009. | |
[25] NL-TV-NCC | 20 | 2 | color | M. Werlberger, T. Pock, and H. Bischof. Motion estimation with non-local total variation regularization. CVPR 2010. | |
[26] MDP-Flow | 188 | 2 | color | L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. CVPR 2010. | |
[27] ACK-Prior | 5872 | 2 | color | K. Lee, D. Kwon, I. Yun, and S. Lee. Optical flow estimation with adaptive convolution kernel prior on discrete framework. CVPR 2010. | |
[28] LDOF | 122 | 2 | color | T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI 33(3):500-513, 2011. | |
[29] p-harmonic | 565 | 2 | gray | J. Gai and R. Stevenson. Optical flow estimation with p-harmonic regularization. ICIP 2010. | |
[30] TriangleFlow | 4200 | 2 | gray | B. Glocker, H. Heibel, N. Navab, P. Kohli, and C. Rother. TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. ECCV 2010. | |
[31] Classic+NL | 972 | 2 | color | D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR 2010. Matlab code. | |
[32] Classic++ | 486 | 2 | gray | A modern implementation of the classical formulation descended from Horn & Schunck and Black & Anandan; see D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, CVPR 2010. | |
[33] Nguyen | 33 | 2 | gray | D. Nguyen. Tuning optical flow estimation with image-driven functions. ICRA 2011. | |
[34] Modified CLG | 133 | 2 | gray | R. Fezzani, F. Champagnat, and G. Le Besnerais. Combined local global method for optic flow computation. EUSIPCO 2010. | |
[35] ComplOF-FED-GPU | 0.97 | 2 | color | P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, and J. Weickert. A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. CVGPU Workshop 2010. | |
[36] Ad-TV-NDC | 35 | 2 | gray | M. Nawaz. Motion estimation with adaptive regularization and neighborhood dependent constraint. DICTA 2010. | |
[37] Layers++ | 18206 | 2 | color | D. Sun, E. Sudderth, and M. Black. Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010. | |
[38] OFH | 620 | 3 | color | H. Zimmer, A. Bruhn, J. Weickert. Optic flow in harmony. IJCV 93(3) 2011. | |
[39] LSM | 1615 | 2 | color | K. Jia, X. Wang, and X. Tang. Optical flow estimation using learned sparse model. ICCV 2011. | |
[40] CostFilter | 55 | 2 | color | C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. CVPR 2011. | |
[41] Bartels | 0.15 | 2 | gray | C. Bartels and G. de Haan. Smoothness constraints in recursive search motion estimation for picture rate conversion. IEEE TCSVT 2010. Version improved since publication: mapped on GPU. | |
[42] Shiralkar | 600 | 2 | gray | M. Shiralkar and R. Schalkoff. A self organization-based optical flow estimator with GPU implementation. MVA 23(6):1229-1242. | |
[43] HBpMotionGpu | 1000 | 5 | gray | S. Grauer-Gray and C. Kambhamettu. Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. WACV 2009. (Method improved since publication.) | |
[44] StereoFlow | 7200 | 2 | color | G. Rosman, S. Shem-Tov, D. Bitton, T. Nir, G. Adiv, R. Kimmel, A. Feuer, and A. Bruckstein. Over-parameterized optical flow using a stereoscopic constraint. SSVM 2011:761-772. | |
[45] Adaptive flow | 121 | 2 | gray | T. Arici. Energy minimization based motion estimation using adaptive smoothness priors. Submitted to IEEE TIP 2011. | |
[46] TC-Flow | 2500 | 5 | color | S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer. Modeling temporal coherence for optical flow. ICCV 2011. | |
[47] SLK | 300 | 2 | gray | T. Corpetti and E. Mémin. Stochastic uncertainty models for the luminance consistency assumption. IEEE TIP 2011. | |
[48] CLG-TV | 29 | 2 | gray | M. Drulea. Total variation regularization of local-global optical flow. ITSC 2011. Matlab code. | |
[49] SimpleFlow | 1.7 | 2 | color | M. Tao, J. Bai, P. Kohli, S. Paris. SimpleFlow: a non-iterative, sublinear optical flow algorithm. EUROGRAPHICS 2012. | |
[50] IAOF | 57 | 2 | gray | D. Nguyen. Improving motion estimation using image-driven functions and hybrid scheme. PSIVT 2011. | |
[51] IAOF2 | 56 | 2 | gray | D. Nguyen. Enhancing the sharpness of flow field using image-driven functions with occlusion-aware filter. Submitted to TIP 2011. | |
[52] LocallyOriented | 9541 | 2 | gray | Y.Niu, A. Dick, and M. Brooks. Locally oriented optical flow computation. To appear in TIP 2012. | |
[53] IROF-TV | 261 | 2 | color | H. Rashwan, D. Puig, and M. Garcia. On improving the robustness of differential optical flow. ICCV 2011 Artemis workshop. | |
[54] Sparse Occlusion | 2312 | 2 | color | A. Ayvaci, M. Raptis, and S. Soatto. Sparse occlusion detection with optical flow. Submitted to IJCV 2011. | |
[55] PGAM+LK | 0.37 | 2 | gray | A. Alba, E. Arce-Santana, and M. Rivera. Optical flow estimation with prior models obtained from phase correlation. ISVC 2010. | |
[56] Sparse-NonSparse | 713 | 2 | color | L. Chen, J. Wang, and Y. Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. Submitted to PAMI 2013. | |
[57] nLayers | 36150 | 4 | color | D. Sun, E. Sudderth, and M. Black. Layered segmentation and optical flow estimation over time. CVPR 2012. | |
[58] IROF++ | 187 | 2 | color | H. Rashwan, D. Puig, and M. Garcia. Variational optical flow estimation based on stick tensor voting. IEEE TIP 2013. | |
[59] COFM | 600 | 3 | color | M. Mozerov. Constrained optical flow estimation as a matching problem. IEEE TIP 2013. | |
[60] Efficient-NL | 400 | 2 | color | P. Krähenbühl and V. Koltun. Efficient nonlocal regularization for optical flow. ECCV 2012. | |
[61] BlockOverlap | 2 | 2 | gray | M. Santoro, G. AlRegib, and Y. Altunbasak. Motion estimation using block overlap minimization. Submitted to MMSP 2012. | |
[62] Ramp | 1200 | 2 | color | A. Singh and N. Ahuja. Exploiting ramp structures for improving optical flow estimation. ICPR 2012. | |
[63] Occlusion-TV-L1 | 538 | 3 | gray | C. Ballester, L. Garrido, V. Lazcano, and V. Caselles. A TV-L1 optical flow method with occlusion detection. DAGM-OAGM 2012. | |
[64] TV-L1-MCT | 90 | 2 | color | M. Mohamed and B. Mertsching. TV-L1 optical flow estimation with image details recovering based on modified census transform. ISVC 2012. | |
[65] Local-TV-L1 | 500 | 2 | gray | L. Raket. Local smoothness for global optical flow. ICIP 2012. | |
[66] ALD-Flow | 61 | 2 | color | M. Stoll, A. Bruhn, and S. Volz. Adaptive integration of feature matches into variational optic flow methods. ACCV 2012. | |
[67] SIOF | 234 | 2 | color | L. Xu, Z. Dai, and J. Jia. Scale invariant optical flow. ECCV 2012. | |
[68] MDP-Flow2 | 342 | 2 | color | L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012. Code available. | |
[69] TCOF | 1421 | all | gray | J. Sanchez, A. Salgado, and N. Monzon. Optical flow estimation with consistent spatio-temporal coherence models. VISAPP 2013. | |
[70] LME | 476 | 2 | color | W. Li, D. Cosker, M. Brown, and R. Tang. Optical flow estimation using Laplacian mesh energy. CVPR 2013. | |
[71] NN-field | 362 | 2 | color | L. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013. | |
[72] FESL | 3310 | 2 | color | W. Dong, G. Shi, X. Hu, and Y. Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. Submitted to IEEE TIP 2013. | |
[73] PMF | 35 | 2 | color | J. Lu, H. Yang, D. Min, and M. Do. PatchMatch filter: efficient edge-aware filtering meets randomized search for fast correspondence field estimation. CVPR 2013. | |
[74] FC-2Layers-FF | 2662 | 4 | color | D. Sun, J. Wulff, E. Sudderth, H. Pfister, and M. Black. A fully-connected layered model of foreground and background flow. CVPR 2013. | |
[75] Correlation Flow | 290 | 2 | color | M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code. | |
[76] TC/T-Flow | 341 | 5 | color | M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013. | |
[77] OFLAF | 1530 | 2 | color | T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013. | |
[78] Periodicity | 8000 | 4 | color | G. Khachaturov, S. Gonzalez-Brambila, and J. Gonzalez-Trejo. Periodicity-based computation of optical flow. Submitted to Computacion y Sistemas (CyS) 2013. | |
[79] SILK | 572 | 2 | gray | P. Zille, C. Xu, T. Corpetti, L. Shao. Observation models based on scale interactions for optical flow estimation. Submitted to IEEE TIP. | |
[80] CRTflow | 13 | 3 | color | O. Demetz, D. Hafner, and J. Weickert. The complete rank transform: a tool for accurate and morphologically invariant matching of structures. BMVC 2013. | |
[81] SuperFlow | 178 | 2 | color | Anonymous. Superpixel based optical flow estimation. ICCV 2013 submission 507. | |
[82] Aniso-Texture | 300 | 2 | color | Anonymous. Texture information-based optical flow estimation using an incremental multi-resolution approach. ITC-CSCC 2013 submission 267. | |
[83] Classic+CPF | 640 | 2 | gray | Z. Tu, R. Veltkamp, and N. van der Aa. A combined post-filtering method to improve accuracy of variational optical flow estimation. Submitted to Pattern Recognition 2013. | |
[84] S2D-Matching | 1200 | 2 | color | Anonymous. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013 submission 1479. | |
[85] AGIF+OF | 438 | 2 | gray | Z. Tu, R. Poppe, and R. Veltkamp. Adaptive guided image filter to warped interpolation image for variational optical flow computation. Submitted to Signal Processing 2015. | |
[86] DeepFlow | 13 | 2 | color | P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013. | |
[87] NNF-Local | 673 | 2 | color | Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow with nearest neighbor field. Submitted to PAMI 2014. | |
[88] EPPM w/o HM | 2.5 | 2 | color | L. Bao, Q. Yang, and H. Jin. Fast edge-preserving PatchMatch for large displacement optical flow. CVPR 2014. | |
[89] MLDP_OF | 165 | 2 | gray | M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia, and D. Puig. Illumination-robust optical flow approach using local directional pattern. IEEE TCSVT 24(9):1499-1508, 2014. | |
[90] RFlow | 20 | 2 | gray | S. Ali, C. Daul, and W. Blondel. Robust and accurate optical flow estimation for weak texture and varying illumination condition: Application to cystoscopy. IPTA 2014. | |
[91] SRR-TVOF-NL | 32 | all | color | P. Pohl, M. Sirotenko, E. Tolstaya, and V. Bucha. Edge preserving motion estimation with occlusions correction for assisted 2D to 3D conversion. IS&T/SPIE Electronic Imaging 2014. | |
[92] 2DHMM-SAS | 157 | 2 | color | M.-C. Shih, R. Shenoy, and K. Rose. A two-dimensional hidden Markov model with spatially-adaptive states with application of optical flow. ICIP 2014 submission. | |
[93] WLIF-Flow | 700 | 2 | color | Z. Tu, R. Veltkamp, N. van der Aa, and C. Van Gemeren. Weighted local intensity fusion method for variational optical flow estimation. Submitted to TIP 2014. | |
[94] FMOF | 215 | 2 | color | N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014. | |
[95] TriFlow | 150 | 2 | color | TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914. | |
[96] ComponentFusion | 6.5 | 2 | color | Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941. | |
[97] AggregFlow | 1642 | 2 | color | D. Fortun, P. Bouthemy, and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. Preprint arXiv:1407.5759. | |
[98] 2bit-BM-tele | 124 | 2 | gray | R. Xu and D. Taubman. Robust dense block-based motion estimation using a two-bit transform on a Laplacian pyramid. ICIP 2013. | |
[99] HCIC-L | 330 | 2 | color | Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114. | |
[100] TF+OM | 600 | 2 | color | R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015. | |
[101] PH-Flow | 800 | 2 | color | J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015. | |
[102] EpicFlow | 16 | 2 | color | J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015. | |
[103] NNF-EAC | 380 | 2 | color | Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336. | |
[104] Heeger++ | 6600 | 5 | gray | Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238. | |
[105] HBM-GC | 330 | 2 | color | A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015. | |
[106] FFV1MT | 358 | 5 | gray | F. Solari, M. Chessa, N. Medathati, and P. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation? Submitted to Signal Processing: Image Communication 2015. | |
[107] ROF-ND | 4 | 2 | color | S. Ali, C. Daul, E. Galbrun, and W. Blondel. Illumination invariant large displacement optical flow using robust neighbourhood descriptors. Submitted to CVIU 2015. | |
[108] DeepFlow2 | 16 | 2 | color | J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015. | |
[109] HAST | 2667 | 2 | color | Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221. | |
[110] FlowFields | 15 | 2 | color | C. Bailer, B. Taetz, and D. Stricker. Flow Fields: Dense unregularized correspondence fields for highly accurate large displacement optical flow estimation. ICCV 2015. | |
[111] SVFilterOh | 1.56 | 2 | color | Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788. | |
[112] FlowNetS+ft+v | 0.5 | 2 | color | Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235. | |
[113] CombBMOF | 51 | 2 | color | M. Brüggemann, R. Kays, P. Springer, and O. Erdler. Combined block-matching and adaptive differential motion estimation in a hierarchical multi-scale framework. ICGIP 2014. (Method improved since publication.) | |
[114] PMMST | 182 | 2 | color | F. Zhang, S. Xu, and X. Zhang. High accuracy correspondence field estimation via MST based patch matching. Submitted to TIP 2015. | |
[115] DF-Auto | 70 | 2 | color | N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015. | |
[116] CPM-Flow | 3 | 2 | color | Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241. | |
[117] CNN-flow-warp+ref | 1.4 | 3 | color | D. Teney and M. Hebert. Learning to extract motion from videos in convolutional neural networks. ArXiv 1601.07532, 2016. | |
[118] Steered-L1 | 804 | 2 | color | Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016. | |
[119] StereoOF-V1MT | 343 | 2 | gray | Anonymous. Visual features for action-oriented tasks: a cortical-like model for disparity and optic flow computation. BMVC 2016 submission 132. | |
[120] PGM-C | 5 | 2 | color | Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016. | |
[121] RNLOD-Flow | 1040 | 2 | gray | C. Zhang, Z. Chen, M. Wang, M. Li, and S. Jiang. Robust non-local TV-L1 optical flow estimation with occlusion detection. Submitted to TIP 2016. | |
[122] FlowNet2 | 0.091 | 2 | color | Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900. | |
[123] S2F-IF | 20 | 2 | color | Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765. | |
[124] BriefMatch | 0.068 | 2 | gray | G. Eilertsen, P.-E. Forssen, and J. Unger. Dense binary feature matching for real-time optical flow estimation. SCIA 2017 submission 62. | |
[125] OAR-Flow | 60 | 2 | color | Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20. | |
[126] AdaConv-v1 | 2.8 | 2 | color | S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017. | |
[127] SepConv-v1 | 0.2 | 2 | color | S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017. | |
[128] ProbFlowFields | 37 | 2 | color | A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017. | |
[129] UnFlow | 0.12 | 2 | color | Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018. | |
[130] FlowFields+ | 10.5 | 2 | color | C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. Submitted to PAMI 2017. | |
[131] Kuang | 9.9 | 2 | gray | F. Kuang. PatchMatch algorithms for motion estimation and stereo reconstruction. Master thesis, University of Stuttgart, 2017. |