MPI Sintel Dataset

Results and Rankings

Results for methods appear here after users upload them and approve them for public display.


Final
Clean
EPE all EPE matched EPE unmatched d0-10 d10-60 d60-140 s0-10 s10-40 s40+
GroundTruth [1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Visualize Results
HD3-F [2] 4.788 2.247 25.497 3.784 1.811 1.572 0.765 2.240 30.916 Visualize Results
FTD [3] 5.486 2.562 29.339 3.973 2.146 1.847 0.768 2.273 36.690 Visualize Results
HTC [4] 5.385 2.635 27.808 3.985 2.135 2.019 0.876 2.351 35.104 Visualize Results
SelFlow [5] 4.262 2.040 22.369 4.083 1.715 1.287 0.582 2.343 27.154 Visualize Results
LiteFlowNet [6] 5.381 2.419 29.535 4.090 2.097 1.729 0.754 2.747 34.722 Visualize Results
SF_Net [7] 4.860 2.301 25.732 4.121 1.991 1.493 0.812 2.606 30.402 Visualize Results
LiteFlowNet2 [8] 4.903 2.346 25.769 4.142 2.014 1.546 0.797 2.529 31.039 Visualize Results
CompactFlow [9] 4.675 2.142 25.336 4.153 1.840 1.361 0.858 2.712 28.378 Visualize Results
CompactFlow-woscv [10] 4.858 2.213 26.439 4.220 1.867 1.453 0.906 2.701 29.709 Visualize Results
IRR-PWC [11] 4.751 2.278 24.910 4.271 1.919 1.369 0.876 2.541 29.325 Visualize Results
FlowSR [12] 5.027 2.482 25.771 4.451 2.152 1.585 0.748 2.929 31.328 Visualize Results
PWC-Net+KF2 [13] 4.979 2.430 25.783 4.571 2.078 1.480 0.778 3.112 30.353 Visualize Results
PWC-Net+KF [14] 4.964 2.438 25.561 4.603 2.066 1.500 0.739 3.087 30.465 Visualize Results
AutoScaler+ [15] 6.076 2.569 34.656 4.610 2.195 1.863 1.298 3.737 35.431 Visualize Results
PWC-Net [16] 5.042 2.445 26.221 4.636 2.087 1.475 0.799 2.986 31.070 Visualize Results
PWC-Net_ROB [17] 4.903 2.454 24.878 4.636 2.090 1.517 0.799 3.029 29.800 Visualize Results
DCFlow+KF2 [18] 5.067 2.195 28.475 4.652 1.948 1.393 1.021 3.232 29.574 Visualize Results
3Dflow [19] 5.650 2.497 31.340 4.662 2.070 1.530 0.990 3.201 34.681 Visualize Results
MFF [20] 4.566 2.216 23.732 4.664 2.017 1.222 0.893 2.902 26.810 Visualize Results
DCFlow [21] 5.119 2.283 28.228 4.665 2.108 1.440 1.052 3.434 29.351 Visualize Results
FlowFields+ [22] 5.707 2.684 30.356 4.691 2.117 1.793 1.131 3.330 34.167 Visualize Results
PST [23] 5.416 2.572 28.592 4.695 2.088 1.761 1.172 3.546 30.987 Visualize Results
ProbFlowFields [24] 5.696 2.545 31.371 4.696 2.150 1.686 1.146 3.658 33.188 Visualize Results
CPM_AUG [25] 5.645 2.737 29.362 4.707 2.150 1.918 1.087 3.306 33.925 Visualize Results
FlowFieldsCNN [26] 5.363 2.303 30.313 4.718 2.020 1.399 1.032 3.065 32.422 Visualize Results
InterpoNet_ff [27] 5.535 2.372 31.296 4.720 2.018 1.532 1.064 3.496 32.633 Visualize Results
SPM-BPv2 [28] 5.812 2.754 30.743 4.736 2.255 1.933 1.048 3.468 35.118 Visualize Results
TIMCflow [29] 5.049 2.094 29.134 4.738 1.812 1.221 0.922 3.226 29.926 Visualize Results
S2F-IF [30] 5.417 2.549 28.795 4.745 2.198 1.712 1.157 3.468 31.262 Visualize Results
DCFlow+KF [31] 5.120 2.245 28.557 4.747 1.981 1.473 1.031 3.421 29.511 Visualize Results
2chPWC-Net-ft [32] 5.712 2.769 29.717 4.752 2.499 1.918 0.879 3.176 35.826 Visualize Results
FlowSAC_dcf [33] 4.932 2.232 26.936 4.763 2.047 1.303 1.044 3.450 27.794 Visualize Results
PWC-Net+ [34] 4.596 2.254 23.696 4.781 2.045 1.234 0.945 2.978 26.620 Visualize Results
FlowSAC_ff [35] 5.390 2.483 29.086 4.785 2.258 1.459 1.123 3.664 30.723 Visualize Results
FlowNet2-ft-sintel [36] 5.739 2.752 30.108 4.818 2.557 1.735 0.959 3.228 35.538 Visualize Results
FlowFields [37] 5.810 2.621 31.799 4.851 2.232 1.682 1.157 3.739 33.890 Visualize Results
FullFlow+KF [38] 5.802 2.761 30.582 4.866 2.438 1.795 1.037 3.287 35.517 Visualize Results
SBFlow [39] 6.114 2.854 32.675 4.871 2.440 2.006 1.076 3.555 37.287 Visualize Results
Deep-EIP [40] 6.123 2.704 34.004 4.887 2.417 1.976 1.236 3.155 37.558 Visualize Results
NNF-Local [41] 7.249 2.973 42.088 4.896 2.817 2.218 1.159 4.183 44.866 Visualize Results
FullFlow [42] 5.895 2.838 30.793 4.905 2.506 1.913 1.136 3.373 35.592 Visualize Results
FlowFields++ [43] 5.486 2.614 28.900 4.927 2.273 1.706 1.156 3.326 32.200 Visualize Results
SfM-PM [44] 5.466 2.683 28.147 4.963 2.186 1.782 1.031 3.182 32.991 Visualize Results
SegFlow153 [45] 6.191 2.940 32.682 4.969 2.492 2.119 1.201 3.865 36.570 Visualize Results
InterpoNet_cpm [46] 5.627 2.594 30.344 4.975 2.213 1.640 1.042 3.575 33.321 Visualize Results
PGM-C [47] 5.591 2.672 29.389 4.975 2.340 1.791 1.057 3.421 33.339 Visualize Results
ProFlow_ROB [48] 5.015 2.659 24.192 4.985 2.185 1.771 0.964 2.989 29.987 Visualize Results
ProFlow [49] 5.017 2.596 24.736 5.016 2.146 1.601 0.910 2.809 30.715 Visualize Results
CPM-Flow [50] 5.960 2.990 30.177 5.038 2.419 2.143 1.155 3.755 35.136 Visualize Results
F3-MPLF [51] 6.274 3.093 32.210 5.045 2.859 2.082 1.083 3.227 39.409 Visualize Results
ContinualFlow_ROB [52] 4.528 2.723 19.248 5.050 2.573 1.713 0.872 3.114 26.063 Visualize Results
SegFlow113 [53] 6.423 2.995 34.361 5.050 2.496 2.139 1.193 3.818 38.664 Visualize Results
CPM2 [54] 6.180 3.012 32.008 5.059 2.399 2.126 1.212 3.625 37.014 Visualize Results
InterpoNet_dm [55] 5.711 2.650 30.642 5.078 2.308 1.762 0.997 3.702 33.929 Visualize Results
DiscreteFlow [56] 6.077 2.937 31.685 5.106 2.459 1.945 1.074 3.832 36.339 Visualize Results
MR-Flow [57] 5.376 2.818 26.235 5.109 2.395 1.755 0.908 3.443 32.221 Visualize Results
MPIF [58] 5.591 2.663 29.473 5.109 2.206 1.723 1.102 3.163 33.748 Visualize Results
FlowNet2 [59] 6.016 2.977 30.807 5.139 2.786 2.102 1.243 4.027 34.505 Visualize Results
SegFlow33 [60] 6.726 3.200 35.458 5.141 2.553 2.280 1.276 4.110 40.076 Visualize Results
RicFlow [61] 5.620 2.765 28.907 5.146 2.366 1.679 1.088 3.364 33.573 Visualize Results
DiscreteFlow+OIR [62] 5.862 2.864 30.303 5.153 2.427 1.825 1.118 3.698 34.633 Visualize Results
InterpoNet_df [63] 6.044 2.788 32.581 5.154 2.404 1.779 0.985 3.377 37.596 Visualize Results
Deep+R [64] 6.769 2.996 37.494 5.182 2.770 2.064 1.157 3.837 41.687 Visualize Results
EpicFlow [65] 6.285 3.060 32.564 5.205 2.611 2.216 1.135 3.727 38.021 Visualize Results
SegFlow193 [66] 7.071 3.380 37.136 5.206 2.781 2.494 1.236 3.613 44.373 Visualize Results
DeepFlow2 [67] 6.928 3.093 38.166 5.207 2.819 2.144 1.182 3.859 42.854 Visualize Results
PPM [68] 7.177 3.392 38.025 5.212 2.771 2.642 1.155 3.790 45.233 Visualize Results
PatchBatch+Inter [69] 6.222 3.167 31.122 5.246 2.862 2.152 0.905 3.200 39.912 Visualize Results
DIP-Flow [70] 6.014 2.922 31.224 5.268 2.521 1.848 1.092 3.788 35.821 Visualize Results
SelFlow [71] 6.571 3.119 34.721 5.275 2.834 2.092 1.358 3.883 38.945 Visualize Results
AugFNG_ROB [72] 5.500 2.978 26.052 5.282 2.694 1.956 1.113 4.083 30.666 Visualize Results
GlobalPatchCollider [73] 6.040 2.938 31.309 5.310 2.624 1.824 1.102 3.589 36.455 Visualize Results
FF++_ROB [74] 6.496 2.990 35.057 5.319 2.540 2.045 1.030 4.182 39.191 Visualize Results
FALDOI [75] 7.337 3.580 37.904 5.332 3.434 2.885 1.487 4.355 43.526 Visualize Results
Devon [76] 6.350 3.234 31.775 5.338 2.878 2.297 1.120 3.834 38.382 Visualize Results
FDFlowNet [77] 6.640 3.450 32.625 5.339 3.305 2.562 1.528 3.843 38.744 Visualize Results
DeepDiscreteFlow [78] 5.728 2.623 31.042 5.347 2.478 1.590 0.959 3.072 35.819 Visualize Results
DDFlow [79] 7.401 3.409 39.936 5.357 3.092 2.430 1.548 4.198 44.188 Visualize Results
EPMNet [80] 6.134 3.141 30.540 5.431 2.987 2.152 1.306 4.188 34.780 Visualize Results
FGI [81] 6.607 3.101 35.158 5.432 2.970 2.131 1.152 3.986 39.985 Visualize Results
MirrorFlow [82] 6.071 3.186 29.567 5.433 2.801 1.943 0.930 3.183 38.544 Visualize Results
IHBPFlow [83] 6.100 2.891 32.242 5.501 2.660 1.772 1.081 3.464 37.368 Visualize Results
ZZZ [84] 8.103 3.604 44.772 5.529 3.212 2.925 1.641 4.832 48.068 Visualize Results
SPM-BP [85] 7.325 3.493 38.561 5.534 3.052 2.691 1.279 4.385 44.434 Visualize Results
AggregFlow [86] 7.329 3.696 36.929 5.538 3.435 2.918 1.241 4.296 44.858 Visualize Results
TF+OFM [87] 6.727 3.388 33.929 5.544 3.238 2.551 1.512 3.765 39.761 Visualize Results
PH-Flow [88] 7.423 3.795 36.960 5.550 3.675 2.716 1.119 4.827 44.926 Visualize Results
ResPWCR_ROB [89] 6.530 3.849 28.371 5.565 3.396 2.876 1.306 3.848 38.892 Visualize Results
SparseFlowFused [90] 7.189 3.286 38.977 5.567 3.098 2.159 1.275 3.963 44.319 Visualize Results
PMF [91] 7.630 3.607 40.435 5.584 3.171 2.770 1.266 4.414 46.985 Visualize Results
DMF_ROB [92] 7.475 3.575 39.245 5.650 3.419 2.441 1.165 4.044 47.063 Visualize Results
DeepFlow [93] 7.212 3.336 38.781 5.650 3.144 2.208 1.284 4.107 44.118 Visualize Results
M-1px [94] 8.229 3.680 45.306 5.654 3.267 2.997 1.718 5.023 48.284 Visualize Results
LocalLayering [95] 8.043 4.014 40.879 5.680 3.841 3.122 1.186 4.990 49.426 Visualize Results
CNet [96] 8.331 4.455 39.943 5.690 3.877 3.916 1.589 3.779 52.823 Visualize Results
MDP-Flow2 [97] 8.445 4.150 43.430 5.703 3.925 3.406 1.420 5.449 50.507 Visualize Results
PatchWMF-OF [98] 7.971 3.766 42.218 5.712 3.568 2.797 1.279 4.970 48.396 Visualize Results
BOOM+PF.XY [99] 7.600 3.731 39.137 5.766 3.599 2.798 1.488 4.709 44.902 Visualize Results
FAOP-Flow [100] 7.923 3.589 43.239 5.794 3.373 2.861 1.777 5.228 44.927 Visualize Results
BOOM+PF.XYT [101] 7.683 3.828 39.117 5.797 3.694 2.902 1.585 4.794 44.921 Visualize Results
GeoFlow [102] 8.459 3.908 45.553 5.805 3.550 2.899 1.398 4.667 52.653 Visualize Results
WLIF-Flow [103] 8.049 3.837 42.348 5.851 3.657 2.811 1.290 5.033 48.843 Visualize Results
S2D-Matching [104] 6.817 3.737 31.920 5.954 3.380 2.544 1.157 3.831 42.097 Visualize Results
SVFilterOh [105] 7.737 3.920 38.851 5.954 3.499 3.160 1.403 4.731 46.420 Visualize Results
ICALD [106] 7.554 3.784 38.270 6.065 3.812 2.626 1.336 4.939 44.705 Visualize Results
PatchBatch-CENT+SD [107] 6.783 3.507 33.498 6.080 3.408 2.103 0.725 3.064 45.858 Visualize Results
SparseFlow [108] 7.851 3.855 40.401 6.117 3.838 2.557 1.071 3.771 51.353 Visualize Results
DSPyNet+ft [109] 7.453 3.852 36.769 6.147 3.941 2.549 1.261 4.639 44.957 Visualize Results
ComponentFusion [110] 8.231 4.274 40.460 6.221 4.252 3.193 1.702 5.701 46.696 Visualize Results
ContFusion [111] 7.857 4.010 39.194 6.265 4.022 2.797 1.333 4.981 47.158 Visualize Results
AnyFlow [112] 7.933 3.994 40.027 6.284 3.997 2.756 2.279 5.391 42.122 Visualize Results
EPIflow [113] 8.506 4.070 44.676 6.286 3.897 2.777 1.246 3.761 56.015 Visualize Results
MLDP-OF [114] 8.287 4.165 41.905 6.345 4.127 2.996 1.312 5.122 50.540 Visualize Results
FlowNetS+ft+v [115] 7.218 3.752 35.445 6.439 3.635 2.292 1.358 4.609 42.571 Visualize Results
FlowNetC+ft+v [116] 7.883 4.132 38.426 6.466 3.952 2.860 1.369 5.049 47.005 Visualize Results
Classic+NLP [117] 8.291 4.287 40.925 6.520 4.265 2.984 1.208 5.090 51.162 Visualize Results
FC-2Layers-FF [118] 8.137 4.261 39.723 6.537 4.257 2.946 1.034 4.835 51.349 Visualize Results
EPPM [119] 8.377 4.286 41.695 6.556 4.024 3.323 1.834 4.955 49.083 Visualize Results
WOLF_ROB [120] 8.351 4.512 39.607 6.663 4.462 3.278 1.437 5.348 49.879 Visualize Results
SPyNet+ft [121] 8.360 4.512 39.687 6.694 4.368 3.290 1.395 5.534 49.707 Visualize Results
HAST [122] 9.185 5.116 42.360 6.698 4.956 4.345 1.614 5.856 54.781 Visualize Results
Grts-Flow-V2 [123] 8.764 4.718 41.737 6.748 4.276 3.902 2.105 6.536 47.153 Visualize Results
Channel-Flow [124] 8.835 4.754 42.064 6.757 4.566 3.657 1.292 5.349 54.648 Visualize Results
COF [125] 8.204 4.448 38.805 6.788 4.500 3.324 1.561 5.560 47.534 Visualize Results
DeformableFlow [126] 8.025 4.489 36.839 6.818 4.567 3.138 1.372 4.694 49.051 Visualize Results
DF [127] 9.188 4.758 45.309 6.821 4.594 3.675 1.708 6.093 53.782 Visualize Results
DF-Beta [128] 9.196 4.765 45.327 6.829 4.603 3.677 1.706 6.085 53.877 Visualize Results
H-1px [129] 10.148 4.454 56.545 6.832 4.306 3.637 2.173 6.606 58.262 Visualize Results
LDOF [130] 9.116 5.037 42.344 6.849 4.928 4.003 1.485 4.839 57.296 Visualize Results
IPOL_Brox [131] 9.198 4.869 44.479 6.856 4.682 3.789 1.679 6.106 53.956 Visualize Results
AGIF+OF [132] 8.514 4.355 42.390 6.873 4.284 2.869 1.190 5.010 53.305 Visualize Results
SPyNet [133] 8.431 4.549 40.042 6.921 4.511 3.179 1.442 5.545 50.042 Visualize Results
DSPyNet [134] 8.328 4.608 38.675 6.959 4.675 3.221 1.703 5.643 47.680 Visualize Results
TV-L1+EM [135] 8.916 4.712 43.157 7.021 4.788 3.401 1.456 5.188 54.932 Visualize Results
JOF [136] 8.818 4.599 43.175 7.049 4.617 3.131 1.170 4.196 57.923 Visualize Results
NLTGV-SC [137] 8.746 4.635 42.242 7.084 4.749 3.391 1.587 4.780 53.860 Visualize Results
RLOF_DENSE [138] 8.286 4.499 39.169 7.137 4.496 2.952 1.097 4.125 54.018 Visualize Results
OAR-Flow [139] 8.179 4.578 37.525 7.146 4.611 3.131 1.316 5.593 48.445 Visualize Results
Back2FutureFlow [140] 8.814 5.031 39.647 7.153 4.880 3.904 1.752 5.961 50.725 Visualize Results
RC-LSTM-1dir [141] 7.939 4.486 36.069 7.183 4.551 2.991 1.881 5.869 42.936 Visualize Results
Classic+NL [142] 9.153 4.814 44.509 7.215 4.822 3.427 1.113 4.496 60.291 Visualize Results
RC-LSTM-4dir [143] 7.905 4.466 35.921 7.223 4.557 2.950 1.849 5.854 42.848 Visualize Results
CPNFlow [144] 8.589 4.575 41.327 7.234 4.542 2.986 1.856 5.484 49.511 Visualize Results
PosetOptimization [145] 11.961 6.281 58.181 7.275 5.620 6.151 3.271 8.917 62.365 Visualize Results
IIOF-NLDP [146] 8.715 4.705 41.403 7.280 4.778 3.265 1.263 4.538 55.795 Visualize Results
PCA-Layers [147] 7.886 4.256 37.480 7.284 4.250 2.739 1.672 4.276 47.449 Visualize Results
SFL [148] 7.870 4.476 35.499 7.289 4.547 2.890 1.505 4.818 46.753 Visualize Results
Data-Flow [149] 8.868 4.601 43.675 7.294 4.698 3.021 1.794 5.294 52.635 Visualize Results
DF-Auto [150] 9.723 5.200 46.594 7.483 5.027 3.988 1.705 6.311 57.743 Visualize Results
WRTflow [151] 9.457 4.943 46.217 7.494 5.043 3.556 1.646 4.347 60.566 Visualize Results
HCOF+multi [152] 8.799 4.980 39.941 7.547 4.752 3.732 1.682 5.786 51.363 Visualize Results
ROF-NND [153] 9.286 5.130 43.129 7.561 5.350 3.796 1.221 4.700 60.367 Visualize Results
PCA-Flow [154] 8.652 4.725 40.671 7.602 4.818 3.196 1.961 4.524 51.844 Visualize Results
2bit-BM-tele [155] 10.978 5.793 53.220 7.710 5.603 4.814 2.338 6.959 63.521 Visualize Results
FlowNetProbOut [156] 8.302 4.610 38.405 7.876 4.788 2.907 1.726 6.067 46.290 Visualize Results
TVL1_ROB [157] 9.861 5.435 45.916 7.906 5.545 4.105 1.759 6.727 57.603 Visualize Results
Horn+Schunck [158] 9.610 5.419 43.734 7.950 5.658 3.976 1.882 5.335 58.274 Visualize Results
FlowNetADF [159] 8.532 4.768 39.222 8.002 4.979 3.060 1.846 6.304 47.067 Visualize Results
Classic+NL-fast [160] 10.088 5.659 46.145 8.010 5.738 4.160 1.092 4.666 67.801 Visualize Results
Classic++ [161] 9.959 5.410 47.000 8.072 5.554 3.750 1.403 5.098 64.135 Visualize Results
TV-L1 [162] 10.462 5.848 48.013 8.193 5.860 4.431 1.551 6.012 65.384 Visualize Results
OF_OCC_LD [163] 10.024 5.815 44.272 8.370 6.009 4.170 1.064 4.017 68.986 Visualize Results
UnFlow [164] 10.219 6.061 44.110 8.407 5.828 4.665 1.742 6.689 60.765 Visualize Results
DIS-Fast [165] 10.127 5.905 44.541 8.503 6.142 4.162 2.174 5.925 59.698 Visualize Results
Steered-L1 [166] 12.277 7.489 51.200 9.143 7.683 6.369 1.254 8.198 76.703 Visualize Results
AnisoHuber.L1 [167] 11.927 7.323 49.366 9.464 7.692 5.929 1.155 7.966 74.796 Visualize Results
H+S_ROB [168] 12.451 8.230 46.801 9.968 8.497 7.220 2.909 8.739 68.643 Visualize Results
SimpleFlow [169] 13.364 8.620 51.949 10.872 8.884 7.171 1.475 9.582 81.350 Visualize Results
AtrousFlow [170] 14.173 9.573 51.548 11.511 10.027 8.092 2.011 12.052 79.484 Visualize Results
WKSparse [171] 16.036 11.653 51.588 13.401 12.398 10.150 2.471 16.450 86.918 Visualize Results
BASELINE-Mean [172] 15.422 11.488 47.336 14.676 12.649 9.350 5.310 14.717 66.922 Visualize Results
AVG_FLOW_ROB [173] 18.799 14.422 54.251 16.903 15.516 12.457 4.566 19.946 86.008 Visualize Results
BASELINE-zero [174] 18.926 14.396 55.633 17.050 15.613 12.172 4.049 20.873 87.342 Visualize Results
C-2px [175] 44.062 37.047 101.160 37.366 36.580 37.093 34.342 35.847 112.424 Visualize Results

Metrics


EPE Endpoint error over the complete frames
EPE matched Endpoint error over regions that remain visible in adjacent frames
EPE unmatched Endpoint error over regions that are visible only in one of two adjacent frames
d0-10 Endpoint error over regions closer than 10 pixels to the nearest occlusion boundary
d10-60 Endpoint error over regions between 10 and 60 pixels apart from the nearest occlusion boundary
d60-140 Endpoint error over regions between 60 and 140 pixels apart from the nearest occlusion boundary
s0-10 Endpoint error over regions with velocities lower than 10 pixels per frame
s10-40 Endpoint error over regions with velocities between 10 and 40 pixels per frame
s40+ Endpoint error over regions with velocities larger than 40 pixels per frame
All error images are normalized to be in the range between 0 and 1.

References
[1]
GroundTruth
[2]
HD3-F
Anonymous.
[3]
FTD
Anonymous. None
[4]
HTC
Anonymous.
[5]
SelFlow
Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)
[6]
LiteFlowNet
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018.
[7]
SF_Net
Anonymous. TBA
[8]
LiteFlowNet2
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization
[9]
CompactFlow
Anonymous. We further improve the current flow model by: (1) Parameter sharing, allowing models to train faster (2) Revisit classic shiftable window idea, and adapt the idea to construct a better cost volume for flow estimation.
[10]
CompactFlow-woscv
Anonymous.
[11]
IRR-PWC
Junhwa Hur and Stefan Roth. Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019
[12]
FlowSR
Anonymous.
[13]
PWC-Net+KF2
W. Bao, Y. Xiao, L. Chen, and Z. Gao. KalmanFlow 2.0: Efficient Video Optical Flow Estimation via Context-Aware Kalman Filtering. submitted to TIP.
[14]
PWC-Net+KF
W. Bao, Y. Xiao, L. Chen, and Z. Gao. KalmanFlow 2.0: Efficient Video Optical Flow Estimation via Context-Aware Kalman Filtering. submitted to TIP.
[15]
AutoScaler+
Anonymous. AutoScaler+
[16]
PWC-Net
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018.
[17]
PWC-Net_ROB
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018.
[18]
DCFlow+KF2
W. Bao, Y. Xiao, L. Chen, and Z. Gao. KalmanFlow 2.0: Efficient Video Optical Flow Estimation via Context-Aware Kalman Filtering. submitted to TIP.
[19]
3Dflow
Improved
[20]
MFF
Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth and Jan Kautz: A Fusion Approach for Multi-Frame Optical Flow Estimation. IEEE Winter Conference on Applications of Computer Vision (WACV 2019)
[21]
DCFlow
Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
[22]
FlowFields+
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. T-PAMI.
[23]
PST
Anonymous. ACCV2018 submission #1195
[24]
ProbFlowFields
Anne S. Wannenwetsch, Margret Keuper, Stefan Roth. ProbFlow: Joint Optical Flow and Uncertainty Estimation. ICCV 2017.
[25]
CPM_AUG
Anonymous. CVPR 18 submission #1939
[26]
FlowFieldsCNN
C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
[27]
InterpoNet_ff
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[28]
SPM-BPv2
Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu. SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs. ICCV 2015. Improved version. Details coming soon.
[29]
TIMCflow
Anonymous.
[30]
S2F-IF
Yanchao Yang, Stefano Soatto. S2F: Slow-To-Fast Interpolator Flow. CVPR 2017.
[31]
DCFlow+KF
W. Bao, Y. Xiao, L. Chen, and Z. Gao. KalmanFlow 2.0: Efficient Video Optical Flow Estimation via Context-Aware Kalman Filtering. submitted to TIP.
[32]
2chPWC-Net-ft
An Embedding Scheme of Optical Flow Networks with Small Size for Fast and Small Objects, and Occlusions
[33]
FlowSAC_dcf
Anonymous.
[34]
PWC-Net+
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation. TPAMI, to appear. arXiv link https://arxiv.org/abs/1809.05571
[35]
FlowSAC_ff
Anonymous
[36]
FlowNet2-ft-sintel
Anonymous. CVPR Submission #900
[37]
FlowFields
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.
[38]
FullFlow+KF
W. Bao, Y. Xiao, L. Chen, and Z. Gao. Kalmanflow: Efficient Kalman Filtering for Video Optical Flow. ICIP 2018.
[39]
SBFlow
Fast Optical Flow Estimation Based on the Split Bregman Method. 2016 TCSVT.
[40]
Deep-EIP
Anonymous. End-2-End learning for energy based inpainting of optical flow. ACCV submission 572
[41]
NNF-Local
Anonymous. Large Displacement Optical Flow from Nearest Neighbor Fields
[42]
FullFlow
Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
[43]
FlowFields++
R. Schuster, C. Bailer, O. Wasenmüller, D. Stricker. FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018
[44]
SfM-PM
D. Maurer, N. Marniok, B. Goldluecke, A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation. ECCV 2018.
[45]
SegFlow153
d0=15,d1=3
[46]
InterpoNet_cpm
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[47]
PGM-C
Anonymous
[48]
ProFlow_ROB
D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018. (ROB setting)
[49]
ProFlow
D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.
[50]
CPM-Flow
Yinlin Hu, Rui Song, Yunsong Li. Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.
[51]
F3-MPLF
Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh: Filter Flow made Practical: Massively Parallel and Lock-Free -- CVPR 2017
[52]
ContinualFlow_ROB
Michal Neoral, Jan Šochman and Jiří Matas. Continual Occlusions and Optical Flow Estimation, ACCV 2018
[53]
SegFlow113
d0=11,d1=3
[54]
CPM2
Coarse-to-fine PatchMatch for dense correspondence, Yunsong Li, Yinlin Hu, Rui Song, Peng Rao, Yangli Wang, submitted to T-CSVT
[55]
InterpoNet_dm
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[56]
DiscreteFlow
M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015
[57]
MR-Flow
J. Wulff, L. Sevilla-Lara, M. J. Black: Optical Flow in Mostly Rigid Scenes. CVPR 2017.
[58]
MPIF
Anonymous. multi-level interpolation for optical flow estimation
[59]
FlowNet2
Anonymous. CVPR Submission #900
[60]
SegFlow33
d0=3, d1=3
[61]
RicFlow
Yinlin Hu, Yunsong Li, Rui Song. Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.
[62]
DiscreteFlow+OIR
D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.
[63]
InterpoNet_df
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[64]
Deep+R
B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015
[65]
EpicFlow
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.
[66]
SegFlow193
d0=19,d1=3
[67]
DeepFlow2
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.
[68]
PPM
Parametric PatchMatch, Fangjun Kuang, master thesis, 2017
[69]
PatchBatch+Inter
T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.
[70]
DIP-Flow
D. Maurer, M. Stoll, A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
[71]
SelFlow
Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)
[72]
AugFNG_ROB
Anonymous.
[73]
GlobalPatchCollider
Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016
[74]
FF++_ROB
Accurate matching and robust interpolation. KITTI parameters as given by the paper. Edge detector trained on KITTI + Sintel + BSDS500.
[75]
FALDOI
Roberto P.Palomares, Enric Meinhardt-Llopis, Coloma Ballester and Gloria Haro. FALDOI: A new minimization strategy for large displacement variational optical flow. To appear in JMIV
[76]
Devon
Anonymous. CVPR submission #1906
[77]
FDFlowNet
[78]
DeepDiscreteFlow
F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
[79]
DDFlow
Pengpeng Liu, Irwin King, Michael R. Lyu, Jia Xu.  DDFlow: Learning Optical Flow with Unlabeled Data Distillation.  Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Jan 2019
[80]
EPMNet
Anonymous. The high accuracy for both small and large motion estimation are mainly cause by two contributions: firstly, we present and implement an edge preserve patch match (EPM) layer that propagates self-similarity patterns in addition to offsets. The accuracy of optical flow prediction has greatly improved by this method. Secondly, we develop a course-to-fine network architecture to tackle large displacement estimation and introduce a residual flow method to solve small displacement estimation.
[81]
FGI
Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016
[82]
MirrorFlow
Junhwa Hur and Stefan Roth. MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017
[83]
IHBPFlow
Anonymous.
[84]
ZZZ
Anonymous.
[85]
SPM-BP
Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu. SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs. ICCV 2015
[86]
AggregFlow
D. Fortun, P. Bouthemy and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. CVIU 2016
[87]
TF+OFM
R. Kennedy and C.J. Taylor. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.
[88]
PH-Flow
Jiaolong Yang and Hongdong Li. Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015
[89]
ResPWCR_ROB
Anonymous.
[90]
SparseFlowFused
Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
[91]
PMF
J. Lu, Y. Li, H. Yang, D. Min, W. Eng, M. N. Do. PatchMatch Filter: Edge-Aware Filtering Meets Randomized Search for Visual Correspondence. TPAMI 2016
[92]
DMF_ROB
Baseline submission for ROB flow challenge: Deepflow with Deepmatching as prior
[93]
DeepFlow
P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.
[94]
M-1px
Anonymous.
[95]
LocalLayering
D. Sun, C. Liu, and H. Pfister. Local Layering for Joint Motion Estimation and Occlusion Detection. CVPR 2014.
[96]
CNet
Anonymous.
[97]
MDP-Flow2
L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012.
[98]
PatchWMF-OF
Zhigang Tu, Coert Van Gemeren and Remco C. Veltkamp. Improved Color Patch Similarity Measure Based Weighted Median Filter. ACCV2014
[99]
BOOM+PF.XY
Fast optical flow method (0.54s single core i7 @3.1GHz) that employs the permeability filter to interpolate NNF correspondences. The NNF is an improved version of CPM that uses a new binary descriptor termed BOOM instead of SiftFlow in order to efficiently compute matching costs. Publication: Towards Edge-Aware Spatio-Temporal Filtering in Real-Time. M. Schaffner, F. Scheidegger, L. Cavigelli, H. Kaeslin, L. Benini and A. Smolic. Accepted for publication in Trans. on Image Processing (TIP), 2017. DOI: 10.1109/TIP.2017.2757259
[100]
FAOP-Flow
Anonymous.
[101]
BOOM+PF.XYT
Fast optical flow method (0.59s single core i7 @3.1GHz) that employs the permeability filter to interpolate NNF correspondences. The NNF is an improved version of CPM that uses a new binary descriptor termed BOOM instead of SiftFlow in order to efficiently compute matching costs. Publication: Towards Edge-Aware Spatio-Temporal Filtering in Real-Time. M. Schaffner, F. Scheidegger, L. Cavigelli, H. Kaeslin, L. Benini and A. Smolic. Accepted for publication in Trans. on Image Processing (TIP), 2017. DOI: 10.1109/TIP.2017.2757259
[102]
GeoFlow
Anonymous. Submission to Electrocnic Letters in 2018, we propose a simple but novel algorithm to achieve global belief propagation called geodesic-based Probability Propagation for optical flow estimation.
[103]
WLIF-Flow
Z. Tu, R. Poppe, and R. C. Veltkamp. Weighted local intensity fusion method for variational optical flow estimation.Pattern Recognition,2015
[104]
S2D-Matching
M. Leordeanu, A. Zanfir, C. Sminchisescu. Locally Affine Sparse-to-Dense Matching for Motion and Occlusion estimation. ICCV 2013
[105]
SVFilterOh
Mohamed A. Helala, Faisal Z. Qureshi, Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering, CRV 2017
[106]
ICALD
M. Stoll, D. Maurer, S. Volz and A. Bruhn. Illumination-Aware Large Displacement Optical Flow. EMMCVPR 2017.
[107]
PatchBatch-CENT+SD
Anonymous.
[108]
SparseFlow
Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
[109]
DSPyNet+ft
Zefeng Sun and Hanli Wang, Deeper Spatial Pyramid Network with Refined Up-Sampling for Optical Flow Estimation, 2018 Pacific Rim Conference on Multimedia (PCM'18), LNCS 11164, pp. 492-501, 2018.
[110]
ComponentFusion
Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.
[111]
ContFusion
M. Stoll, D. Maurer, and A. Bruhn. Variational Large Displacement Optical Flow without Feature Matches. EMMCVPR 2017.
[112]
AnyFlow
Anonymous. PAMI pending review
[113]
EPIflow
Deep Epipolar Flow
[114]
MLDP-OF
M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia, and D. Puig. Illumination-robust optical flow approach using local directional pattern. Accepted in IEEE TCSVT 2014.
[115]
FlowNetS+ft+v
Anonymous. ICCV submission 235
[116]
FlowNetC+ft+v
Anonymous. ICCV sumbmission 235
[117]
Classic+NLP
D. Sun, S. Roth, and M. Black. A Quantitative Analysis of Current Practices in Optical Flow Estimation and The Principles behind Them. International Journal of Computer Vision (IJCV), 106(2):115-137, 2014.
[118]
FC-2Layers-FF
D. Sun, J. Wulff, E. Sudderth, H. Pfister, M.J. Black. A fully connected layered model of foreground and background flow. CVPR 2013
[119]
EPPM
L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.
[120]
WOLF_ROB
[121]
SPyNet+ft
Ranjan, Anurag and Black, Michael J., Optical Flow Estimation using a Spatial Pyramid Network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017
[122]
HAST
Yinlin Hu, Rui Song, Yunsong Li, Peng Rao, Yangli Wang. Highly Accurate Optical Flow Estimation on Superpixel Tree. Image and Vision Computing (IVC), 2016
[123]
Grts-Flow-V2
En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT
[124]
Channel-Flow
L. Sevilla-Lara, D. Sun, E. Learned-Miller, and M. Black. Optical Flow Estimation with Channel Constancy. ECCV, pages 423-438, 2014.
[125]
COF
H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[126]
DeformableFlow
Anonymous.
[127]
DF
Nelson Monzón, Agustín Salgado and Javier Sánchez. Regularization Strategies for Discontinuity-Preserving Optical Flow Methods. IEEE Transactions on Image Processing, Vol 25(4), pp. 1580 - 1591, 2016
[128]
DF-Beta
Nelson Monzón, Agustín Salgado and Javier Sánchez. Regularization Strategies for Discontinuity-Preserving Optical Flow Methods. IEEE Transactions on Image Processing, Vol 25(4), pp. 1580 - 1591, 2016
[129]
H-1px
Anonymous.
[130]
LDOF
T. Brox, J. Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3): 500-513, 2011.
[131]
IPOL_Brox
J. Sánchez, N. Monzón, and A. Salgado, Robust Optical Flow Estimation, Image Processing On Line (IPOL), 3 (2013), pp. 252–270.
[132]
AGIF+OF
Anonymous. Signal Processing 2015
[133]
SPyNet
Ranjan, Anurag and Black, Michael J., Optical Flow Estimation using a Spatial Pyramid Network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017
[134]
DSPyNet
Zefeng Sun and Hanli Wang, Deeper Spatial Pyramid Network with Refined Up-Sampling for Optical Flow Estimation, 2018 Pacific Rim Conference on Multimedia (PCM'18), LNCS 11164, pp. 492-501, 2018.
[135]
TV-L1+EM
V. Lazcano. EXHAUSTIVE MATCHING EMPIRICAL STUDY FOR IMPROVING THE MOTION FIELD ESTIMATION
[136]
JOF
Anonymous.
[137]
NLTGV-SC
R. Ranftl, K. Bredies, T. Pock. Non-Local Total Generalized Variation for Optical Flow Estimation, ECCV 2014
[138]
RLOF_DENSE
Jonas Geistert, Tobias Senst and Thomas Sikora, Robust Local Optical Flow: Dense Motion Vector Field Interpolation, PCS 2016
[139]
OAR-Flow
D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.
[140]
Back2FutureFlow
J. Janai, F. Güney, A. Ranjan, M. Black and A. Geiger. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. ECCV, 2018.
[141]
RC-LSTM-1dir
Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
[142]
Classic+NL
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[143]
RC-LSTM-4dir
Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
[144]
CPNFlow
Anonymous. ECCV 2018 submission, Paper ID: 448
[145]
PosetOptimization
Anonymous.
[146]
IIOF-NLDP
Dinh-Hoan Trinh, Walter Blondel, and Christian Daul. A General Form of Illumination-Invariant Descriptors in Variational Optical Flow Estimation. ICIP 2017.
[147]
PCA-Layers
J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
[148]
SFL
Jingchun Cheng, Yi-Hsuan, Tsai, Shengjin Wang, Ming-Hsuan Yang. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow. ICCV 2017
[149]
Data-Flow
Anonymous. CSAD data cost + second order smoothness
[150]
DF-Auto
Nelson Monzón, Agustín Salgado and Javier Sánchez. Regularization Strategies for Discontinuity-Preserving Optical Flow Methods. IEEE Transactions on Image Processing, Vol 25(4), pp. 1580 - 1591, 2016
[151]
WRTflow
We proposed a weighted regularization transform to reduce the influence of illumination variations of optical flow estimation. We submit it to TCSVT.
[152]
HCOF+multi
R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.
[153]
ROF-NND
Sharib Ali, Christian Daul, Ernest Galbrun, Walter Blondel, Illumination invariant optical flow using neighborhood descriptors, Computer Vision and Image Understanding, Available online 17 December 2015, ISSN 1077-3142.
[154]
PCA-Flow
J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
[155]
2bit-BM-tele
Rui Xu & David Taubman, Robust Dense Block-Based Motion Estimation Using a Two-Bit Transform on a Laplacian Pyramid, ICIP 2013 +telescopic search
[156]
FlowNetProbOut
Lightweight Probabilistic Deep Networks
[157]
TVL1_ROB
Baseline submission for the robust vision flow challenge: TV-L1 Optical Flow Estimation
[158]
Horn+Schunck
A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.
[159]
FlowNetADF
Lightweight Probabilistic Deep Networks
[160]
Classic+NL-fast
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[161]
Classic++
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[162]
TV-L1
Anonymous. It is a well-known model that uses TV-L1 (coupled) as the regularization term and an L1 data term.
[163]
OF_OCC_LD
V. Lazcano, L. Garrido, C. Ballester. Jointly Optical flow and Occlusion Estimation for images with Large Displacements.
[164]
UnFlow
Anonymous.
[165]
DIS-Fast
T. Kroeger, R. Timofte, D. Dai, L. Van Gool, Fast Optical Flow using Dense Inverse Search. ECCV 2016. Run-time: 0.023 s (20ms preprocessing, 3ms flow computation). Using operating point 2 of the paper.
[166]
Steered-L1
Anonymous.
[167]
AnisoHuber.L1
M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009.
[168]
H+S_ROB
Baseline submission for the ROB flow challenge: Horn-Schunck Optical Flow with a Multi-Scale Strategy
[169]
SimpleFlow
M. W. Tao, J. Bai, P. Kohli, and S. Paris. "SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm". Computer Graphics Forum (Eurographics 2012), 2012. (OpenCV 2.4.5 Implementation, parameters provided by OpenCV sample)
[170]
AtrousFlow
Anonymous. Real-time dense optical flow using CUDA
[171]
WKSparse
[172]
BASELINE-Mean
[173]
AVG_FLOW_ROB
Average flow field of ROB2018 training set. No image information used!
[174]
BASELINE-zero
[175]
C-2px
Anonymous.