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
MR-Flow [2] 2.527 0.954 15.365 2.866 0.710 0.420 0.446 1.715 14.826 Visualize Results
FlowFields+ [3] 3.102 0.820 21.718 2.340 0.616 0.373 0.593 1.865 18.549 Visualize Results
PGM-C [4] 3.234 0.929 22.045 2.724 0.659 0.424 0.567 1.999 19.467 Visualize Results
CPM2 [5] 3.253 0.980 21.812 2.663 0.751 0.416 0.615 1.954 19.503 Visualize Results
DiscreteFlow+OIR [6] 3.331 0.942 22.817 2.794 0.712 0.442 0.583 1.948 20.318 Visualize Results
FFlow [7] 3.469 1.139 22.500 3.107 0.946 0.525 0.469 1.896 22.185 Visualize Results
S2F-IF [8] 3.500 0.988 23.986 2.629 0.816 0.533 0.524 1.976 21.960 Visualize Results
SPM-BPv2 [9] 3.515 1.020 23.865 2.603 0.841 0.521 0.474 1.773 22.830 Visualize Results
DCFlow [10] 3.537 1.103 23.394 2.897 0.868 0.632 0.703 2.015 21.296 Visualize Results
RicFlow [11] 3.550 1.264 22.220 3.248 1.023 0.576 0.601 2.203 21.465 Visualize Results
CPM-Flow [12] 3.557 1.189 22.889 3.032 0.973 0.613 0.592 2.064 21.900 Visualize Results
DiscreteFlow [13] 3.567 1.108 23.626 3.398 0.799 0.446 0.703 2.277 20.906 Visualize Results
MirrorFlow [14] 3.568 1.612 19.537 4.470 1.445 0.524 0.619 2.072 21.843 Visualize Results
FullFlow [15] 3.601 1.296 22.424 2.944 1.023 0.732 0.933 2.055 20.612 Visualize Results
CPM_AUG [16] 3.609 1.135 23.804 2.945 0.935 0.516 0.615 2.099 22.137 Visualize Results
ProbFlowFields [17] 3.615 1.049 24.554 2.689 0.882 0.594 0.533 2.109 22.564 Visualize Results
PatchBatch+Inter [18] 3.624 1.324 22.397 3.076 1.115 0.673 0.589 1.824 23.062 Visualize Results
FlowFields [19] 3.748 1.056 25.700 2.784 0.878 0.570 0.546 2.110 23.602 Visualize Results
RegionalFF [20] 3.761 1.164 24.957 3.002 1.000 0.651 0.540 2.086 23.813 Visualize Results
FlowFieldsCNN [21] 3.778 0.996 26.469 2.604 0.796 0.631 0.648 2.017 23.582 Visualize Results
InterpoNet_df [22] 3.862 1.193 25.632 3.613 0.885 0.508 0.581 2.253 24.038 Visualize Results
DeepDiscreteFlow [23] 3.863 1.296 24.820 3.077 0.975 0.803 0.794 2.024 23.575 Visualize Results
InterpoNet_ff [24] 3.952 1.232 26.121 3.600 0.946 0.573 0.571 2.228 24.900 Visualize Results
FlowNet2 [25] 3.959 1.468 24.294 3.089 1.319 0.920 0.643 1.898 25.422 Visualize Results
InterpoNet_dm [26] 3.973 1.412 24.852 4.015 1.032 0.636 0.706 2.142 24.619 Visualize Results
InterpoNet_cpm [27] 4.086 1.371 26.222 3.992 1.064 0.569 0.637 2.325 25.466 Visualize Results
EpicFlow [28] 4.115 1.360 26.595 3.660 1.079 0.599 0.712 2.117 25.859 Visualize Results
GlobalPatchCollider [29] 4.134 1.432 26.179 3.914 1.268 0.554 0.613 2.232 26.222 Visualize Results
FlowNet2-ft-sintel [30] 4.157 1.557 25.403 3.272 1.461 0.856 0.597 1.890 27.347 Visualize Results
PH-Flow [31] 4.388 1.714 26.202 3.612 1.713 0.834 0.590 2.430 27.997 Visualize Results
FGI [32] 4.664 1.540 30.110 3.771 1.336 0.850 0.669 2.310 30.185 Visualize Results
AggregFlow [33] 4.754 1.694 29.685 3.705 1.603 0.981 0.650 2.251 31.184 Visualize Results
F3-MPLF [34] 4.771 2.063 26.863 3.458 1.809 1.244 0.678 1.884 32.091 Visualize Results
DeepFlow2 [35] 4.891 1.403 33.317 3.714 1.119 0.626 0.800 2.210 31.690 Visualize Results
TF+OFM [36] 4.917 1.874 29.735 3.676 1.689 1.309 0.839 2.349 31.391 Visualize Results
FALDOI [37] 4.927 1.542 32.535 3.307 1.318 0.885 1.047 2.647 29.719 Visualize Results
Deep+R [38] 5.041 1.481 34.047 3.710 1.102 0.722 0.929 2.333 31.999 Visualize Results
SPM-BP [39] 5.202 1.815 32.839 4.008 1.704 1.179 0.643 2.576 34.214 Visualize Results
STEAFlow-XY [40] 5.204 1.696 33.826 3.512 1.540 1.021 0.927 2.921 31.976 Visualize Results
SparseFlowFused [41] 5.257 1.627 34.834 4.211 1.397 0.729 0.880 2.567 33.489 Visualize Results
STEAFlow-XYT [42] 5.311 1.820 33.809 3.552 1.661 1.180 1.049 3.031 32.008 Visualize Results
DeepFlow [43] 5.377 1.771 34.751 4.519 1.534 0.837 0.960 2.730 33.701 Visualize Results
PMF [44] 5.378 1.858 34.102 3.877 1.835 1.235 0.628 2.428 36.128 Visualize Results
NNF-Local [45] 5.386 1.397 37.896 2.722 1.341 1.004 0.683 2.245 36.342 Visualize Results
PWC-DC-ft [46] 5.447 2.227 31.733 4.912 2.166 1.282 0.988 2.792 34.032 Visualize Results
SVFilterOh [47] 5.540 2.043 34.067 3.875 1.865 1.625 0.778 2.713 36.033 Visualize Results
PatchWMF-OF [48] 5.550 1.781 36.257 3.339 1.843 1.277 0.581 2.612 37.319 Visualize Results
PCA-Layers [49] 5.730 2.455 32.468 5.447 2.337 1.415 1.129 3.051 35.079 Visualize Results
WLIF-Flow [50] 5.734 1.759 38.125 3.242 1.818 1.296 0.597 2.512 39.036 Visualize Results
AGIF+OF [51] 5.766 1.695 38.936 3.034 1.709 1.329 0.613 2.554 39.121 Visualize Results
PatchBatch-CENT+SD [52] 5.789 2.743 30.599 5.232 2.756 1.492 0.492 1.801 41.746 Visualize Results
LocalLayering [53] 5.820 2.143 35.784 3.817 2.342 1.399 0.580 2.461 39.976 Visualize Results
MDP-Flow2 [54] 5.837 1.869 38.158 3.210 1.913 1.441 0.640 2.603 39.459 Visualize Results
ICALD [55] 6.002 2.376 35.550 4.835 2.636 1.213 0.758 3.459 38.169 Visualize Results
ComponentFusion [56] 6.065 2.033 38.912 4.114 2.063 1.213 0.910 2.996 39.074 Visualize Results
AnyFlow [57] 6.066 2.412 35.852 5.211 2.432 1.215 1.429 3.665 34.900 Visualize Results
AutoScaler+ [58] 6.076 2.569 34.656 4.610 2.195 1.863 1.298 3.737 35.431 Visualize Results
FlowNetC+ft+v [59] 6.081 2.576 34.620 5.079 2.371 1.480 0.764 2.686 40.676 Visualize Results
FlowNetS+ft+v [60] 6.158 2.800 33.491 5.535 2.687 1.563 0.766 2.938 40.686 Visualize Results
SparseFlow [61] 6.197 2.357 37.460 4.642 2.273 1.392 0.681 2.533 42.422 Visualize Results
OAR-Flow [62] 6.227 2.760 34.455 5.639 3.096 1.375 0.648 3.132 41.378 Visualize Results
Grts-Flow-V2 [63] 6.415 2.664 36.995 5.177 2.482 1.773 1.310 3.983 37.635 Visualize Results
EPPM [64] 6.494 2.675 37.632 4.997 2.422 1.948 1.402 3.446 39.152 Visualize Results
COF [65] 6.496 2.849 36.216 5.679 3.176 1.450 0.859 3.656 41.334 Visualize Results
SPyNet+ft [66] 6.640 3.013 36.190 5.501 3.122 1.719 0.832 3.343 43.442 Visualize Results
SPyNet [67] 6.689 3.020 36.596 5.770 3.289 1.562 0.911 3.455 43.207 Visualize Results
RC-LSTM-4dir [68] 6.694 3.419 33.345 5.930 3.513 2.162 1.375 3.826 39.961 Visualize Results
RC-LSTM-1dir [69] 6.717 3.445 33.352 5.916 3.527 2.209 1.410 3.820 40.001 Visualize Results
HCOF+multi [70] 6.717 3.244 35.046 5.724 3.175 2.290 1.223 4.158 40.158 Visualize Results
Classic+NLP [71] 6.731 2.949 37.545 5.573 3.291 1.648 0.638 3.296 45.290 Visualize Results
FC-2Layers-FF [72] 6.781 3.053 37.144 5.841 3.390 1.688 0.580 3.308 45.962 Visualize Results
HAST [73] 6.802 3.081 37.112 4.641 3.111 2.245 0.683 3.022 46.330 Visualize Results
PCA-Flow [74] 6.828 3.014 37.939 6.444 3.038 1.655 1.363 3.484 42.048 Visualize Results
Channel-Flow [75] 7.023 3.086 39.084 5.411 3.236 1.918 0.624 2.791 49.021 Visualize Results
IPOL_Brox [76] 7.283 3.150 40.931 5.705 3.347 1.831 1.090 3.647 46.796 Visualize Results
MLDP-OF [77] 7.297 3.260 40.183 5.581 3.304 2.007 0.600 2.916 51.146 Visualize Results
DF-Beta [78] 7.391 3.153 41.890 5.492 3.337 1.895 1.087 3.597 47.836 Visualize Results
DF [79] 7.406 3.164 41.936 5.504 3.345 1.908 1.091 3.594 47.949 Visualize Results
LDOF [80] 7.563 3.432 41.170 5.353 3.284 2.454 0.936 2.908 51.696 Visualize Results
NLTGV-SC [81] 7.680 3.565 41.168 6.132 3.801 2.170 0.996 3.557 50.808 Visualize Results
Classic+NL [82] 7.961 3.770 42.079 6.191 3.911 2.509 0.573 2.694 57.374 Visualize Results
Data-Flow [83] 7.972 3.494 44.527 6.341 3.665 1.921 1.258 3.970 51.049 Visualize Results
RLOF_DENSE [84] 7.977 3.887 41.315 6.689 4.005 2.415 0.739 3.080 55.766 Visualize Results
ROF-NND [85] 8.061 3.944 41.603 6.365 4.145 2.756 0.717 3.301 56.051 Visualize Results
DF-Auto [86] 8.480 3.945 45.399 6.445 4.149 2.537 1.057 3.732 56.780 Visualize Results
Classic++ [87] 8.721 4.259 45.047 6.983 4.494 2.753 0.902 3.295 60.645 Visualize Results
Horn+Schunck [88] 8.739 4.525 43.032 7.542 5.045 2.891 1.141 3.860 58.243 Visualize Results
Classic+NL-fast [89] 9.129 4.725 44.956 7.157 4.974 3.331 0.558 2.812 66.935 Visualize Results
2bit-BM-tele [90] 9.274 4.219 50.491 6.233 4.119 3.323 1.390 4.564 59.833 Visualize Results
DIS-Fast [91] 9.353 5.165 43.503 8.317 5.677 3.212 1.906 4.899 57.071 Visualize Results
TV-L1 [92] 9.471 4.875 46.845 7.630 5.151 3.337 0.976 3.856 65.165 Visualize Results
PosetOptimization [93] 9.874 3.639 60.633 5.005 3.366 3.207 1.981 6.486 57.189 Visualize Results
Steered-L1 [94] 10.864 6.018 50.244 7.976 6.187 4.832 0.811 6.049 72.292 Visualize Results
SimpleFlow [95] 12.617 7.848 51.435 10.693 8.422 6.170 0.711 8.411 81.786 Visualize Results
AnisoHuber.L1 [96] 12.642 7.983 50.472 10.457 8.675 6.320 0.753 9.976 77.835 Visualize Results
AtrousFlow [97] 14.200 9.584 51.758 11.964 10.338 7.926 1.702 12.440 80.185 Visualize Results
BASELINE-Mean [98] 15.422 11.488 47.336 14.676 12.649 9.350 5.310 14.717 66.922 Visualize Results
WKSparse [99] 15.765 11.414 51.045 13.469 11.864 10.609 2.756 15.781 81.907 Visualize Results
S2D-Matching [100] 18.485 13.372 59.935 13.676 12.747 12.149 9.472 15.466 70.185 Visualize Results
BASELINE-zero [101] 18.926 14.396 55.633 17.050 15.613 12.172 4.049 20.873 87.342 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]
MR-Flow
J. Wulff, L. Sevilla-Lara, M. J. Black: Optical Flow in Mostly Rigid Scenes. CVPR 2017.
[3]
FlowFields+
C. Bailer, B. Taetz and D. Stricker. Optical Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. Submitted to IEEE T-PAMI.
[4]
PGM-C
Anonymous
[5]
CPM2
Coarse-to-fine PatchMatch for dense correspondence, Yunsong Li, Yinlin Hu, Rui Song, Peng Rao, Yangli Wang, submitted to T-CSVT
[6]
DiscreteFlow+OIR
Anonymous. Anonymous BMVC 2017 submission #662
[7]
FFlow
Anonymous. FFlow
[8]
S2F-IF
Yanchao Yang, Stefano Soatto. S2F: Slow-To-Fast Interpolator Flow. CVPR 2017.
[9]
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.
[10]
DCFlow
Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
[11]
RicFlow
Yinlin Hu, Yunsong Li, Rui Song. Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.
[12]
CPM-Flow
Yinlin Hu, Rui Song, Yunsong Li. Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.
[13]
DiscreteFlow
M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015
[14]
MirrorFlow
Anonymous. ICCV 2017 submission #230
[15]
FullFlow
Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
[16]
CPM_AUG
NIPS submission #487
[17]
ProbFlowFields
Anonymous. ICCV 2017 submission, # 619
[18]
PatchBatch+Inter
T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.
[19]
FlowFields
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.
[20]
RegionalFF
Anonymous. ICCV 2017 submission, #1533
[21]
FlowFieldsCNN
C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
[22]
InterpoNet_df
Anonymous. CVPR 2017 submission, #1887
[23]
DeepDiscreteFlow
F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
[24]
InterpoNet_ff
Anonymous. CVPR 2017 submission, #1887
[25]
FlowNet2
Anonymous. CVPR Submission #900
[26]
InterpoNet_dm
Anonymous. CVPR 2017 submission, #1887
[27]
InterpoNet_cpm
Anonymous. CVPR 2017 submission, #1887
[28]
EpicFlow
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.
[29]
GlobalPatchCollider
Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016
[30]
FlowNet2-ft-sintel
Anonymous. CVPR Submission #900
[31]
PH-Flow
Jiaolong Yang and Hongdong Li. Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015
[32]
FGI
Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016
[33]
AggregFlow
D. Fortun, P. Bouthemy and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. CVIU 2016
[34]
F3-MPLF
Anonymous. CVPR Submission #1311
[35]
DeepFlow2
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.
[36]
TF+OFM
R. Kennedy and C.J. Taylor. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.
[37]
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
[38]
Deep+R
B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015
[39]
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
[40]
STEAFlow-XY
Anonymous. Submitted to TIP.
[41]
SparseFlowFused
Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
[42]
STEAFlow-XYT
Anonymous. Submitted to TIP.
[43]
DeepFlow
P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.
[44]
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
[45]
NNF-Local
Anonymous. Large Displacement Optical Flow from Nearest Neighbor Fields
[46]
PWC-DC-ft
Anonymous. ICCV 2017 submission #645
[47]
SVFilterOh
Mohamed A. Helala, Faisal Z. Qureshi, Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering, CRV 2017
[48]
PatchWMF-OF
Zhigang Tu, Coert Van Gemeren and Remco C. Veltkamp. Improved Color Patch Similarity Measure Based Weighted Median Filter. ACCV2014
[49]
PCA-Layers
J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
[50]
WLIF-Flow
Z. Tu, R. Poppe, and R. C. Veltkamp. Weighted local intensity fusion method for variational optical flow estimation.Pattern Recognition,2015
[51]
AGIF+OF
Anonymous. Signal Processing 2015
[52]
PatchBatch-CENT+SD
Anonymous.
[53]
LocalLayering
D. Sun, C. Liu, and H. Pfister. Local Layering for Joint Motion Estimation and Occlusion Detection. CVPR 2014.
[54]
MDP-Flow2
L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012.
[55]
ICALD
Anonymous. BMVC 2017 Submission #390.
[56]
ComponentFusion
Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.
[57]
AnyFlow
Anonymous. PAMI pending review
[58]
AutoScaler+
Anonymous. AutoScaler+
[59]
FlowNetC+ft+v
Anonymous. ICCV sumbmission 235
[60]
FlowNetS+ft+v
Anonymous. ICCV submission 235
[61]
SparseFlow
Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
[62]
OAR-Flow
D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.
[63]
Grts-Flow-V2
En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT
[64]
EPPM
L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.
[65]
COF
H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[66]
SPyNet+ft
Anonymous.
[67]
SPyNet
Anonymous.
[68]
RC-LSTM-4dir
Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
[69]
RC-LSTM-1dir
Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
[70]
HCOF+multi
R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.
[71]
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.
[72]
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
[73]
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
[74]
PCA-Flow
J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
[75]
Channel-Flow
L. Sevilla-Lara, D. Sun, E. Learned-Miller, and M. Black. Optical Flow Estimation with Channel Constancy. ECCV, pages 423-438, 2014.
[76]
IPOL_Brox
J. Sánchez, N. Monzón, and A. Salgado, Robust Optical Flow Estimation, Image Processing On Line (IPOL), 3 (2013), pp. 252–270.
[77]
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.
[78]
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
[79]
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
[80]
LDOF
T. Brox, J. Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3): 500-513, 2011.
[81]
NLTGV-SC
R. Ranftl, K. Bredies, T. Pock. Non-Local Total Generalized Variation for Optical Flow Estimation, ECCV 2014
[82]
Classic+NL
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[83]
Data-Flow
Anonymous. CSAD data cost + second order smoothness
[84]
RLOF_DENSE
Jonas Geistert, Tobias Senst and Thomas Sikora, Robust Local Optical Flow: Dense Motion Vector Field Interpolation, PCS 2016
[85]
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.
[86]
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
[87]
Classic++
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[88]
Horn+Schunck
A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.
[89]
Classic+NL-fast
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[90]
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
[91]
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.
[92]
TV-L1
Anonymous. It is a well-known model that uses TV-L1 (coupled) as the regularization term and an L1 data term.
[93]
PosetOptimization
Anonymous.
[94]
Steered-L1
Anonymous.
[95]
SimpleFlow
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M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009.
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AtrousFlow
Anonymous. Real-time dense optical flow using CUDA
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WKSparse
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M. Leordeanu, A. Zanfir, C. Sminchisescu. Locally Affine Sparse-to-Dense Matching for Motion and Occlusion estimation. ICCV 2013
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BASELINE-zero