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] | 5.2 | 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 3 | 9.14 7 | 1.06 4 | 2.28 2 | 2.94 1 | 1.57 2 | 2.39 5 | 6.78 2 | 2.15 9 | 2.00 21 | 3.36 17 | 1.62 15 | 0.99 1 | 2.16 3 | 0.57 2 |
NN-field [71] | 10.1 | 2.89 8 | 8.13 16 | 2.11 5 | 2.10 6 | 7.15 9 | 1.77 15 | 2.27 4 | 5.59 5 | 1.61 8 | 1.58 1 | 8.52 6 | 0.79 1 | 2.35 4 | 3.05 5 | 1.60 3 | 1.89 1 | 5.20 1 | 1.37 1 | 2.43 49 | 3.70 50 | 1.95 38 | 1.01 2 | 2.25 4 | 0.53 1 |
OFLAF [77] | 13.1 | 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 19 | 13.7 25 | 1.37 21 | 2.35 4 | 3.13 6 | 1.62 4 | 2.98 22 | 7.73 7 | 2.57 21 | 2.08 27 | 3.27 12 | 2.05 42 | 1.33 13 | 2.43 7 | 1.40 16 |
PMMST [114] | 14.3 | 3.42 42 | 7.60 5 | 2.65 30 | 2.32 11 | 6.39 1 | 2.20 33 | 2.63 11 | 6.08 8 | 2.03 27 | 2.06 6 | 6.07 2 | 1.44 29 | 2.60 10 | 3.27 8 | 1.91 10 | 2.56 7 | 6.78 2 | 2.09 5 | 2.06 23 | 3.53 36 | 1.63 16 | 1.27 10 | 2.29 5 | 1.02 7 |
nLayers [57] | 16.7 | 2.80 6 | 7.42 3 | 2.20 8 | 2.71 30 | 7.24 10 | 2.55 60 | 2.61 9 | 6.24 9 | 2.45 52 | 2.30 12 | 12.7 13 | 1.16 7 | 2.30 3 | 3.02 3 | 1.70 5 | 2.62 10 | 6.95 4 | 2.09 5 | 2.29 43 | 3.46 26 | 1.89 35 | 1.38 15 | 3.06 18 | 1.29 14 |
MDP-Flow2 [68] | 19.1 | 3.23 31 | 7.93 13 | 2.60 22 | 1.92 2 | 6.64 2 | 1.52 1 | 2.46 7 | 5.91 7 | 1.56 5 | 3.05 44 | 15.8 54 | 1.51 39 | 2.77 24 | 3.50 18 | 2.16 27 | 2.86 18 | 8.58 18 | 2.70 32 | 2.00 21 | 3.50 33 | 1.59 13 | 1.28 11 | 2.67 12 | 0.89 4 |
ComponentFusion [96] | 19.4 | 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 11 | 13.1 15 | 1.01 3 | 2.71 21 | 3.56 20 | 2.10 22 | 3.55 51 | 12.4 58 | 3.22 58 | 2.19 38 | 3.60 43 | 1.54 12 | 1.32 12 | 2.91 14 | 1.13 9 |
TC/T-Flow [76] | 21.7 | 2.69 3 | 7.75 9 | 1.87 2 | 2.76 33 | 10.2 47 | 1.73 10 | 3.33 25 | 9.01 31 | 1.49 2 | 2.86 35 | 16.7 64 | 1.21 9 | 2.60 10 | 3.49 17 | 1.90 9 | 2.21 2 | 7.65 5 | 2.04 4 | 1.84 10 | 3.23 9 | 3.14 89 | 2.03 38 | 4.53 38 | 1.49 20 |
FC-2Layers-FF [74] | 25.1 | 3.02 14 | 7.87 12 | 2.61 23 | 2.72 31 | 9.35 36 | 2.29 41 | 2.36 5 | 5.47 4 | 2.15 34 | 2.48 13 | 12.6 12 | 1.28 12 | 2.49 7 | 3.19 7 | 2.03 16 | 3.39 40 | 8.92 20 | 2.83 42 | 2.83 72 | 3.92 64 | 2.80 68 | 1.25 8 | 2.57 11 | 1.20 11 |
WLIF-Flow [93] | 26.4 | 2.96 10 | 7.67 6 | 2.40 13 | 2.41 16 | 7.70 15 | 2.10 27 | 2.98 17 | 7.63 19 | 1.97 26 | 2.71 27 | 13.5 21 | 1.33 15 | 3.01 43 | 4.00 49 | 2.40 45 | 3.03 25 | 8.32 12 | 2.44 16 | 2.09 29 | 3.36 17 | 2.04 41 | 2.26 46 | 4.97 46 | 2.59 52 |
Layers++ [37] | 26.8 | 3.11 18 | 8.22 20 | 2.79 42 | 2.43 19 | 7.02 5 | 2.24 36 | 2.43 6 | 5.77 6 | 2.18 37 | 2.13 8 | 9.71 9 | 1.15 6 | 2.35 4 | 3.02 3 | 1.96 11 | 3.81 59 | 11.4 44 | 3.22 58 | 2.74 67 | 4.01 69 | 2.35 53 | 1.45 16 | 3.05 17 | 1.79 30 |
HAST [109] | 27.2 | 2.58 1 | 7.12 1 | 1.81 1 | 2.41 16 | 7.05 7 | 2.10 27 | 1.83 1 | 4.19 1 | 1.17 1 | 2.84 34 | 15.5 49 | 1.08 5 | 2.23 1 | 2.97 2 | 1.40 1 | 3.72 56 | 10.0 33 | 3.92 81 | 3.40 94 | 4.90 99 | 5.66 121 | 1.20 7 | 2.09 1 | 1.24 12 |
FESL [72] | 28.5 | 2.96 10 | 7.70 7 | 2.54 18 | 3.26 68 | 10.4 49 | 2.56 61 | 3.25 23 | 8.39 23 | 2.17 35 | 2.56 15 | 13.2 16 | 1.31 14 | 2.57 9 | 3.40 11 | 2.12 25 | 2.60 9 | 7.65 5 | 2.30 10 | 2.64 63 | 4.22 78 | 2.47 56 | 1.75 27 | 3.49 26 | 1.71 25 |
AGIF+OF [85] | 28.6 | 3.06 16 | 8.20 17 | 2.55 20 | 3.17 58 | 10.6 52 | 2.46 54 | 3.46 30 | 8.97 30 | 2.24 40 | 2.61 17 | 13.7 25 | 1.33 15 | 2.63 15 | 3.46 15 | 2.11 23 | 2.88 20 | 8.34 14 | 2.35 12 | 2.10 31 | 3.56 39 | 2.09 44 | 1.80 29 | 3.68 29 | 2.24 41 |
Efficient-NL [60] | 28.7 | 2.99 13 | 8.23 21 | 2.28 9 | 2.72 31 | 8.95 32 | 2.25 39 | 3.81 40 | 9.87 37 | 2.07 31 | 2.77 31 | 14.3 33 | 1.46 34 | 2.61 12 | 3.48 16 | 1.96 11 | 3.31 36 | 8.33 13 | 2.59 23 | 2.60 58 | 3.75 51 | 2.54 59 | 1.60 22 | 3.02 15 | 1.66 22 |
LME [70] | 28.9 | 3.15 23 | 8.04 15 | 2.31 11 | 1.95 3 | 6.65 3 | 1.59 4 | 4.03 46 | 9.31 32 | 4.57 93 | 2.69 25 | 13.6 23 | 1.42 26 | 2.85 31 | 3.61 23 | 2.42 47 | 3.47 47 | 12.8 63 | 3.17 54 | 2.12 33 | 3.53 36 | 1.73 18 | 1.34 14 | 2.75 13 | 1.18 10 |
ALD-Flow [66] | 29.0 | 2.82 7 | 7.86 11 | 2.16 6 | 2.84 40 | 10.1 44 | 1.86 17 | 3.73 38 | 10.4 41 | 1.67 12 | 3.10 46 | 16.8 65 | 1.28 12 | 2.69 20 | 3.60 22 | 1.85 8 | 2.79 14 | 11.3 43 | 2.32 11 | 2.07 25 | 3.25 11 | 3.10 86 | 2.03 38 | 5.11 47 | 1.94 33 |
RNLOD-Flow [121] | 30.0 | 2.66 2 | 7.33 2 | 2.17 7 | 2.53 26 | 9.46 38 | 1.86 17 | 3.94 44 | 10.7 47 | 1.95 24 | 2.50 14 | 13.5 21 | 1.21 9 | 2.68 18 | 3.62 25 | 2.05 18 | 2.99 23 | 8.59 19 | 2.75 36 | 3.00 81 | 4.54 88 | 3.25 94 | 1.48 18 | 3.24 21 | 1.76 29 |
IROF++ [58] | 30.6 | 3.17 25 | 8.69 30 | 2.61 23 | 2.79 35 | 9.61 39 | 2.33 42 | 3.43 27 | 8.86 27 | 2.38 46 | 2.87 36 | 14.8 38 | 1.52 41 | 2.74 22 | 3.57 21 | 2.19 29 | 3.20 32 | 9.70 30 | 2.71 33 | 1.96 19 | 3.45 25 | 1.22 6 | 1.80 29 | 4.06 31 | 2.50 48 |
NNF-EAC [103] | 30.7 | 3.31 34 | 8.21 19 | 2.68 32 | 2.19 9 | 7.49 13 | 1.76 13 | 2.73 13 | 6.62 12 | 1.70 14 | 3.18 51 | 15.8 54 | 1.64 50 | 2.87 34 | 3.66 28 | 2.24 31 | 3.02 24 | 8.07 10 | 2.59 23 | 2.19 38 | 3.48 29 | 1.74 19 | 2.85 60 | 6.52 62 | 3.12 64 |
PH-Flow [101] | 31.0 | 3.19 28 | 8.87 35 | 2.71 33 | 2.84 40 | 9.33 35 | 2.37 44 | 2.85 14 | 7.20 15 | 2.36 43 | 2.92 39 | 15.4 46 | 1.51 39 | 2.63 15 | 3.42 12 | 2.04 17 | 3.03 25 | 8.52 17 | 2.49 18 | 2.69 65 | 3.60 43 | 3.13 88 | 1.25 8 | 2.53 9 | 1.34 15 |
Classic+CPF [83] | 31.6 | 3.14 21 | 8.60 27 | 2.63 27 | 3.03 56 | 10.6 52 | 2.33 42 | 3.66 34 | 9.58 33 | 2.20 38 | 2.61 17 | 14.1 30 | 1.34 18 | 2.68 18 | 3.53 19 | 2.21 30 | 2.85 17 | 7.95 9 | 2.38 13 | 2.44 51 | 3.49 31 | 2.90 79 | 1.67 25 | 3.40 24 | 2.43 47 |
Sparse-NonSparse [56] | 33.4 | 3.14 21 | 8.75 32 | 2.76 40 | 3.02 54 | 10.6 52 | 2.43 49 | 3.45 29 | 8.96 28 | 2.36 43 | 2.66 22 | 13.7 25 | 1.42 26 | 2.85 31 | 3.75 35 | 2.33 36 | 3.28 35 | 9.40 26 | 2.73 34 | 2.42 48 | 3.31 14 | 2.69 63 | 1.47 17 | 3.07 19 | 1.66 22 |
TC-Flow [46] | 34.4 | 2.91 9 | 8.00 14 | 2.34 12 | 2.18 8 | 8.77 27 | 1.52 1 | 3.84 42 | 10.7 47 | 1.49 2 | 3.13 47 | 16.6 63 | 1.46 34 | 2.78 25 | 3.73 34 | 1.96 11 | 3.08 28 | 11.4 44 | 2.66 27 | 1.94 17 | 3.43 22 | 3.20 93 | 3.06 65 | 7.04 64 | 4.08 89 |
3DFlow [135] | 34.9 | 3.44 43 | 8.63 29 | 2.46 15 | 2.43 19 | 8.59 26 | 1.75 12 | 3.71 36 | 9.93 39 | 1.64 10 | 1.61 2 | 4.58 1 | 1.23 11 | 2.86 33 | 3.72 32 | 2.16 27 | 4.52 81 | 11.6 50 | 4.20 88 | 3.16 89 | 4.02 70 | 4.44 113 | 1.13 5 | 2.14 2 | 0.89 4 |
LSM [39] | 35.6 | 3.12 19 | 8.62 28 | 2.75 39 | 3.00 52 | 10.5 51 | 2.44 51 | 3.43 27 | 8.85 26 | 2.35 42 | 2.66 22 | 13.6 23 | 1.44 29 | 2.82 27 | 3.68 29 | 2.36 38 | 3.38 39 | 9.41 27 | 2.81 40 | 2.69 65 | 3.52 34 | 2.84 72 | 1.59 21 | 3.38 23 | 1.80 31 |
SVFilterOh [111] | 36.3 | 3.63 49 | 8.82 33 | 2.86 44 | 2.60 28 | 8.06 18 | 2.05 26 | 2.95 15 | 7.09 14 | 2.03 27 | 2.80 33 | 13.8 28 | 1.41 25 | 2.63 15 | 3.42 12 | 1.75 7 | 3.49 48 | 10.3 35 | 3.23 61 | 3.63 102 | 5.75 120 | 4.47 114 | 1.09 4 | 2.45 8 | 0.92 6 |
Correlation Flow [75] | 36.4 | 3.38 40 | 8.40 23 | 2.64 28 | 2.23 10 | 7.54 14 | 1.56 3 | 5.14 67 | 13.1 66 | 1.60 7 | 2.09 7 | 8.15 5 | 1.35 20 | 3.12 51 | 4.09 56 | 2.34 37 | 4.01 70 | 11.5 48 | 4.00 83 | 2.59 57 | 3.61 45 | 3.00 84 | 1.49 19 | 3.04 16 | 1.42 18 |
Ramp [62] | 36.6 | 3.18 27 | 8.83 34 | 2.73 36 | 2.89 45 | 10.1 44 | 2.44 51 | 3.27 24 | 8.43 24 | 2.38 46 | 2.74 29 | 14.2 31 | 1.46 34 | 2.82 27 | 3.69 31 | 2.29 34 | 3.37 38 | 9.31 24 | 2.93 46 | 2.62 61 | 3.38 20 | 3.19 92 | 1.54 20 | 3.21 20 | 2.24 41 |
PMF [73] | 37.0 | 3.61 47 | 9.07 38 | 2.62 25 | 2.40 14 | 8.05 17 | 1.83 16 | 2.61 9 | 6.27 10 | 1.65 11 | 3.35 60 | 15.4 46 | 1.58 45 | 2.54 8 | 3.27 8 | 1.71 6 | 3.59 52 | 11.1 41 | 3.46 67 | 4.07 112 | 6.18 126 | 4.02 109 | 1.06 3 | 2.38 6 | 1.25 13 |
ProbFlowFields [128] | 37.4 | 4.18 68 | 12.4 83 | 3.40 74 | 2.43 19 | 8.16 20 | 2.19 32 | 3.65 33 | 9.72 35 | 2.86 67 | 2.22 9 | 9.42 8 | 1.42 26 | 3.01 43 | 3.96 46 | 2.36 38 | 2.73 13 | 10.9 36 | 2.51 19 | 1.89 16 | 3.39 21 | 1.82 24 | 2.59 53 | 6.21 60 | 2.75 55 |
COFM [59] | 37.6 | 3.17 25 | 9.90 54 | 2.46 15 | 2.41 16 | 8.34 23 | 1.92 20 | 3.77 39 | 10.5 42 | 2.54 55 | 2.71 27 | 14.9 40 | 1.19 8 | 3.08 48 | 3.92 44 | 3.25 87 | 3.83 62 | 10.9 36 | 3.15 53 | 2.20 41 | 3.35 15 | 2.91 81 | 1.62 24 | 2.56 10 | 2.09 37 |
FMOF [94] | 38.5 | 3.12 19 | 8.23 21 | 2.73 36 | 3.25 65 | 10.7 59 | 2.52 58 | 3.01 18 | 7.61 18 | 2.20 38 | 2.56 15 | 13.4 19 | 1.33 15 | 2.75 23 | 3.61 23 | 2.24 31 | 3.66 54 | 8.50 16 | 2.78 38 | 2.62 61 | 3.84 59 | 3.27 96 | 2.66 57 | 5.69 51 | 1.95 35 |
JOF [141] | 38.7 | 3.08 17 | 8.56 26 | 2.51 17 | 3.27 69 | 10.2 47 | 2.81 78 | 3.02 19 | 7.55 17 | 2.42 50 | 2.64 20 | 14.2 31 | 1.34 18 | 2.62 13 | 3.42 12 | 2.08 19 | 3.26 33 | 8.96 21 | 2.56 20 | 3.12 88 | 4.26 79 | 4.09 111 | 2.11 44 | 4.58 40 | 2.18 39 |
OAR-Flow [125] | 39.5 | 3.37 38 | 9.87 53 | 2.67 31 | 4.22 88 | 12.8 82 | 2.87 80 | 4.95 63 | 13.4 69 | 2.66 59 | 3.23 53 | 16.4 62 | 1.37 21 | 2.83 29 | 3.82 38 | 1.97 14 | 2.49 6 | 10.9 36 | 1.87 3 | 1.52 2 | 2.82 1 | 1.86 29 | 1.85 32 | 4.35 36 | 1.68 24 |
Classic+NL [31] | 40.0 | 3.20 30 | 8.72 31 | 2.81 43 | 3.02 54 | 10.6 52 | 2.44 51 | 3.46 30 | 8.84 25 | 2.38 46 | 2.78 32 | 14.3 33 | 1.46 34 | 2.83 29 | 3.68 29 | 2.31 35 | 3.40 41 | 9.09 23 | 2.76 37 | 2.87 74 | 3.82 58 | 2.86 76 | 1.67 25 | 3.53 27 | 2.26 44 |
TV-L1-MCT [64] | 41.1 | 3.16 24 | 8.48 25 | 2.71 33 | 3.28 70 | 10.8 63 | 2.60 68 | 3.95 45 | 10.5 42 | 2.38 46 | 2.69 25 | 13.9 29 | 1.45 33 | 2.94 39 | 3.79 36 | 2.63 66 | 3.50 49 | 9.75 31 | 3.06 50 | 2.08 27 | 3.35 15 | 2.29 51 | 1.95 35 | 3.89 30 | 2.71 54 |
IIOF-NLDP [131] | 44.3 | 3.65 50 | 9.81 52 | 2.56 21 | 2.79 35 | 9.36 37 | 2.00 22 | 4.28 52 | 11.3 53 | 1.69 13 | 2.02 5 | 7.52 4 | 1.38 24 | 3.36 72 | 4.52 86 | 2.40 45 | 3.82 60 | 11.2 42 | 3.67 75 | 2.07 25 | 3.79 55 | 1.88 33 | 2.91 62 | 5.30 50 | 4.17 90 |
SimpleFlow [49] | 45.0 | 3.35 35 | 9.20 41 | 2.98 51 | 3.18 61 | 10.7 59 | 2.71 72 | 5.06 65 | 12.6 64 | 2.70 61 | 2.95 41 | 15.1 42 | 1.58 45 | 2.91 37 | 3.79 36 | 2.47 49 | 3.59 52 | 9.49 28 | 2.99 48 | 2.39 46 | 3.46 26 | 2.24 50 | 1.60 22 | 3.56 28 | 1.57 21 |
CostFilter [40] | 45.0 | 3.84 53 | 9.64 48 | 3.06 53 | 2.55 27 | 8.09 19 | 2.03 24 | 2.69 12 | 6.47 11 | 1.88 20 | 3.66 70 | 16.8 65 | 1.88 62 | 2.62 13 | 3.34 10 | 1.99 15 | 4.05 71 | 11.0 40 | 3.65 74 | 4.16 114 | 7.18 133 | 4.66 116 | 1.16 6 | 3.36 22 | 0.87 3 |
2DHMM-SAS [92] | 47.1 | 3.19 28 | 8.89 36 | 2.71 33 | 3.20 63 | 11.5 70 | 2.38 45 | 5.19 68 | 12.2 60 | 2.73 63 | 2.92 39 | 15.2 43 | 1.53 42 | 2.79 26 | 3.65 27 | 2.27 33 | 3.45 45 | 9.34 25 | 2.78 38 | 2.66 64 | 3.56 39 | 3.07 85 | 2.34 49 | 5.12 48 | 2.97 62 |
S2D-Matching [84] | 48.2 | 3.36 36 | 9.66 49 | 2.86 44 | 3.19 62 | 11.1 66 | 2.46 54 | 4.86 62 | 12.9 65 | 2.47 53 | 2.67 24 | 13.2 16 | 1.44 29 | 2.87 34 | 3.72 32 | 2.38 41 | 3.45 45 | 9.76 32 | 2.95 47 | 3.05 82 | 3.79 55 | 3.30 98 | 1.95 35 | 4.16 34 | 3.00 63 |
MLDP_OF [89] | 48.5 | 4.13 64 | 10.3 61 | 3.60 82 | 2.34 12 | 7.70 15 | 1.88 19 | 4.23 51 | 10.9 50 | 1.87 19 | 2.74 29 | 14.6 37 | 1.37 21 | 3.10 49 | 3.91 43 | 2.48 53 | 3.40 41 | 9.00 22 | 3.79 78 | 3.46 96 | 4.20 76 | 5.55 120 | 2.31 47 | 4.64 42 | 1.98 36 |
MDP-Flow [26] | 48.6 | 3.48 45 | 9.46 45 | 3.10 55 | 2.45 22 | 7.36 12 | 2.41 46 | 3.21 22 | 8.31 22 | 2.78 65 | 3.18 51 | 17.8 70 | 1.70 55 | 3.03 45 | 3.87 39 | 2.60 62 | 3.43 43 | 12.6 61 | 2.81 40 | 2.19 38 | 3.88 62 | 1.60 14 | 4.13 81 | 9.96 87 | 3.86 84 |
FlowFields+ [130] | 48.6 | 4.57 86 | 13.7 96 | 3.35 66 | 2.94 50 | 10.1 44 | 2.58 65 | 4.05 47 | 10.6 44 | 3.26 76 | 2.90 38 | 13.2 16 | 1.81 60 | 3.18 55 | 4.20 63 | 2.54 55 | 2.68 12 | 11.4 44 | 2.40 15 | 1.84 10 | 3.62 46 | 1.77 20 | 2.48 50 | 5.86 53 | 2.77 56 |
AggregFlow [97] | 49.0 | 4.25 74 | 11.9 80 | 3.26 58 | 4.46 93 | 13.7 92 | 3.43 90 | 4.76 60 | 12.4 61 | 3.93 90 | 3.28 56 | 15.6 50 | 1.68 52 | 2.89 36 | 3.89 41 | 2.08 19 | 2.32 3 | 7.75 8 | 2.14 7 | 2.06 23 | 3.77 53 | 1.48 11 | 2.07 42 | 4.11 32 | 2.36 45 |
IROF-TV [53] | 49.7 | 3.40 41 | 9.29 43 | 2.95 50 | 2.99 51 | 11.1 66 | 2.53 59 | 3.81 40 | 9.81 36 | 2.44 51 | 3.25 55 | 16.9 67 | 1.78 59 | 3.27 69 | 4.10 57 | 2.93 79 | 4.47 78 | 16.0 90 | 3.53 69 | 1.70 4 | 3.21 7 | 1.12 3 | 1.91 34 | 4.75 44 | 2.19 40 |
CombBMOF [113] | 50.1 | 3.94 57 | 10.6 65 | 2.74 38 | 2.80 37 | 8.55 25 | 2.16 30 | 3.10 21 | 7.99 21 | 1.76 15 | 2.99 42 | 13.4 19 | 1.95 66 | 3.04 46 | 3.89 41 | 2.49 54 | 5.64 100 | 12.3 56 | 6.74 114 | 3.54 98 | 5.16 107 | 2.81 69 | 1.85 32 | 4.60 41 | 1.10 8 |
S2F-IF [123] | 51.1 | 4.51 84 | 13.6 95 | 3.31 62 | 2.90 46 | 10.4 49 | 2.48 57 | 4.07 49 | 10.8 49 | 3.15 72 | 3.31 57 | 15.7 53 | 1.90 63 | 3.17 53 | 4.19 61 | 2.55 58 | 2.81 16 | 11.6 50 | 2.60 25 | 1.86 13 | 3.67 48 | 1.87 30 | 2.11 44 | 4.64 42 | 2.54 51 |
FlowFields [110] | 53.8 | 4.57 86 | 13.7 96 | 3.38 69 | 3.01 53 | 10.6 52 | 2.59 66 | 4.19 50 | 11.1 51 | 3.30 77 | 3.17 50 | 15.0 41 | 1.96 67 | 3.21 62 | 4.24 70 | 2.61 65 | 2.91 21 | 12.4 58 | 2.66 27 | 1.84 10 | 3.46 26 | 1.84 27 | 2.50 51 | 6.15 58 | 2.79 57 |
NL-TV-NCC [25] | 54.3 | 3.89 55 | 9.16 40 | 2.98 51 | 2.87 44 | 9.69 40 | 1.99 21 | 4.44 56 | 11.6 55 | 1.76 15 | 2.64 20 | 11.8 11 | 1.48 38 | 3.49 84 | 4.60 91 | 2.47 49 | 4.67 86 | 13.5 68 | 4.26 91 | 2.83 72 | 4.57 90 | 2.84 72 | 2.62 55 | 6.00 57 | 2.25 43 |
Sparse Occlusion [54] | 54.4 | 3.62 48 | 9.12 39 | 2.90 46 | 2.92 48 | 9.08 33 | 2.56 61 | 4.49 57 | 11.8 58 | 2.11 33 | 3.14 48 | 15.8 54 | 1.57 44 | 3.26 67 | 4.22 66 | 2.36 38 | 3.52 50 | 10.9 36 | 2.66 27 | 5.10 129 | 6.32 127 | 3.15 90 | 2.02 37 | 4.92 45 | 1.71 25 |
OFH [38] | 54.9 | 3.90 56 | 9.77 51 | 3.62 85 | 2.84 40 | 11.0 65 | 2.04 25 | 5.52 73 | 14.4 74 | 1.89 21 | 3.52 63 | 20.5 88 | 1.60 48 | 3.18 55 | 4.06 54 | 2.82 74 | 3.86 63 | 14.1 76 | 3.59 71 | 1.77 6 | 3.62 46 | 1.81 23 | 2.64 56 | 7.08 66 | 2.15 38 |
EPPM w/o HM [88] | 55.0 | 4.25 74 | 11.1 69 | 3.13 56 | 2.36 13 | 8.35 24 | 1.76 13 | 3.72 37 | 10.2 40 | 1.81 17 | 3.24 54 | 14.5 36 | 1.94 65 | 3.16 52 | 3.94 45 | 2.82 74 | 4.78 89 | 12.9 64 | 4.32 92 | 3.64 104 | 4.54 88 | 5.73 122 | 1.76 28 | 4.11 32 | 1.94 33 |
PGM-C [120] | 55.6 | 4.62 91 | 14.0 101 | 3.39 71 | 3.29 72 | 12.3 75 | 2.70 71 | 4.39 55 | 11.7 56 | 3.43 80 | 4.00 78 | 19.8 77 | 2.15 72 | 3.19 57 | 4.23 67 | 2.54 55 | 2.79 14 | 11.9 53 | 2.45 17 | 1.83 8 | 3.21 7 | 1.83 25 | 2.31 47 | 5.87 54 | 1.82 32 |
Occlusion-TV-L1 [63] | 56.8 | 3.59 46 | 9.61 46 | 2.64 28 | 2.93 49 | 10.6 52 | 2.41 46 | 6.16 80 | 15.2 78 | 2.70 61 | 3.32 58 | 17.0 68 | 1.68 52 | 3.38 74 | 4.44 80 | 2.82 74 | 3.10 30 | 13.2 67 | 2.68 30 | 2.17 34 | 3.52 34 | 1.46 9 | 4.63 93 | 11.1 102 | 3.53 73 |
Complementary OF [21] | 57.8 | 4.44 80 | 11.2 72 | 4.04 94 | 2.51 25 | 9.77 42 | 1.74 11 | 3.93 43 | 10.6 44 | 2.04 29 | 3.87 74 | 18.8 72 | 2.19 76 | 3.17 53 | 4.00 49 | 2.92 78 | 4.64 84 | 13.8 73 | 3.64 73 | 2.17 34 | 3.36 17 | 2.51 57 | 3.08 66 | 7.04 64 | 3.65 77 |
Adaptive [20] | 58.8 | 3.29 32 | 9.43 44 | 2.28 9 | 3.10 57 | 11.4 69 | 2.46 54 | 6.58 84 | 15.7 84 | 2.52 54 | 3.14 48 | 15.6 50 | 1.56 43 | 3.67 93 | 4.46 82 | 3.48 97 | 3.32 37 | 13.0 66 | 2.38 13 | 2.76 70 | 4.39 83 | 1.93 37 | 3.58 73 | 8.18 73 | 2.88 59 |
Aniso-Texture [82] | 59.2 | 2.96 10 | 7.72 8 | 2.54 18 | 2.48 24 | 8.26 22 | 2.24 36 | 6.48 82 | 15.9 89 | 2.63 57 | 1.96 4 | 10.1 10 | 0.98 2 | 3.26 67 | 4.21 64 | 2.60 62 | 5.74 102 | 16.9 99 | 5.61 105 | 4.47 121 | 5.88 123 | 3.33 99 | 3.51 72 | 7.12 67 | 3.68 79 |
ACK-Prior [27] | 59.9 | 4.19 70 | 9.27 42 | 3.60 82 | 2.40 14 | 8.21 21 | 1.65 7 | 3.40 26 | 8.96 28 | 1.84 18 | 2.87 36 | 14.4 35 | 1.44 29 | 3.36 72 | 4.15 58 | 3.07 82 | 6.35 110 | 16.1 92 | 4.90 99 | 4.21 116 | 4.80 94 | 6.03 124 | 3.29 69 | 5.99 56 | 2.82 58 |
CPM-Flow [116] | 61.4 | 4.63 92 | 14.1 104 | 3.39 71 | 3.33 73 | 12.5 79 | 2.73 73 | 4.37 53 | 11.7 56 | 3.43 80 | 4.00 78 | 19.9 80 | 2.14 71 | 3.19 57 | 4.23 67 | 2.54 55 | 3.08 28 | 12.0 54 | 2.88 44 | 1.87 14 | 3.44 23 | 1.84 27 | 2.91 62 | 7.48 71 | 2.91 61 |
EpicFlow [102] | 61.7 | 4.61 90 | 14.0 101 | 3.39 71 | 3.33 73 | 12.5 79 | 2.74 74 | 5.37 70 | 14.8 77 | 3.46 83 | 3.94 77 | 19.2 74 | 2.13 70 | 3.20 59 | 4.23 67 | 2.58 61 | 2.87 19 | 12.2 55 | 2.64 26 | 1.83 8 | 3.28 13 | 1.83 25 | 3.21 68 | 7.12 67 | 3.61 74 |
DPOF [18] | 62.0 | 4.67 95 | 12.6 88 | 3.30 60 | 3.57 79 | 10.6 52 | 3.12 87 | 3.09 20 | 7.50 16 | 2.32 41 | 3.06 45 | 14.8 38 | 1.82 61 | 3.21 62 | 4.18 60 | 2.79 73 | 4.47 78 | 12.5 60 | 3.33 62 | 4.09 113 | 3.92 64 | 6.96 126 | 2.09 43 | 4.39 37 | 1.74 27 |
DeepFlow2 [108] | 62.6 | 4.04 61 | 11.2 72 | 3.38 69 | 3.80 81 | 12.4 78 | 2.86 79 | 5.12 66 | 13.4 69 | 3.00 68 | 4.17 84 | 20.1 82 | 2.18 75 | 2.96 40 | 3.97 47 | 2.08 19 | 3.06 27 | 12.6 61 | 2.69 31 | 2.17 34 | 3.24 10 | 2.71 64 | 4.74 95 | 10.4 95 | 4.38 95 |
TCOF [69] | 63.4 | 4.17 67 | 10.4 63 | 3.71 88 | 3.17 58 | 10.7 59 | 2.59 66 | 6.58 84 | 15.7 84 | 3.82 88 | 3.69 72 | 16.1 59 | 2.37 83 | 3.78 97 | 4.95 112 | 2.47 49 | 2.59 8 | 8.47 15 | 2.58 22 | 3.66 106 | 4.83 95 | 2.67 62 | 1.83 31 | 4.20 35 | 1.46 19 |
ROF-ND [107] | 63.5 | 4.12 62 | 10.0 55 | 3.37 68 | 2.78 34 | 8.82 29 | 2.12 29 | 4.61 59 | 11.9 59 | 2.09 32 | 2.23 10 | 6.56 3 | 1.69 54 | 3.60 90 | 4.75 101 | 2.85 77 | 4.92 92 | 13.6 71 | 3.75 76 | 4.59 123 | 5.18 108 | 4.10 112 | 2.67 58 | 5.19 49 | 3.46 72 |
RFlow [90] | 64.5 | 3.82 52 | 10.0 55 | 3.44 77 | 2.61 29 | 9.73 41 | 2.02 23 | 5.66 75 | 14.5 75 | 2.05 30 | 3.93 76 | 23.1 102 | 1.90 63 | 3.24 64 | 4.19 61 | 2.66 69 | 4.12 74 | 15.2 86 | 3.34 64 | 2.61 59 | 3.56 39 | 2.65 61 | 4.48 88 | 10.5 98 | 3.93 88 |
HBM-GC [105] | 65.3 | 5.25 101 | 10.5 64 | 4.34 101 | 3.17 58 | 8.78 28 | 2.94 83 | 4.38 54 | 10.6 44 | 2.68 60 | 3.59 66 | 12.8 14 | 2.47 86 | 2.96 40 | 3.64 26 | 2.64 67 | 3.96 69 | 8.26 11 | 3.56 70 | 4.40 119 | 5.92 124 | 3.62 103 | 2.55 52 | 6.34 61 | 3.29 67 |
Steered-L1 [118] | 65.3 | 3.30 33 | 8.44 24 | 2.91 47 | 1.89 1 | 7.14 8 | 1.60 6 | 3.61 32 | 9.91 38 | 1.89 21 | 3.45 61 | 19.4 76 | 1.64 50 | 3.42 77 | 4.30 72 | 3.39 90 | 5.18 95 | 14.5 79 | 4.37 94 | 5.09 128 | 5.05 103 | 10.1 130 | 5.56 102 | 10.2 93 | 6.24 108 |
DMF_ROB [140] | 66.9 | 4.37 77 | 12.3 82 | 3.62 85 | 3.46 77 | 12.9 84 | 2.60 68 | 5.98 77 | 15.8 86 | 3.23 75 | 4.05 80 | 19.8 77 | 2.15 72 | 3.10 49 | 4.06 54 | 2.57 60 | 3.79 58 | 14.3 77 | 3.13 52 | 1.88 15 | 3.12 5 | 1.99 40 | 4.34 83 | 10.0 88 | 3.87 85 |
ComplOF-FED-GPU [35] | 67.2 | 4.28 76 | 11.3 74 | 3.70 87 | 3.25 65 | 13.0 85 | 2.16 30 | 4.06 48 | 11.2 52 | 1.95 24 | 3.91 75 | 19.2 74 | 2.01 68 | 3.20 59 | 4.15 58 | 2.64 67 | 4.61 83 | 16.1 92 | 3.90 80 | 2.98 79 | 3.77 53 | 3.69 104 | 2.85 60 | 7.44 70 | 2.53 50 |
SRR-TVOF-NL [91] | 67.6 | 4.47 82 | 10.9 67 | 3.32 64 | 4.04 85 | 13.2 88 | 2.90 81 | 4.81 61 | 12.5 62 | 3.15 72 | 3.33 59 | 15.3 44 | 1.61 49 | 3.24 64 | 4.03 53 | 2.70 71 | 3.94 67 | 11.8 52 | 3.33 62 | 4.16 114 | 5.21 111 | 3.44 102 | 2.06 41 | 3.48 25 | 2.42 46 |
TF+OM [100] | 69.8 | 3.97 58 | 10.2 58 | 2.94 49 | 2.91 47 | 9.12 34 | 2.57 64 | 5.22 69 | 11.5 54 | 6.92 98 | 3.59 66 | 16.1 59 | 2.28 80 | 3.20 59 | 3.97 47 | 3.11 83 | 4.70 87 | 14.5 79 | 4.32 92 | 3.06 84 | 4.84 96 | 2.71 64 | 3.93 77 | 8.79 78 | 4.32 94 |
Aniso. Huber-L1 [22] | 70.5 | 3.71 51 | 10.1 57 | 3.08 54 | 4.36 92 | 13.0 85 | 3.77 94 | 6.92 88 | 15.3 80 | 3.60 86 | 3.54 64 | 15.9 57 | 2.04 69 | 3.38 74 | 4.45 81 | 2.47 49 | 3.88 64 | 12.9 64 | 2.74 35 | 3.37 93 | 4.36 82 | 2.85 75 | 3.16 67 | 7.52 72 | 2.90 60 |
DeepFlow [86] | 71.4 | 4.49 83 | 11.7 77 | 4.14 96 | 4.26 89 | 12.8 82 | 3.36 88 | 5.96 76 | 14.2 73 | 5.10 94 | 4.89 96 | 23.1 102 | 2.67 89 | 2.98 42 | 4.00 49 | 2.11 23 | 3.26 33 | 13.5 68 | 2.84 43 | 2.09 29 | 3.10 3 | 2.77 66 | 5.83 104 | 11.4 104 | 5.45 105 |
Classic++ [32] | 72.2 | 3.37 38 | 9.67 50 | 2.91 47 | 3.28 70 | 12.1 73 | 2.61 70 | 5.46 72 | 14.1 72 | 3.00 68 | 3.63 68 | 20.2 85 | 1.70 55 | 3.24 64 | 4.34 74 | 2.60 62 | 4.65 85 | 16.0 90 | 3.60 72 | 3.09 85 | 3.94 67 | 3.28 97 | 4.64 94 | 10.4 95 | 3.71 80 |
TV-L1-improved [17] | 72.5 | 3.36 36 | 9.63 47 | 2.62 25 | 2.82 38 | 10.7 59 | 2.23 34 | 6.50 83 | 15.8 86 | 2.73 63 | 3.80 73 | 21.3 93 | 1.76 58 | 3.34 71 | 4.38 78 | 2.39 42 | 5.97 104 | 18.1 106 | 5.67 106 | 3.57 100 | 4.92 101 | 3.43 101 | 4.01 80 | 9.84 86 | 3.44 71 |
LocallyOriented [52] | 75.2 | 4.54 85 | 12.8 90 | 3.27 59 | 4.73 97 | 14.8 99 | 3.73 93 | 7.77 95 | 18.3 103 | 3.44 82 | 3.56 65 | 15.6 50 | 2.22 77 | 3.46 81 | 4.47 83 | 2.69 70 | 3.15 31 | 10.2 34 | 3.19 56 | 2.61 59 | 4.20 76 | 2.52 58 | 4.39 85 | 8.52 75 | 5.23 101 |
SIOF [67] | 75.4 | 4.23 72 | 10.2 58 | 3.31 62 | 3.97 83 | 14.5 97 | 2.97 84 | 7.81 97 | 16.4 92 | 7.48 100 | 4.82 92 | 20.1 82 | 2.96 92 | 3.54 87 | 4.49 84 | 3.12 84 | 4.31 76 | 13.5 68 | 4.13 86 | 2.36 45 | 3.59 42 | 1.68 17 | 3.46 71 | 7.39 69 | 3.37 69 |
TriangleFlow [30] | 77.7 | 4.12 62 | 10.6 65 | 3.47 78 | 3.47 78 | 13.1 87 | 2.41 46 | 6.00 78 | 15.2 78 | 2.17 35 | 2.99 42 | 16.0 58 | 1.58 45 | 4.46 119 | 5.79 124 | 4.15 111 | 5.42 98 | 13.9 75 | 5.24 100 | 3.10 87 | 5.47 116 | 2.90 79 | 3.02 64 | 6.82 63 | 3.64 76 |
Brox et al. [5] | 78.2 | 4.44 80 | 12.4 83 | 4.22 99 | 3.72 80 | 13.5 91 | 3.06 85 | 4.97 64 | 13.3 68 | 3.11 70 | 4.58 90 | 22.0 96 | 2.37 83 | 3.79 99 | 4.60 91 | 4.33 115 | 3.91 66 | 17.0 100 | 3.45 66 | 2.22 42 | 3.79 55 | 1.19 4 | 4.62 92 | 10.0 88 | 3.38 70 |
CRTflow [80] | 78.2 | 4.18 68 | 11.8 79 | 3.20 57 | 3.22 64 | 10.8 63 | 2.43 49 | 6.20 81 | 15.5 82 | 2.63 57 | 4.21 85 | 22.0 96 | 2.24 78 | 3.32 70 | 4.34 74 | 2.44 48 | 7.43 117 | 19.3 112 | 8.15 120 | 2.55 55 | 4.09 72 | 2.59 60 | 4.60 91 | 11.2 103 | 4.45 96 |
OFRF [134] | 79.5 | 4.77 98 | 11.6 75 | 4.03 93 | 8.72 112 | 15.3 103 | 8.51 115 | 8.49 106 | 16.7 94 | 7.32 99 | 4.55 89 | 15.3 44 | 3.16 99 | 2.92 38 | 3.87 39 | 2.13 26 | 3.76 57 | 9.69 29 | 3.22 58 | 2.98 79 | 4.50 87 | 4.04 110 | 4.59 90 | 5.76 52 | 8.61 116 |
BriefMatch [124] | 79.9 | 3.44 43 | 9.01 37 | 2.77 41 | 2.85 43 | 9.93 43 | 2.23 34 | 2.97 16 | 7.65 20 | 1.94 23 | 3.64 69 | 20.1 82 | 1.75 57 | 4.10 113 | 4.90 110 | 5.82 125 | 7.95 119 | 17.8 103 | 8.08 119 | 4.73 125 | 5.20 109 | 12.2 132 | 7.88 121 | 12.0 107 | 13.7 127 |
Rannacher [23] | 80.3 | 4.13 64 | 11.0 68 | 3.61 84 | 3.39 75 | 12.3 75 | 2.80 77 | 7.26 90 | 17.4 99 | 3.59 85 | 4.40 87 | 23.1 102 | 2.24 78 | 3.43 79 | 4.54 88 | 2.56 59 | 5.41 97 | 18.5 107 | 4.23 89 | 2.92 76 | 3.91 63 | 2.82 70 | 3.45 70 | 9.14 79 | 3.27 66 |
F-TV-L1 [15] | 81.3 | 5.44 104 | 12.5 87 | 5.69 108 | 5.46 101 | 15.0 102 | 4.03 96 | 7.48 92 | 16.3 91 | 3.42 79 | 5.08 98 | 23.3 105 | 2.81 91 | 3.42 77 | 4.34 74 | 3.03 80 | 4.05 71 | 15.1 85 | 3.18 55 | 2.43 49 | 3.92 64 | 1.87 30 | 3.90 76 | 9.35 83 | 2.61 53 |
Local-TV-L1 [65] | 82.1 | 5.33 102 | 12.6 88 | 5.19 106 | 6.90 107 | 15.7 106 | 6.22 105 | 10.0 109 | 18.2 102 | 8.89 102 | 5.81 104 | 24.7 110 | 3.70 103 | 3.05 47 | 4.00 49 | 2.39 42 | 4.05 71 | 14.6 81 | 3.09 51 | 1.95 18 | 3.11 4 | 2.15 47 | 5.85 105 | 10.8 100 | 7.34 111 |
SuperFlow [81] | 82.2 | 4.16 66 | 11.1 69 | 3.32 64 | 4.80 98 | 12.2 74 | 4.68 100 | 7.80 96 | 16.0 90 | 10.6 110 | 5.16 100 | 22.4 100 | 3.24 100 | 3.39 76 | 4.24 70 | 3.71 101 | 3.44 44 | 13.7 72 | 2.91 45 | 3.19 90 | 4.62 92 | 1.87 30 | 4.74 95 | 10.6 99 | 4.24 92 |
DF-Auto [115] | 82.4 | 5.04 100 | 13.7 96 | 3.30 60 | 6.51 104 | 14.1 96 | 6.09 104 | 8.14 101 | 16.5 93 | 10.2 108 | 5.06 97 | 21.3 93 | 3.10 98 | 3.74 95 | 4.91 111 | 3.25 87 | 2.67 11 | 11.4 44 | 2.14 7 | 3.36 92 | 5.23 112 | 1.45 8 | 4.45 87 | 9.18 80 | 4.28 93 |
TriFlow [95] | 83.1 | 4.73 97 | 12.4 83 | 3.49 80 | 4.03 84 | 12.5 79 | 3.70 92 | 8.18 103 | 17.2 97 | 10.4 109 | 3.50 62 | 15.4 46 | 2.32 82 | 3.43 79 | 4.21 64 | 3.42 91 | 3.90 65 | 12.3 56 | 3.76 77 | 7.86 134 | 5.72 119 | 16.2 134 | 2.80 59 | 5.89 55 | 2.50 48 |
CLG-TV [48] | 83.2 | 4.00 59 | 10.3 61 | 3.40 74 | 4.33 91 | 12.3 75 | 4.08 97 | 6.78 86 | 15.5 82 | 3.64 87 | 4.07 81 | 17.7 69 | 2.39 85 | 3.79 99 | 4.86 105 | 3.23 86 | 4.48 80 | 16.5 97 | 3.80 79 | 3.55 99 | 4.65 93 | 2.89 78 | 4.00 79 | 10.1 91 | 3.18 65 |
CBF [12] | 84.7 | 3.88 54 | 10.2 58 | 3.50 81 | 4.60 95 | 11.3 68 | 5.06 101 | 5.43 71 | 13.1 66 | 3.39 78 | 4.09 82 | 21.2 92 | 2.16 74 | 3.80 102 | 4.72 100 | 3.52 98 | 4.33 77 | 14.4 78 | 3.01 49 | 4.97 126 | 5.51 117 | 4.93 118 | 3.99 78 | 9.27 82 | 3.91 87 |
Bartels [41] | 86.3 | 4.43 78 | 11.1 69 | 4.17 98 | 2.83 39 | 8.84 30 | 2.56 61 | 4.54 58 | 12.5 62 | 2.80 66 | 4.87 93 | 22.1 98 | 3.05 96 | 3.58 89 | 4.35 77 | 4.15 111 | 5.55 99 | 17.5 101 | 5.78 107 | 3.74 107 | 5.02 102 | 5.98 123 | 5.21 101 | 11.9 106 | 5.20 100 |
Fusion [6] | 86.4 | 4.43 78 | 13.7 96 | 4.08 95 | 2.47 23 | 8.91 31 | 2.24 36 | 3.70 35 | 9.68 34 | 3.12 71 | 3.68 71 | 19.8 77 | 2.54 88 | 4.26 116 | 5.16 117 | 4.31 114 | 6.32 107 | 16.8 98 | 6.15 111 | 4.55 122 | 5.78 121 | 3.10 86 | 7.12 115 | 13.6 116 | 7.86 115 |
p-harmonic [29] | 87.1 | 4.64 93 | 13.0 91 | 4.43 102 | 3.41 76 | 11.9 71 | 2.93 82 | 7.60 93 | 18.1 101 | 3.96 91 | 4.65 91 | 21.0 90 | 2.97 94 | 3.46 81 | 4.33 73 | 3.34 89 | 4.75 88 | 17.5 101 | 4.60 98 | 3.05 82 | 4.17 74 | 2.15 47 | 5.09 100 | 10.9 101 | 3.77 82 |
CNN-flow-warp+ref [117] | 88.0 | 4.93 99 | 14.5 108 | 4.29 100 | 4.18 87 | 11.9 71 | 4.24 98 | 8.23 104 | 19.7 110 | 6.35 97 | 5.13 99 | 24.4 109 | 2.96 92 | 3.55 88 | 4.40 79 | 3.85 104 | 3.82 60 | 15.0 83 | 3.39 65 | 1.96 19 | 3.44 23 | 2.14 46 | 10.0 125 | 14.8 122 | 10.8 123 |
Dynamic MRF [7] | 88.7 | 4.58 88 | 12.4 83 | 4.14 96 | 3.25 65 | 13.9 93 | 2.27 40 | 6.02 79 | 16.8 95 | 2.36 43 | 4.39 86 | 22.6 101 | 2.51 87 | 3.61 91 | 4.55 89 | 3.46 93 | 6.81 112 | 22.2 122 | 6.78 116 | 2.41 47 | 3.48 29 | 3.69 104 | 9.26 123 | 17.8 126 | 10.2 120 |
SegOF [10] | 89.0 | 5.85 105 | 13.5 94 | 3.98 92 | 7.40 108 | 14.9 100 | 8.13 113 | 8.55 107 | 17.3 98 | 9.01 103 | 6.50 109 | 18.1 71 | 5.14 111 | 3.90 106 | 4.53 87 | 4.81 119 | 6.57 111 | 21.7 120 | 6.81 117 | 1.65 3 | 3.49 31 | 1.08 2 | 3.71 74 | 9.23 81 | 3.63 75 |
FlowNetS+ft+v [112] | 89.8 | 4.22 71 | 12.1 81 | 3.48 79 | 4.50 94 | 13.4 89 | 3.85 95 | 8.29 105 | 18.4 104 | 6.20 96 | 4.87 93 | 21.6 95 | 3.01 95 | 3.93 107 | 5.04 114 | 3.47 96 | 3.71 55 | 15.3 87 | 3.21 57 | 3.32 91 | 5.12 105 | 3.87 106 | 3.76 75 | 9.44 84 | 3.74 81 |
LDOF [28] | 90.5 | 4.60 89 | 13.0 91 | 3.77 89 | 4.67 96 | 15.5 105 | 3.67 91 | 5.63 74 | 14.0 71 | 4.21 92 | 5.80 103 | 27.1 119 | 3.43 101 | 3.52 86 | 4.50 85 | 3.46 93 | 4.84 91 | 17.8 103 | 4.04 84 | 2.46 53 | 4.14 73 | 3.25 94 | 4.85 98 | 12.0 107 | 3.78 83 |
Second-order prior [8] | 90.9 | 4.03 60 | 11.6 75 | 3.35 66 | 3.88 82 | 14.0 95 | 3.08 86 | 7.21 89 | 17.6 100 | 3.57 84 | 4.14 83 | 19.9 80 | 2.31 81 | 3.66 92 | 4.86 105 | 2.73 72 | 7.32 115 | 21.2 118 | 6.76 115 | 4.02 110 | 4.58 91 | 4.01 108 | 4.27 82 | 10.4 95 | 5.12 97 |
FlowNet2 [122] | 94.5 | 8.58 120 | 18.6 117 | 6.31 110 | 9.39 117 | 17.6 111 | 9.09 118 | 8.06 100 | 15.8 86 | 9.81 106 | 5.61 102 | 16.2 61 | 4.12 105 | 4.04 110 | 4.88 107 | 3.79 102 | 4.92 92 | 16.2 94 | 4.50 95 | 4.28 117 | 6.73 129 | 2.84 72 | 2.05 40 | 4.54 39 | 1.41 17 |
StereoFlow [44] | 95.1 | 17.1 136 | 28.1 136 | 17.9 135 | 18.7 133 | 29.7 134 | 16.5 128 | 20.1 133 | 30.9 132 | 17.5 128 | 21.2 133 | 38.3 135 | 17.9 131 | 4.60 120 | 5.05 115 | 5.52 121 | 2.38 4 | 11.5 48 | 1.77 2 | 1.25 1 | 2.92 2 | 0.71 1 | 4.49 89 | 10.3 94 | 4.23 91 |
EPMNet [133] | 95.3 | 8.37 119 | 18.8 119 | 6.44 112 | 9.35 116 | 18.4 113 | 8.78 117 | 7.42 91 | 14.7 76 | 8.61 101 | 5.98 106 | 20.4 87 | 4.27 107 | 4.04 110 | 4.88 107 | 3.79 102 | 4.92 92 | 16.2 94 | 4.50 95 | 3.65 105 | 6.14 125 | 2.42 55 | 2.60 54 | 6.15 58 | 1.74 27 |
Ad-TV-NDC [36] | 96.5 | 8.36 118 | 14.0 101 | 11.1 128 | 12.9 124 | 19.9 119 | 12.8 124 | 14.4 120 | 23.1 113 | 12.1 114 | 7.40 112 | 20.6 89 | 6.33 112 | 3.47 83 | 4.66 96 | 2.39 42 | 3.95 68 | 13.8 73 | 3.51 68 | 2.48 54 | 3.75 51 | 2.05 42 | 9.75 124 | 12.1 109 | 16.7 131 |
Learning Flow [11] | 99.5 | 4.23 72 | 11.7 77 | 3.41 76 | 4.16 86 | 15.3 103 | 3.42 89 | 6.78 86 | 16.9 96 | 3.83 89 | 6.41 108 | 25.3 113 | 4.25 106 | 4.66 122 | 6.01 129 | 4.00 107 | 6.33 109 | 20.7 117 | 5.30 101 | 3.09 85 | 4.84 96 | 2.91 81 | 7.08 114 | 15.0 123 | 5.27 102 |
Shiralkar [42] | 99.7 | 4.64 93 | 14.1 104 | 3.94 90 | 4.29 90 | 16.9 109 | 2.77 75 | 7.75 94 | 18.8 106 | 3.19 74 | 5.54 101 | 25.0 112 | 3.56 102 | 3.51 85 | 4.55 89 | 3.04 81 | 7.41 116 | 20.1 116 | 6.41 112 | 3.76 108 | 4.35 81 | 5.28 119 | 6.56 111 | 14.4 121 | 5.30 103 |
StereoOF-V1MT [119] | 100.3 | 4.71 96 | 14.1 104 | 3.95 91 | 5.10 100 | 20.3 121 | 2.78 76 | 7.98 99 | 20.7 111 | 2.57 56 | 4.48 88 | 21.1 91 | 2.79 90 | 4.20 115 | 5.29 119 | 4.10 109 | 6.85 114 | 22.3 123 | 6.42 113 | 2.45 52 | 4.17 74 | 3.15 90 | 10.5 126 | 18.4 129 | 10.5 121 |
IAOF2 [51] | 101.2 | 5.38 103 | 13.7 96 | 4.50 103 | 5.95 103 | 14.6 98 | 5.61 103 | 8.80 108 | 18.8 106 | 9.40 104 | 12.2 122 | 23.8 108 | 13.1 126 | 3.86 103 | 4.89 109 | 3.12 84 | 5.21 96 | 14.9 82 | 4.54 97 | 4.33 118 | 5.15 106 | 3.93 107 | 4.39 85 | 8.57 76 | 3.87 85 |
TVL1_ROB [139] | 102.2 | 11.3 125 | 19.8 120 | 13.0 130 | 12.9 124 | 19.6 118 | 13.7 126 | 17.4 127 | 27.8 126 | 18.0 129 | 12.6 124 | 28.9 121 | 11.8 124 | 3.71 94 | 4.78 103 | 3.46 93 | 4.21 75 | 18.0 105 | 3.99 82 | 1.79 7 | 3.54 38 | 1.21 5 | 7.58 119 | 13.9 119 | 8.92 118 |
Modified CLG [34] | 102.5 | 7.17 113 | 17.1 116 | 6.47 113 | 6.85 106 | 14.9 100 | 7.48 109 | 14.0 116 | 24.8 117 | 15.7 124 | 8.35 115 | 27.3 120 | 6.36 113 | 3.96 108 | 4.99 113 | 4.08 108 | 4.54 82 | 19.3 112 | 4.15 87 | 2.33 44 | 3.86 61 | 2.40 54 | 6.00 106 | 13.8 118 | 5.40 104 |
2D-CLG [1] | 103.1 | 10.1 122 | 22.6 127 | 7.59 118 | 9.84 119 | 16.9 109 | 11.1 123 | 16.9 126 | 28.2 127 | 18.8 132 | 14.1 126 | 31.1 125 | 13.1 126 | 3.86 103 | 4.62 94 | 4.53 116 | 5.98 105 | 21.2 118 | 5.97 109 | 1.76 5 | 3.14 6 | 1.46 9 | 6.29 108 | 12.9 115 | 5.81 106 |
SPSA-learn [13] | 103.6 | 6.84 112 | 16.7 114 | 6.74 114 | 8.47 110 | 19.4 116 | 7.49 110 | 12.5 112 | 23.1 113 | 13.1 118 | 8.40 116 | 25.8 116 | 7.08 115 | 3.87 105 | 4.66 96 | 4.10 109 | 6.32 107 | 18.8 108 | 6.89 118 | 2.56 56 | 3.85 60 | 1.79 21 | 7.29 116 | 12.5 112 | 7.47 113 |
GraphCuts [14] | 103.6 | 6.25 106 | 14.3 107 | 5.53 107 | 8.60 111 | 20.1 120 | 6.61 107 | 7.91 98 | 15.4 81 | 10.9 111 | 4.88 95 | 19.0 73 | 3.05 96 | 3.78 97 | 4.71 98 | 3.94 105 | 8.74 124 | 16.4 96 | 5.39 103 | 4.04 111 | 4.87 98 | 4.85 117 | 6.35 109 | 12.2 110 | 6.05 107 |
Filter Flow [19] | 103.7 | 6.48 107 | 14.6 109 | 4.96 104 | 5.73 102 | 15.7 106 | 5.07 102 | 10.1 110 | 18.6 105 | 14.3 120 | 9.04 117 | 23.3 105 | 7.80 117 | 3.98 109 | 4.71 98 | 4.21 113 | 5.86 103 | 15.0 83 | 5.41 104 | 4.98 127 | 6.87 130 | 2.78 67 | 4.82 97 | 8.66 77 | 3.65 77 |
HBpMotionGpu [43] | 105.4 | 6.57 109 | 15.0 111 | 5.17 105 | 8.29 109 | 18.0 112 | 8.29 114 | 14.1 117 | 26.5 120 | 13.2 119 | 6.12 107 | 25.3 113 | 3.94 104 | 3.79 99 | 4.62 94 | 3.97 106 | 4.80 90 | 15.7 88 | 4.11 85 | 4.40 119 | 5.20 109 | 2.87 77 | 6.28 107 | 11.7 105 | 7.31 110 |
GroupFlow [9] | 106.0 | 8.00 115 | 18.6 117 | 8.09 120 | 11.1 122 | 23.7 126 | 10.3 121 | 12.6 113 | 25.6 118 | 12.8 116 | 5.84 105 | 20.3 86 | 4.39 108 | 4.69 123 | 5.81 125 | 3.67 99 | 9.29 125 | 22.4 124 | 10.1 127 | 2.11 32 | 3.99 68 | 2.29 51 | 5.75 103 | 10.0 88 | 7.39 112 |
IAOF [50] | 106.2 | 6.49 108 | 14.6 109 | 6.42 111 | 9.22 115 | 18.5 114 | 7.94 112 | 16.4 125 | 27.4 124 | 13.0 117 | 8.22 113 | 22.2 99 | 7.73 116 | 3.77 96 | 4.76 102 | 3.42 91 | 6.84 113 | 18.8 108 | 4.23 89 | 3.59 101 | 4.46 85 | 2.83 71 | 7.51 118 | 10.1 91 | 10.6 122 |
Black & Anandan [4] | 106.8 | 6.81 111 | 15.4 112 | 7.43 116 | 8.77 113 | 19.5 117 | 7.35 108 | 13.0 114 | 22.9 112 | 12.5 115 | 8.29 114 | 26.1 117 | 6.77 114 | 4.18 114 | 5.28 118 | 3.69 100 | 6.19 106 | 20.0 115 | 5.34 102 | 3.63 102 | 5.05 103 | 1.79 21 | 6.45 110 | 12.2 110 | 5.17 99 |
BlockOverlap [61] | 110.1 | 6.67 110 | 13.1 93 | 5.87 109 | 6.62 105 | 13.9 93 | 6.53 106 | 10.6 111 | 19.5 109 | 10.1 107 | 6.97 111 | 24.9 111 | 5.13 110 | 4.38 117 | 4.61 93 | 6.37 128 | 7.47 118 | 15.7 88 | 6.05 110 | 6.23 130 | 6.41 128 | 13.0 133 | 6.92 113 | 9.60 85 | 12.2 125 |
Nguyen [33] | 110.2 | 7.88 114 | 16.8 115 | 7.02 115 | 13.4 126 | 19.0 115 | 15.3 127 | 17.6 128 | 28.9 128 | 17.2 127 | 12.0 121 | 26.9 118 | 11.6 123 | 4.38 117 | 5.07 116 | 5.58 124 | 5.69 101 | 19.7 114 | 5.93 108 | 2.75 68 | 4.02 70 | 1.91 36 | 6.59 112 | 12.5 112 | 6.52 109 |
UnFlow [129] | 111.0 | 14.6 134 | 25.8 132 | 9.09 124 | 9.40 118 | 16.8 108 | 9.89 120 | 14.2 118 | 26.9 121 | 11.2 112 | 10.0 118 | 25.4 115 | 8.67 119 | 5.43 129 | 5.90 126 | 6.72 129 | 8.64 122 | 24.0 126 | 9.41 125 | 3.51 97 | 4.90 99 | 1.37 7 | 4.37 84 | 12.6 114 | 3.33 68 |
2bit-BM-tele [98] | 112.5 | 8.00 115 | 15.8 113 | 8.40 122 | 4.91 99 | 13.4 89 | 4.67 99 | 8.14 101 | 19.0 108 | 5.12 95 | 6.62 110 | 23.5 107 | 5.04 109 | 4.08 112 | 4.78 103 | 4.61 118 | 8.68 123 | 18.8 108 | 8.31 121 | 6.46 132 | 7.08 132 | 9.47 129 | 7.36 117 | 14.1 120 | 9.62 119 |
Horn & Schunck [3] | 116.2 | 8.01 117 | 19.9 121 | 8.38 121 | 9.13 114 | 23.2 125 | 7.71 111 | 14.2 118 | 25.9 119 | 14.6 122 | 12.4 123 | 30.6 123 | 11.3 122 | 4.64 121 | 5.64 121 | 4.60 117 | 8.21 121 | 24.4 127 | 8.45 122 | 4.01 109 | 5.41 113 | 1.95 38 | 9.16 122 | 17.5 124 | 8.86 117 |
SILK [79] | 117.6 | 9.34 121 | 20.4 122 | 10.5 127 | 10.4 120 | 21.9 122 | 10.3 121 | 16.0 124 | 27.5 125 | 14.5 121 | 10.3 119 | 29.0 122 | 8.54 118 | 4.81 124 | 5.65 122 | 5.56 123 | 9.41 126 | 25.4 129 | 8.74 123 | 2.79 71 | 3.68 49 | 4.62 115 | 10.9 127 | 17.8 126 | 12.3 126 |
Heeger++ [104] | 119.2 | 11.9 128 | 21.8 125 | 8.08 119 | 12.5 123 | 29.7 134 | 9.42 119 | 14.8 121 | 27.1 122 | 9.68 105 | 14.3 127 | 31.0 124 | 12.7 125 | 4.98 126 | 5.74 123 | 4.97 120 | 17.5 134 | 34.1 135 | 18.4 134 | 2.75 68 | 5.44 114 | 2.15 47 | 12.3 129 | 18.8 130 | 14.8 129 |
TI-DOFE [24] | 120.2 | 13.4 132 | 23.2 128 | 16.5 134 | 16.5 130 | 24.1 127 | 18.2 132 | 20.2 134 | 31.1 134 | 20.6 133 | 19.9 132 | 32.9 128 | 20.8 133 | 4.89 125 | 5.90 126 | 5.54 122 | 8.04 120 | 23.9 125 | 8.81 124 | 2.97 78 | 4.34 80 | 1.88 33 | 10.9 127 | 17.7 125 | 11.9 124 |
H+S_ROB [138] | 121.8 | 13.0 130 | 27.1 135 | 9.66 125 | 13.4 126 | 24.8 129 | 13.4 125 | 18.7 132 | 30.9 132 | 18.3 131 | 25.8 136 | 35.7 132 | 26.4 135 | 7.08 134 | 8.13 134 | 9.10 133 | 14.6 132 | 31.3 134 | 16.3 132 | 2.17 34 | 4.44 84 | 2.11 45 | 15.1 133 | 19.9 132 | 14.2 128 |
HCIC-L [99] | 124.1 | 15.7 135 | 22.0 126 | 10.1 126 | 31.5 136 | 26.6 132 | 41.0 136 | 14.8 121 | 23.1 113 | 16.8 126 | 18.4 131 | 34.4 130 | 18.2 132 | 5.94 130 | 6.35 130 | 6.35 127 | 10.6 129 | 19.2 111 | 11.4 129 | 18.7 136 | 17.8 136 | 19.2 135 | 4.93 99 | 8.34 74 | 5.16 98 |
SLK [47] | 124.3 | 11.6 126 | 26.0 133 | 14.6 133 | 15.3 129 | 25.0 130 | 17.5 130 | 17.8 130 | 30.1 131 | 18.1 130 | 25.4 135 | 33.6 129 | 28.0 136 | 5.25 127 | 5.90 126 | 7.03 130 | 10.3 128 | 27.4 131 | 10.6 128 | 2.89 75 | 4.47 86 | 2.94 83 | 14.9 132 | 20.7 133 | 18.8 132 |
FFV1MT [106] | 125.1 | 12.0 129 | 23.3 129 | 8.83 123 | 10.7 121 | 26.6 132 | 8.71 116 | 15.6 123 | 29.0 129 | 12.0 113 | 16.6 130 | 36.3 134 | 15.5 129 | 6.51 133 | 6.40 131 | 10.4 134 | 16.2 133 | 30.7 133 | 17.7 133 | 3.41 95 | 5.44 114 | 3.35 100 | 12.3 129 | 18.8 130 | 14.8 129 |
Adaptive flow [45] | 126.9 | 13.2 131 | 20.8 123 | 14.0 132 | 17.1 132 | 22.0 123 | 17.9 131 | 18.1 131 | 27.1 122 | 22.8 135 | 11.8 120 | 31.1 125 | 10.5 120 | 6.35 132 | 7.13 133 | 6.25 126 | 9.87 127 | 21.8 121 | 9.44 126 | 12.6 135 | 11.4 135 | 20.0 136 | 7.75 120 | 13.6 116 | 7.73 114 |
PGAM+LK [55] | 128.2 | 11.8 127 | 25.6 130 | 13.9 131 | 14.8 128 | 24.4 128 | 16.7 129 | 13.2 115 | 24.0 116 | 15.0 123 | 16.2 129 | 41.2 136 | 15.3 128 | 5.40 128 | 5.45 120 | 8.10 131 | 12.3 131 | 26.5 130 | 12.1 130 | 7.42 133 | 8.24 134 | 7.87 127 | 13.2 131 | 18.3 128 | 19.4 133 |
Periodicity [78] | 129.2 | 11.2 124 | 27.0 134 | 7.46 117 | 16.6 131 | 29.8 136 | 18.2 132 | 25.3 136 | 31.2 136 | 24.9 136 | 12.7 125 | 35.7 132 | 11.1 121 | 31.7 136 | 41.4 136 | 25.1 136 | 23.8 136 | 41.5 136 | 23.8 136 | 2.92 76 | 5.62 118 | 6.90 125 | 18.6 135 | 33.1 136 | 22.3 134 |
FOLKI [16] | 129.9 | 10.5 123 | 25.6 130 | 11.9 129 | 20.9 134 | 26.2 131 | 26.1 134 | 17.6 128 | 31.1 134 | 16.5 125 | 15.4 128 | 32.6 127 | 16.0 130 | 6.16 131 | 6.53 132 | 9.07 132 | 12.2 130 | 29.7 132 | 13.0 131 | 4.67 124 | 5.83 122 | 9.41 128 | 18.2 134 | 22.8 134 | 25.1 135 |
Pyramid LK [2] | 132.8 | 13.9 133 | 20.9 124 | 21.4 136 | 24.1 135 | 23.1 124 | 30.2 135 | 20.9 135 | 29.5 130 | 21.9 134 | 22.2 134 | 34.6 131 | 25.0 134 | 18.7 135 | 23.1 135 | 20.2 135 | 21.2 135 | 24.5 128 | 21.0 135 | 6.41 131 | 7.02 131 | 10.8 131 | 25.6 136 | 31.5 135 | 34.5 136 |
AdaConv-v1 [126] | 137.0 | 39.2 137 | 39.9 137 | 41.8 137 | 73.0 137 | 74.5 137 | 71.1 137 | 70.1 137 | 67.3 137 | 71.8 137 | 64.4 137 | 66.2 137 | 65.9 137 | 76.5 137 | 78.1 137 | 72.0 137 | 68.2 137 | 64.9 137 | 66.5 137 | 52.3 137 | 45.1 137 | 70.9 137 | 81.8 137 | 81.6 137 | 82.3 137 |
SepConv-v1 [127] | 137.0 | 39.2 137 | 39.9 137 | 41.8 137 | 73.0 137 | 74.5 137 | 71.1 137 | 70.1 137 | 67.3 137 | 71.8 137 | 64.4 137 | 66.2 137 | 65.9 137 | 76.5 137 | 78.1 137 | 72.0 137 | 68.2 137 | 64.9 137 | 66.5 137 | 52.3 137 | 45.1 137 | 70.9 137 | 81.8 137 | 81.6 137 | 82.3 137 |
SuperSlomo [132] | 137.0 | 39.2 137 | 39.9 137 | 41.8 137 | 73.0 137 | 74.5 137 | 71.1 137 | 70.1 137 | 67.3 137 | 71.8 137 | 64.4 137 | 66.2 137 | 65.9 137 | 76.5 137 | 78.1 137 | 72.0 137 | 68.2 137 | 64.9 137 | 66.5 137 | 52.3 137 | 45.1 137 | 70.9 137 | 81.8 137 | 81.6 137 | 82.3 137 |
FGIK [136] | 137.0 | 39.2 137 | 39.9 137 | 41.8 137 | 73.0 137 | 74.5 137 | 71.1 137 | 70.1 137 | 67.3 137 | 71.8 137 | 64.4 137 | 66.2 137 | 65.9 137 | 76.5 137 | 78.1 137 | 72.0 137 | 68.2 137 | 64.9 137 | 66.5 137 | 52.3 137 | 45.1 137 | 70.9 137 | 81.8 137 | 81.6 137 | 82.3 137 |
CtxSyn [137] | 137.0 | 39.2 137 | 39.9 137 | 41.8 137 | 73.0 137 | 74.5 137 | 71.1 137 | 70.1 137 | 67.3 137 | 71.8 137 | 64.4 137 | 66.2 137 | 65.9 137 | 76.5 137 | 78.1 137 | 72.0 137 | 68.2 137 | 64.9 137 | 66.5 137 | 52.3 137 | 45.1 137 | 70.9 137 | 81.8 137 | 81.6 137 | 82.3 137 |
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. IEEE TIP 26(8):4055-4067, 2017. | |
[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-invariant descriptors in variational optical flow estimation. ICIP 2017. | |
[132] SuperSlomo | 0.5 | 2 | color | Anonymous. (Interpolation results only.) Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. CVPR 2018 submission 325. | |
[133] EPMNet | 0.061 | 2 | color | Anonymous. EPM-convolution multilayer-network for optical flow estimation. ICME 2018 submission 1119. | |
[134] OFRF | 90 | 2 | color | T. Mai, M. Gouiffes, and S. Bouchafa. Optical Flow refinement using iterative propagation under color, proximity and flow reliability constraints. Submitted to Signal, Image and Video Processing 2017. | |
[135] 3DFlow | 328 | 2 | color | J. Chen, Z. Cai, J. Lai, and X. Xie. A filtering based framework for optical flow estimation. IEEE TCSVT 2018. | |
[136] FGIK | 0.18 | 2 | color | Anonymous. (Interpolation results only.) Learning flow-guided interpolation kernels for video frame synthesis. ECCV 2018 submission 433. | |
[137] CtxSyn | 0.07 | 2 | color | S. Niklaus and F. Liu. (Interpolation results only.) Context-aware synthesis for video frame interpolation. CVPR 2018. | |
[138] H+S_ROB | 5 | 2 | color | ROB 2018 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann. Horn-Schunck optical flow with a multi-scale strategy. Image Processing On Line 3:151–172, 2013. | |
[139] TVL1_ROB | 1 | 2 | color | ROB 2018 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo. TV-L1 optical flow estimation. Image Processing On Line 3:137-150, 2013. | |
[140] DMF_ROB | 10 | 2 | color | ROB 2018 baseline submission, based on: P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013. | |
[141] JOF | 657 | 2 | gray | C. Zhang, L. Ge, Z. Chen, M. Li, W. Liu, and H. Chen. Refined TV-L1 optical flow estimation using joint filtering. Submitted to IEEE TMM, 2018. |