MPI Sintel Dataset

Visual Results by Method

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


Return to numerical results table

Final
Clean
Flow
Error
Input
EPE all
EPE matched
EPE unmatched
d0-10
d10-60
d60-140
s0-10
s10-40
s40+

  • Metric_01 Perturbed Market 3, EPE all = 1.087
  • Metric_01 Perturbed Shaman 1, EPE all = 1.478
  • Metric_01 Ambush 1, EPE all = 22.390
  • Metric_01 Ambush 3, EPE all = 9.635
  • Metric_01 Bamboo 3, EPE all = 1.254
  • Metric_01 Cave 3, EPE all = 9.814
  • Metric_01 Market 1, EPE all = 4.406
  • Metric_01 Market 4, EPE all = 41.792
  • Metric_01 Mountain 2, EPE all = 0.539
  • Metric_01 Temple 1, EPE all = 1.388
  • Metric_01 Tiger, EPE all = 1.036
  • Metric_01 Wall, EPE all = 5.745

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]
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
[2]
A-A
Anonymous.
[3]
ACAFlow
Anonymous.
[4]
ACAFlow
Anonymous.
[5]
ACAFlow
Anonymous.
[6]
ADF-Scaleflow
Anonymous. Scaleflow trained by ADF58
[7]
ADLAB-PRFlow
Anonymous.
[8]
ADW
Anonymous.
[9]
ADW-Net
Anonymous. ADW-Net，20201024 submit
[10]
AGF-Flow2
Anonymous. AGF-Flow2
[11]
AGF-Flow3
Anonymous. AGF-Flow3
[12]
AGFlow
Ao Luo, Fan Yang, Kunming Luo, Xin Li, Haoqiang Fan, Shuaicheng Liu. Learning Optical Flow with Adaptive Graph Reasoning, In AAAI, 2022.
[13]
AGIF+OF
Anonymous. Signal Processing 2015
[14]
AGM-FlowNet
Anonymous.
[15]
AL-OF-r0.05
Shuai Yuan, Xian Sun, Hannah Kim, Shuzhi Yu, Carlo Tomasi. Optical Flow Training Under Limited Label Budget via Active Learning. (ECCV-2022)
[16]
AL-OF-r0.1
Shuai Yuan, Xian Sun, Hannah Kim, Shuzhi Yu, Carlo Tomasi. Optical Flow Training Under Limited Label Budget via Active Learning. (ECCV-2022)
[17]
AL-OF-r0.2
Shuai Yuan, Xian Sun, Hannah Kim, Shuzhi Yu, Carlo Tomasi. Optical Flow Training Under Limited Label Budget via Active Learning. (ECCV-2022)
[18]
AMFFlow
Anonymous.
[19]
AMFFlow_3f
[20]
AMFlow
Anonymous. Efficient Optical Flow Estimation via Attentional Cost Volume and Matching Initialization
[21]
AOD
[22]
APCAFlow
Anonymous.
[23]
ARFlow
Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying Tai, et al. Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation. CVPR 2020.
[24]
ARFlow
Anonymous.
[25]
ARFlow+LCT-Flow
ARFlow+LCT-Flow
[26]
ARFlow-base
Anonymous. ARFlow-base
[27]
ARFlow-mv
Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying Tai, et al. Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation. CVPR 2020.
[28]
ARFlow-mv-ft
Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying Tai, et al. Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation. CVPR 2020.
[29]
AVG_FLOW_ROB
Average flow field of ROB2018 training set. No image information used!
[30]
AggregFlow
D. Fortun, P. Bouthemy and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. CVIU 2016
[31]
AllTracker
Anonymous.
[32]
AnisoHuber.L1
M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009.
[33]
AnyFlow
Anonymous. PAMI pending review
[34]
AnyFlow+GMA
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation. CVPR, 2023.
[35]
AtrousFlow
Anonymous. Real-time dense optical flow using CUDA
[36]
AugFNG_ROB
Anonymous.
[37]
AutoScaler+
Anonymous. AutoScaler+
[38]
BASELINE-Mean
[39]
BASELINE-zero
[40]
BD-Flow
[41]
BD-Flow_finetune
[42]
BDFlowNet
Anonymous.
[43]
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
[44]
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
[45]
Back2FutureFlow_UFO
J. Janai, F. Güney, A. Ranjan, M. Black and A. Geiger. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. ECCV, 2018.
[46]
C-2px
Anonymous.
[47]
CAR_100
Anonymous.
[48]
CARflow
[49]
CARflow-mv
Anonymous.
[50]
CCAFlow
Anonymous. Submission
[51]
CCMR+
Azin Jahedi, Maximilian Luz, Marc Rivinius, Andrés Bruhn "CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning", WACV 2024
[52]
CE_SKII
Anonymous.
[53]
CFFlow
Anonymous.
[54]
CGCV-GMA
[55]
CGCV-RAFT
[56]
CNet
Anonymous.
[57]
COF
H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[58]
COF_2019
H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[59]
COMBO
Anonymous.
[60]
CPM-Flow
Yinlin Hu, Rui Song, Yunsong Li. Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.
[61]
CPM2
Coarse-to-fine PatchMatch for dense correspondence, Yunsong Li, Yinlin Hu, Rui Song, Peng Rao, Yangli Wang, submitted to T-CSVT
[62]
CPM_AUG
Anonymous. CVPR 18 submission #1939
[63]
CPNFlow
Conditional Prior Networks for Optical Flow, Yanchao Yang, Stefano Soatto; The European Conference on Computer Vision (ECCV), 2018, pp. 271-287
[64]
CRAFT
Anonymous. Cross-Attentional Flow Transformer
[65]
CSFlow-2-view
Shi, H., Zhou, Y., Yang, K., Yin, X., & Wang, K. (2022). CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving. arXiv preprint arXiv:2202.00909.
[66]
CTFlow
[67]
CVE-RAFT
ljp
[68]
CVEFlow
ljp
[69]
CVENG22+Epic
Anonymous.
[70]
CVENG22+RIC
Anonymous.
[71]
CVPR-1235
Anonymous.
[72]
CasFlow
Anonymous. CasFlow
[73]
Channel-Flow
L. Sevilla-Lara, D. Sun, E. Learned-Miller, and M. Black. Optical Flow Estimation with Channel Constancy. ECCV, pages 423-438, 2014.
[74]
Classic++
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[75]
Classic+NL
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[76]
Classic+NL-fast
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[77]
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.
[78]
CoT-AMFlow
H. Wang, R. Fan, and M. Liu. CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation, CoRL 2020.
[79]
Compact-Unified-Model
A model for two motion tasks: optical flow and rectified stereo-matching
[80]
CompactFlow
Anonymous. ICCV submission.
[81]
CompactFlow-woscv
Anonymous.
[82]
CompactFlowNet
Anonymous.
[83]
ComponentFusion
Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.
[84]
ContFusion
M. Stoll, D. Maurer, and A. Bruhn. Variational Large Displacement Optical Flow without Feature Matches. EMMCVPR 2017.
[85]
ContinualFlow_ROB
Michal Neoral, Jan Šochman and Jiří Matas. Continual Occlusions and Optical Flow Estimation, ACCV 2018
[86]
CroCo-Flow
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. Weinzaepfel et al. ICCV 2023.
[87]
CrossFlow
Anonymous.
[88]
DA_opticalflow
Anonymous.
[89]
DC-RAFT
Anonymous.
[90]
DCFlow
Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
[91]
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.
[92]
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.
[93]
DCN-Flow
DCN-Flow
[94]
DCVNet
Anonymous. 0.03s with a GTX 1080ti GPU.
[95]
DDCNet_B0_tf_sintel
Anonymous. DDCNet_B0 fine-tuned on Sintel
[96]
DDCNet_B1_ft-sintel
DDCNet B1 finetuned on Sintel
[97]
DDCNet_Multires_ft_sintel
DDCNet Multires fine tuned on Sintel
[98]
DDCNet_Stacked
Anonymous. Two blocks of simple DDCNet
[99]
DDCNet_stacked2
Anonymous. two times down-sampling of feature maps, 128 filters each layer, fine-tuned on sintel
[100]
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
[101]
DDVM
Anonymous. NeurIPS 2023 submission #14395
[102]
DEQ-Flow-H
Deep Equilibrium Optical Flow Estimation
[103]
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
[104]
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
[105]
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
[106]
DICL-Flow
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
[107]
DICL-Flow+
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
[108]
DICL_update
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
[109]
DIP
Anonymous. Deep Inverse Patchmatch for High-Resolution Optical Flow
[110]
DIP-Flow
D. Maurer, M. Stoll, A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
[111]
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.
[112]
DMF_ROB
Baseline submission for ROB flow challenge: Deepflow with Deepmatching as prior
[113]
DPCTF
Anonymous. Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow
[114]
DPFlow
H. Morimitsu, X. Zhu, R. Cesar-Jr., X. Ji and X. Yin: DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework. CVPR 2025.
[115]
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.
[116]
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.
[117]
Data-Flow
Anonymous. CSAD data cost + second order smoothness
[118]
Deep+R
B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015
[119]
Deep-EIP
Anonymous. End-2-End learning for energy based inpainting of optical flow. ACCV submission 572
[120]
DeepDiscreteFlow
F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
[121]
DeepFlow
P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.
[122]
DeepFlow2
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.
[123]
DefFlowP
Anonymous.
[124]
Deformable_RAFT
Anonymous. RAFT with deformable
[125]
Devon
Anonymous. CVPR submission #1906
[126]
DictFlowS
Anonymous.
[127]
DiscreteFlow
M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015
[128]
DiscreteFlow+OIR
D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.
[129]
DiscreteFlow_nws
[130]
DistillFlow
Anonymous. Unsupervise result
[131]
DistillFlow+ft
Anonymous. Supervised result.
[132]
EFlow-M
Anonymous.
[133]
EFlow-M-tile
Anonymous.
[134]
EMD-L
Anonymous. Anonymous
[135]
EMD-M
Anonymous. Anonymous
[136]
EMD-OER
Anonymous.
[137]
EMSFlow
Anonymous.
[138]
EPIflow
Deep Epipolar Flow
[139]
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.
[140]
EPPM
L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.
[141]
ER-FLOW2
Anonymous. Adjusted ERFlow
[142]
ERFlow
Anonymous.
[143]
EdgeFlow
Anonymous.
[144]
EgFlow-cl
Anonymous. edge-guided, small parameter optical flow network based on CNN
[145]
EpicFlow
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.
[146]
ErrorMatch-GMA
Anonymous. tba
[147]
ErrorMatch-RAFT
Anonymous. tba
[148]
F2PD_JJN
Anonymous. With Deformable Convolutional Networks
[149]
F3-MPLF
Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh: Filter Flow made Practical: Massively Parallel and Lock-Free -- CVPR 2017
[150]
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
[151]
FAOP-Flow
Anonymous.
[152]
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
[153]
FCTR
Anonymous.
[154]
FCTR-m
Anonymous.
[155]
FDFlowNet
Lingtong Kong and Jie Yang. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network, ICIP 2020.
[156]
FF++_ROB
Accurate matching and robust interpolation. KITTI parameters as given by the paper. Edge detector trained on KITTI + Sintel + BSDS500.
[157]
FGI
Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016
[158]
FPCR-Net
Anonymous.
[159]
FPCR-Net2
Anonymous.
[160]
FTGAN
The Application of Counter Learning of Reverse Residual Attention in Optical Flow Estimation
[161]
FastFlow
Anonymous.
[162]
FastFlow2
Anonymous.
[163]
FastFlowNet
Lingtong Kong, Chunhua Shen and Jie Yang. FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation, ICRA 2021.
[164]
FastFlowNet-ft+
Anonymous.
[165]
Flow1D
Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong. High-Resolution Optical Flow from 1D Attention and Correlation. ICCV 2021, Oral
[166]
Flow1D-OER
Anonymous.
[167]
FlowDiffuser
FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models, CVPR 2024
[168]
FlowFields
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.
[169]
FlowFields+
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. T-PAMI.
[170]
FlowFields++
R. Schuster, C. Bailer, O. Wasenmüller, D. Stricker. FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018
[171]
FlowFieldsCNN
C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
[172]
FlowFormer
FlowFormer: A Transformer Architecture for Optical Flow
[173]
FlowFormer++
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
[174]
FlowNet2
Anonymous. CVPR Submission #900
[175]
FlowNet2-ft-sintel
Anonymous. CVPR Submission #900
[176]
FlowNetADF
Lightweight Probabilistic Deep Networks
[177]
FlowNetC+OFR
Anonymous.
[178]
FlowNetC+ft+v
Anonymous. ICCV sumbmission 235
[179]
FlowNetC-MD
Anonymous.
[180]
FlowNetProbOut
Lightweight Probabilistic Deep Networks
[181]
FlowNetS+ft+v
Anonymous. ICCV submission 235
[182]
FlowSAC_dcf
Anonymous.
[183]
FlowSAC_ff
Anonymous
[184]
Flownet2-IA
Anonymous. Flownet2 combining with illumination adjustment
[185]
Flownet2-IAER
Anonymous. Flownet2 combining with illumination adjustment and edge refinement
[186]
FullFlow
Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
[187]
FullFlow+KF
W. Bao, Y. Xiao, L. Chen, and Z. Gao. Kalmanflow: Efficient Kalman Filtering for Video Optical Flow. ICIP 2018.
[188]
GAFlow
Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
[189]
GAFlow-FF
Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
[190]
GANFlow
Anonymous.
[191]
GCA-Net
Anonymous.
[192]
GCA-Net-ft+
Anonymous. finetune GCA-Net with a better data augmentation method
[193]
GMA
Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley. Learning to Estimate Hidden Motions with Global Motion Aggregation, ICCV 2021.
[194]
GMA+LCT-Flow
GMA+LCT-Flow
[195]
GMA+TCU+aug
Anonymous.
[196]
GMA+TCU-aug
Anonymous.
[197]
GMA-FS
Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
[198]
GMA-base
Anonymous. GMA-base
[199]
GMA-two_img
Anonymous.
[200]
GMFlow
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao. GMFlow: Learning Optical Flow via Global Matching. CVPR 2022, Oral
[201]
GMFlow+
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, Andreas Geiger. Unifying Flow, Stereo and Depth Estimation. TPAMI 2023
[202]
GMFlowNet
Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou and Dimitris Metaxas. Global Matching with Overlapping Attention for Optical Flow Estimation. CVPR 2022
[203]
GMFlow_RVC
GMFlow RVC 2022 submission.
[204]
GPNet
Anonymous.
[205]
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.
[206]
GeoViT
[207]
GlobalPatchCollider
Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016
[208]
GroundTruth
[209]
Grts-Flow-V2
En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT
[210]
H+S_ROB
Baseline submission for the ROB flow challenge: Horn-Schunck Optical Flow with a Multi-Scale Strategy
[211]
H+S_RVC
RVC 2020 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann.
[212]
H-1px
Anonymous.
[213]
H-v3
Anonymous.
[214]
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
[215]
HCOF+multi
R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.
[216]
HCVNet
Anonymous.
[217]
HD3-Flow
Zhichao Yin, Trevor Darrell, Fisher Yu. Hierarchical Discrete Distribution Decomposition for Match Density Estimation (CVPR 2019)
[218]
HD3-Flow-OER
Anonymous.
[219]
HD3F+MSDRNet
Anonymous. HD3F+MSDRNet
[220]
HMAFlow
Anonymous.
[221]
HMFlow
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
[222]
HSVFlow
Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
[223]
Horn+Schunck
A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.
[224]
ICALD
M. Stoll, D. Maurer, S. Volz and A. Bruhn. Illumination-Aware Large Displacement Optical Flow. EMMCVPR 2017.
[225]
ICIMG-Net
Anonymous.
[226]
IHBPFlow
Anonymous.
[227]
IIOF-NLDP
Dinh-Hoan Trinh, Walter Blondel, and Christian Daul. A General Form of Illumination-Invariant Descriptors in Variational Optical Flow Estimation. ICIP 2017.
[228]
IOFPL-CVr8-ft
Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
[229]
IOFPL-ft
Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
[230]
IPOL_Brox
J. Sánchez, N. Monzón, and A. Salgado, Robust Optical Flow Estimation, Image Processing On Line (IPOL), 3 (2013), pp. 252–270.
[231]
IRR-PWC
Junhwa Hur and Stefan Roth. Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019
[232]
IRR-PWC-OER
Anonymous.
[233]
IRR-PWC_RVC
RVC 2020 submission
[234]
ISDAFlow
Anonymous. ISDAFlow: Self-supervised Infusion of Segmented and Depth Anything Models into Optical Flow
[235]
ISDAFlowRAFT
Anonymous. ISDAFlow+RAFT
[236]
IWarp
Anonymous.
[237]
InterpoNet_cpm
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[238]
InterpoNet_df
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[239]
InterpoNet_dm
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[240]
InterpoNet_ff
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[241]
JOF
Zhang Congxuan, Ge Liyue, Chen Zhen, Li Ming, Liu Wen, Chen Hao. Refined TV-L1 Optical Flow Estimation Using Joint Filtering, IEEE Transactions on Multimedia, 2020, 22(2):349-364.
[242]
KPA-Flow
Anonymous. Learning Optical Flow with Kernel Patch Attention, CVPR 2022.
[243]
L2L-Flow-ext
Anonymous.
[244]
L2L-Flow-ext-warm
Anonymous.
[245]
LDOF
T. Brox, J. Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3): 500-513, 2011.
[246]
LLA-FLOW+GMA
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation. ICIP 2023.
[247]
LLA-Flow
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation. ICIP 2023.
[248]
LSHRAFT
Anonymous.
[249]
LSM_FLOW_RVC
LSM: Learning Subspace Minimization for Low-level Vision for RVC2020
[250]
Lavon
Anonymous.
[251]
LiteFlowNet
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018.
[252]
LiteFlowNet2
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020.
[253]
LiteFlowNet3
Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.
[254]
LiteFlowNet3-S
Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.
[255]
LocalLayering
D. Sun, C. Liu, and H. Pfister. Local Layering for Joint Motion Estimation and Occlusion Detection. CVPR 2014.
[256]
M-1px
Anonymous.
[257]
M2FLOW
Xunpei Sun, Gang Chen, and Zuoxun Hou, “M2Flow: A Motion Information Fusion Framework for Enhanced Unsupervised Optical Flow Estimation in Autonomous Driving,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 7, pp. 7140–7148, 2025.
[258]
MCPFlow_RVC
RVC 2022 submission
[259]
MDFlow
Lingtong Kong and Jie Yang. MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation, TCSVT 2022.
[260]
MDFlow-Fast
Lingtong Kong and Jie Yang. MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation, TCSVT 2022.
[261]
MDP-Flow2
L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012.
[262]
MEMFOF
V. Bargatin, E. Chistov