Visual Results by Method

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


Return to numerical results table



  • Metric_09 Perturbed Market 3, s40+ = 7.504
  • Metric_09 Perturbed Shaman 1, s40+ = 0.992
  • Metric_09 Ambush 1, s40+ = 18.844
  • Metric_09 Ambush 3, s40+ = 22.464
  • Metric_09 Bamboo 3, s40+ = 18.619
  • Metric_09 Cave 3, s40+ = 6.739
  • Metric_09 Market 1, s40+ = 1.500
  • Metric_09 Market 4, s40+ = 16.417
  • Metric_09 Mountain 2, s40+ = 0.000
  • Metric_09 Temple 1, s40+ = 15.971
  • Metric_09 Tiger, s40+ = 20.058
  • Metric_09 Wall, s40+ = 22.347

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]
AOD
[19]
APCAFlow
Anonymous.
[20]
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.
[21]
ARFlow+LCT-Flow
ARFlow+LCT-Flow
[22]
ARFlow-base
Anonymous. ARFlow-base
[23]
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.
[24]
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.
[25]
AVG_FLOW_ROB
Average flow field of ROB2018 training set. No image information used!
[26]
AggregFlow
D. Fortun, P. Bouthemy and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. CVIU 2016
[27]
AnisoHuber.L1
M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009.
[28]
AnyFlow
Anonymous. PAMI pending review
[29]
AnyFlow+GMA
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation. CVPR, 2023.
[30]
AtrousFlow
Anonymous. Real-time dense optical flow using CUDA
[31]
AugFNG_ROB
Anonymous.
[32]
AutoScaler+
Anonymous. AutoScaler+
[33]
BASELINE-Mean
[34]
BASELINE-zero
[35]
BD-Flow
[36]
BD-Flow_finetune
[37]
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
[38]
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
[39]
Back2FutureFlow_UFO
J. Janai, F. Güney, A. Ranjan, M. Black and A. Geiger. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. ECCV, 2018.
[40]
C-2px
Anonymous.
[41]
CAR_100
Anonymous.
[42]
CARflow
[43]
CARflow-mv
Anonymous.
[44]
CCAFlow
Anonymous. Submission
[45]
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
[46]
CE_SKII
Anonymous.
[47]
CGCV-GMA
[48]
CGCV-RAFT
[49]
CNet
Anonymous.
[50]
COF
H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[51]
COF_2019
H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[52]
COMBO
Anonymous.
[53]
CPM-Flow
Yinlin Hu, Rui Song, Yunsong Li. Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.
[54]
CPM2
Coarse-to-fine PatchMatch for dense correspondence, Yunsong Li, Yinlin Hu, Rui Song, Peng Rao, Yangli Wang, submitted to T-CSVT
[55]
CPM_AUG
Anonymous. CVPR 18 submission #1939
[56]
CPNFlow
Conditional Prior Networks for Optical Flow, Yanchao Yang, Stefano Soatto; The European Conference on Computer Vision (ECCV), 2018, pp. 271-287
[57]
CRAFT
Anonymous. Cross-Attentional Flow Transformer
[58]
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.
[59]
CVE-RAFT
ljp
[60]
CVEFlow
ljp
[61]
CVENG22+Epic
Anonymous.
[62]
CVENG22+RIC
Anonymous.
[63]
CVPR-1235
Anonymous.
[64]
CasFlow
Anonymous. CasFlow
[65]
Channel-Flow
L. Sevilla-Lara, D. Sun, E. Learned-Miller, and M. Black. Optical Flow Estimation with Channel Constancy. ECCV, pages 423-438, 2014.
[66]
Classic++
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[67]
Classic+NL
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[68]
Classic+NL-fast
D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
[69]
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.
[70]
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.
[71]
CompactFlow
Anonymous. ICCV submission.
[72]
CompactFlow-woscv
Anonymous.
[73]
CompactFlowNet
Anonymous.
[74]
ComponentFusion
Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.
[75]
ContFusion
M. Stoll, D. Maurer, and A. Bruhn. Variational Large Displacement Optical Flow without Feature Matches. EMMCVPR 2017.
[76]
ContinualFlow_ROB
Michal Neoral, Jan Šochman and Jiří Matas. Continual Occlusions and Optical Flow Estimation, ACCV 2018
[77]
CroCo-Flow
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. Weinzaepfel et al. ICCV 2023.
[78]
CrossFlow
Anonymous.
[79]
DA_opticalflow
Anonymous.
[80]
DCFlow
Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
[81]
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.
[82]
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.
[83]
DCN-Flow
DCN-Flow
[84]
DCVNet
Anonymous. 0.03s with a GTX 1080ti GPU.
[85]
DDCNet_B0_tf_sintel
Anonymous. DDCNet_B0 fine-tuned on Sintel
[86]
DDCNet_B1_ft-sintel
DDCNet B1 finetuned on Sintel
[87]
DDCNet_Multires_ft_sintel
DDCNet Multires fine tuned on Sintel
[88]
DDCNet_Stacked
Anonymous. Two blocks of simple DDCNet
[89]
DDCNet_stacked2
Anonymous. two times down-sampling of feature maps, 128 filters each layer, fine-tuned on sintel
[90]
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
[91]
DDVM
Anonymous. NeurIPS 2023 submission #14395
[92]
DEQ-Flow-H
Deep Equilibrium Optical Flow Estimation
[93]
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
[94]
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
[95]
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
[96]
DICL-Flow
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
[97]
DICL-Flow+
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
[98]
DICL_update
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
[99]
DIP
Anonymous. Deep Inverse Patchmatch for High-Resolution Optical Flow
[100]
DIP-Flow
D. Maurer, M. Stoll, A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
[101]
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.
[102]
DMF_ROB
Baseline submission for ROB flow challenge: Deepflow with Deepmatching as prior
[103]
DPCTF
Anonymous. Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow
[104]
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.
[105]
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.
[106]
Data-Flow
Anonymous. CSAD data cost + second order smoothness
[107]
Deep+R
B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015
[108]
Deep-EIP
Anonymous. End-2-End learning for energy based inpainting of optical flow. ACCV submission 572
[109]
DeepDiscreteFlow
F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
[110]
DeepFlow
P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.
[111]
DeepFlow2
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.
[112]
DefFlowP
Anonymous.
[113]
Deformable_RAFT
Anonymous. RAFT with deformable
[114]
Devon
Anonymous. CVPR submission #1906
[115]
DictFlowS
Anonymous.
[116]
DiscreteFlow
M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015
[117]
DiscreteFlow+OIR
D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.
[118]
DistillFlow
Anonymous. Unsupervise result
[119]
DistillFlow+ft
Anonymous. Supervised result.
[120]
EFlow-M
Anonymous.
[121]
EFlow-M-tile
Anonymous.
[122]
EMD-L
Anonymous. Anonymous
[123]
EMD-M
Anonymous. Anonymous
[124]
EMD-OER
Anonymous.
[125]
EPIflow
Deep Epipolar Flow
[126]
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.
[127]
EPPM
L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.
[128]
ER-FLOW2
Anonymous. Adjusted ERFlow
[129]
ERFlow
Anonymous.
[130]
EdgeFlow
Anonymous.
[131]
EgFlow-cl
Anonymous. edge-guided, small parameter optical flow network based on CNN
[132]
EpicFlow
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.
[133]
ErrorMatch-GMA
Anonymous. tba
[134]
ErrorMatch-RAFT
Anonymous. tba
[135]
F2PD_JJN
Anonymous. With Deformable Convolutional Networks
[136]
F3-MPLF
Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh: Filter Flow made Practical: Massively Parallel and Lock-Free -- CVPR 2017
[137]
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
[138]
FAOP-Flow
Anonymous.
[139]
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
[140]
FCTR
Anonymous.
[141]
FCTR-m
Anonymous.
[142]
FDFlowNet
Lingtong Kong and Jie Yang. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network, ICIP 2020.
[143]
FF++_ROB
Accurate matching and robust interpolation. KITTI parameters as given by the paper. Edge detector trained on KITTI + Sintel + BSDS500.
[144]
FGI
Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016
[145]
FPCR-Net
Anonymous.
[146]
FPCR-Net2
Anonymous.
[147]
FTGAN
The Application of Counter Learning of Reverse Residual Attention in Optical Flow Estimation
[148]
FastFlow
Anonymous.
[149]
FastFlow2
Anonymous.
[150]
FastFlowNet
Lingtong Kong, Chunhua Shen and Jie Yang. FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation, ICRA 2021.
[151]
FastFlowNet-ft+
Anonymous.
[152]
Flow1D
Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong. High-Resolution Optical Flow from 1D Attention and Correlation. ICCV 2021, Oral
[153]
Flow1D-OER
Anonymous.
[154]
FlowDiffuser
FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models, CVPR 2024
[155]
FlowFields
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.
[156]
FlowFields+
C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. T-PAMI.
[157]
FlowFields++
R. Schuster, C. Bailer, O. Wasenmüller, D. Stricker. FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018
[158]
FlowFieldsCNN
C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
[159]
FlowFormer
FlowFormer: A Transformer Architecture for Optical Flow
[160]
FlowFormer++
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
[161]
FlowNet2
Anonymous. CVPR Submission #900
[162]
FlowNet2-ft-sintel
Anonymous. CVPR Submission #900
[163]
FlowNetADF
Lightweight Probabilistic Deep Networks
[164]
FlowNetC+OFR
Anonymous.
[165]
FlowNetC+ft+v
Anonymous. ICCV sumbmission 235
[166]
FlowNetC-MD
Anonymous.
[167]
FlowNetProbOut
Lightweight Probabilistic Deep Networks
[168]
FlowNetS+ft+v
Anonymous. ICCV submission 235
[169]
FlowSAC_dcf
Anonymous.
[170]
FlowSAC_ff
Anonymous
[171]
Flownet2-IA
Anonymous. Flownet2 combining with illumination adjustment
[172]
Flownet2-IAER
Anonymous. Flownet2 combining with illumination adjustment and edge refinement
[173]
FullFlow
Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
[174]
FullFlow+KF
W. Bao, Y. Xiao, L. Chen, and Z. Gao. Kalmanflow: Efficient Kalman Filtering for Video Optical Flow. ICIP 2018.
[175]
GAFlow
Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
[176]
GAFlow-FF
Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
[177]
GANFlow
Anonymous.
[178]
GCA-Net
Anonymous.
[179]
GCA-Net-ft+
Anonymous. finetune GCA-Net with a better data augmentation method
[180]
GMA
Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley. Learning to Estimate Hidden Motions with Global Motion Aggregation, ICCV 2021.
[181]
GMA+LCT-Flow
GMA+LCT-Flow
[182]
GMA+TCU+aug
Anonymous.
[183]
GMA+TCU-aug
Anonymous.
[184]
GMA-FS
Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
[185]
GMA-base
Anonymous. GMA-base
[186]
GMA-two_img
Anonymous.
[187]
GMFlow
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao. GMFlow: Learning Optical Flow via Global Matching. CVPR 2022, Oral
[188]
GMFlow+
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, Andreas Geiger. Unifying Flow, Stereo and Depth Estimation. TPAMI 2023
[189]
GMFlowNet
Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou and Dimitris Metaxas. Global Matching with Overlapping Attention for Optical Flow Estimation. CVPR 2022
[190]
GMFlow_RVC
GMFlow RVC 2022 submission.
[191]
GPNet
Anonymous.
[192]
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.
[193]
GlobalPatchCollider
Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016
[194]
GroundTruth
[195]
Grts-Flow-V2
En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT
[196]
H+S_ROB
Baseline submission for the ROB flow challenge: Horn-Schunck Optical Flow with a Multi-Scale Strategy
[197]
H+S_RVC
RVC 2020 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann.
[198]
H-1px
Anonymous.
[199]
H-v3
Anonymous.
[200]
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
[201]
HCOF+multi
R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.
[202]
HCVNet
Anonymous.
[203]
HD3-Flow
Zhichao Yin, Trevor Darrell, Fisher Yu. Hierarchical Discrete Distribution Decomposition for Match Density Estimation (CVPR 2019)
[204]
HD3-Flow-OER
Anonymous.
[205]
HD3F+MSDRNet
Anonymous. HD3F+MSDRNet
[206]
HMAFlow
Anonymous.
[207]
HMFlow
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
[208]
HSVFlow
Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
[209]
Horn+Schunck
A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.
[210]
ICALD
M. Stoll, D. Maurer, S. Volz and A. Bruhn. Illumination-Aware Large Displacement Optical Flow. EMMCVPR 2017.
[211]
IHBPFlow
Anonymous.
[212]
IIOF-NLDP
Dinh-Hoan Trinh, Walter Blondel, and Christian Daul. A General Form of Illumination-Invariant Descriptors in Variational Optical Flow Estimation. ICIP 2017.
[213]
IOFPL-CVr8-ft
Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
[214]
IOFPL-ft
Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
[215]
IPOL_Brox
J. Sánchez, N. Monzón, and A. Salgado, Robust Optical Flow Estimation, Image Processing On Line (IPOL), 3 (2013), pp. 252–270.
[216]
IRR-PWC
Junhwa Hur and Stefan Roth. Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019
[217]
IRR-PWC-OER
Anonymous.
[218]
IRR-PWC_RVC
RVC 2020 submission
[219]
InterpoNet_cpm
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[220]
InterpoNet_df
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[221]
InterpoNet_dm
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[222]
InterpoNet_ff
S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
[223]
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.
[224]
KPA-Flow
Anonymous. Learning Optical Flow with Kernel Patch Attention, CVPR 2022.
[225]
L2L-Flow-ext
Anonymous.
[226]
L2L-Flow-ext-warm
Anonymous.
[227]
LDOF
T. Brox, J. Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3): 500-513, 2011.
[228]
LLA-FLOW+GMA
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation. ICIP 2023.
[229]
LLA-Flow
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation. ICIP 2023.
[230]
LSHRAFT
Anonymous.
[231]
LSM_FLOW_RVC
LSM: Learning Subspace Minimization for Low-level Vision for RVC2020
[232]
Lavon
Anonymous.
[233]
LiteFlowNet
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018.
[234]
LiteFlowNet2
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020.
[235]
LiteFlowNet3
Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.
[236]
LiteFlowNet3-S
Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.
[237]
LocalLayering
D. Sun, C. Liu, and H. Pfister. Local Layering for Joint Motion Estimation and Occlusion Detection. CVPR 2014.
[238]
M-1px
Anonymous.
[239]
MCPFlow_RVC
RVC 2022 submission
[240]
MDFlow
Lingtong Kong and Jie Yang. MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation, TCSVT 2022.
[241]
MDFlow-Fast
Lingtong Kong and Jie Yang. MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation, TCSVT 2022.
[242]
MDP-Flow2
L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012.
[243]
MF2C
Anonymous
[244]
MFCFlow
Anonymous.
[245]
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)
[246]
MFFC
Anonymous
[247]
MFR
Anonymous. Motion Feature Recovery
[248]
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.
[249]
MMAFlow
Anonymous.
[250]
MMAFlow
Anonymous.
[251]
MPIF
Anonymous. multi-level interpolation for optical flow estimation
[252]
MR-Flow
J. Wulff, L. Sevilla-Lara, M. J. Black: Optical Flow in Mostly Rigid Scenes. CVPR 2017.
[253]
MRDFlow
Anonymous.
[254]
MS_RAFT
Azin Jahedi, Lukas Mehl, Marc Rivinius, Andrés Bruhn, "Multi-scale RAFT: Combining Hierarchical Concepts for Learning-based Optical Flow Estimation", ICIP 2022
[255]
MS_RAFT+_RVC
Azin Jahedi, Maximilian Luz, Marc Rivinius, Lukas Mehl, and Andrés Bruhn: "MS-RAFT+: High Resolution Multi-Scale RAFT", IJCV 2023
[256]
MVFlow
Anonymous.
[257]
MaskFlownet
Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I-Chao Chang, and Yan Xu. MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask, CVPR 2020 (Oral).
[258]
MaskFlownet-S
Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I-Chao Chang, and Yan Xu. MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask, CVPR 2020 (Oral).
[259]
MatchFlow_GMA
Qiaole Dong, Chenjie Cao, Yanwei Fu. Rethinking Optical Flow from Geometric Matching Consistent Perspective. CVPR 2023.
[260]
MatchFlow_GMA_2-view
Anonymous.
[261]
MatchFlow_RAFT
Qiaole Dong, Chenjie Cao, Yanwei Fu. Rethinking Optical Flow from Geometric Matching Consistent Perspective. CVPR 2023.
[262]
MeFlow
Anonymous.
[263]
MemFlow
Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
[264]
MemFlow-T
Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
[265]
MemoFlow
Anonymous.
[266]
MirrorFlow
Junhwa Hur and Stefan Roth. MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017
[267]
MobileFlow
Anonymous. Optical flow estimation framework using an enhanced MobileNet and Transformer with a large-kernel upsampler.
[268]
Model_model
Anonymous. this is a model
[269]
MotionFlow
Anonymous.
[270]
MotionFlow+
Anonymous.
[271]
NASFlow
Anonymous.
[272]
NASFlow-PWC
Anonymous.
[273]
NASFlow-RAFT
Anonymous.
[274]
NLTGV-SC
R. Ranftl, K. Bredies, T. Pock. Non-Local Total Generalized Variation for Optical Flow Estimation, ECCV 2014
[275]
NNF-Local
Anonymous. Large Displacement Optical Flow from Nearest Neighbor Fields
[276]
NccFlow
Anonymous.
[277]
OADFlow
Anonymous.
[278]
OAR-Flow
D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.
[279]
OAS-Net
Lingtong Kong, Xiaohang Yang and Jie Yang. OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow, ICASSP 2021.
[280]
OF-OEF
Anonymous. Optical flow estimation combining with objects edge features
[281]
OF_OCC_LD
V. Lazcano, L. Garrido, C. Ballester. Jointly Optical flow and Occlusion Estimation for images with Large Displacements.
[282]
OIFlow
occlusion-inpainting Flow
[283]
OM_CRAFT
Anonymous.
[284]
OM_GMA
Anonymous.
[285]
OM_GMFlow
Anonymous.
[286]
OM_RAFT
Anonymous. Used method with channel shuffle to enhance feature.
[287]
OPPFlow
Anonymous.
[288]
OatNet01
Anonymous.
[289]
PCA-Flow
J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
[290]
PCA-Layers
J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
[291]
PGM-C
Anonymous
[292]
PH-Flow
Jiaolong Yang and Hongdong Li. Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015
[293]
PMC-PWC_edge_loss
Congxuan Zhang, Cheng Feng, Zhen Chen, Weiming Hu, Ming Li, Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection, Signal Processing: Image Communication, Volume 101, 2022, doi: 10.1016/j.image.2021.116560
[294]
PMC-PWC_without_edge_loss
Congxuan Zhang, Cheng Feng, Zhen Chen, Weiming Hu, Ming Li, Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection, Signal Processing: Image Communication, Volume 101, 2022, doi: 10.1016/j.image.2021.116560
[295]
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
[296]
PPAC-HD3
Anne S. Wannenwetsch, Stefan Roth. Probabilistic Pixel-Adaptive Refinement Networks. CVPR 2020.
[297]
PPM
Parametric PatchMatch, Fangjun Kuang, master thesis, 2017
[298]
PRAFlow_RVC
Anonymous.
[299]
PRichFlow
Anonymous.
[300]
PST
Anonymous. ACCV2018 submission #1195
[301]
PVTFlow
Anonymous.
[302]
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.
[303]
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
[304]
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.
[305]
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.
[306]
PWC-Net-OER
Anonymous.
[307]
PWC-Net_RVC
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018. Renamed from PWC-Net_ROB to PWC-Net_RVC.
[308]
PWC_acn
Anonymous.
[309]
PatchBatch+Inter
T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.
[310]
PatchBatch-CENT+SD
Anonymous.
[311]
PatchWMF-OF
Zhigang Tu, Coert Van Gemeren and Remco C. Veltkamp. Improved Color Patch Similarity Measure Based Weighted Median Filter. ACCV2014
[312]
PosetOptimization
Anonymous.
[313]
ProFlow
D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.
[314]
ProFlow_ROB
D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018. (ROB setting)
[315]
ProMotion
Anonymous. Anonymous submission
[316]
ProbFlowFields
Anne S. Wannenwetsch, Margret Keuper, Stefan Roth. ProbFlow: Joint Optical Flow and Uncertainty Estimation. ICCV 2017.
[317]
ProtoFormer
[318]
ProtoFormer
Anonymous.
[319]
Pwc_ps
Anonymous.
[320]
RAFT
Zachary Teed and Jia Deng. RAFT: Recurrent All-Pairs Field Transforms for Optical Flow, ECCV 2020.
[321]
RAFT+AOIR
L. Mehl, C. Beschle, A. Barth, A. Bruhn: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. SSVM 2021.
[322]
RAFT+ConvUp
Anonymous.
[323]
RAFT+LCT-Flow
RAFT+LCT-Flow
[324]
RAFT+LCV
Taihong Xiao, Jinwei Yuan, Deqing Sun, Qifei Wang, Xin-Yu Zhang, Kehan Xu, Ming-Hsuan Yang. Learnable Cost Volume using the Cayley Representation, ECCV 2020
[325]
RAFT+NCUP
Normalized Convolution Upsampling for Refined Optical Flow Estimation
[326]
RAFT+OBS
Anonymous. We change the dataset to train RAFT.
[327]
RAFT-A
2-frame result. Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, and Ce Liu. "AutoFlow: Learning a Better Training Set for Optical Flow" CVPR 2021 https://arxiv.org/abs/2104.14544
[328]
RAFT-CF
[329]
RAFT-DFlow
Anonymous.
[330]
RAFT-FS
Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
[331]
RAFT-GT
Anonymous. CVPR 2021 submission
[332]
RAFT-GT-ft
Anonymous. CVPR 2021 submission
[333]
RAFT-OCTC
Anonymous.
[334]
RAFT-TF_RVC
Deqing Sun, Charles Herrmann, Varun Jampani, Mike Krainin, Forrester Cole, Austin Stone, Rico Jonschkowski, Ramin Zabih, William Freeman, and Ce Liu. A TensorFlow implementation of RAFT (Zachary Teed and Jia Deng. RAFT: Recurrent all-pairs field transforms for optical flow. ECCV 2020.) RVC 2020 submission.
[335]
RAFT-VM
[336]
RAFT-base
Anonymous. RAFT-base
[337]
RAFT-illumination
Anonymous. RAFT-illumination
[338]
RAFT-it
D. Sun, C. Herrmann, F. Reda, M. Rubinstein, D. Fleet and W. Freeman: Disentangling Architecture and Training for Optical Flow. ECCV 2022.
[339]
RAFT-it+_RVC
D. Sun, C. Herrmann, F. Reda, M. Rubinstein, D. Fleet and W. Freeman: Disentangling Architecture and Training for Optical Flow. ECCV 2022. Scaled up RAFT.
[340]
RAFT2-L
Anonymous.
[341]
RAFT_Chairs_Things
Anonymous.
[342]
RAFTv1-OER-2-view
Anonymous.
[343]
RAFTv2-OER-2-view
Anonymous.
[344]
RAFTv2-OER-warm-start
Anonymous.
[345]
RAFTwarm+AOIR
L. Mehl, C. Beschle, A. Barth, A. Bruhn: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. SSVM 2021.
[346]
RAFTwarm+OBS
Anonymous. train the RAFT network on new our datasets and applying warmup
[347]
RAPIDFlow
Henrique Morimitsu, Xiaobin Zhu, Roberto M. Cesar-Jr., Xiangyang Ji, and Xu-Cheng Yin. RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for efficient optical flow estimation. ICRA, 2024.
[348]
RC-LSTM-1dir
Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
[349]
RC-LSTM-4dir
Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
[350]
RFPM
Anonymous.
[351]
RGBFlow
Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
[352]
RICBCDN
Anonymous.
[353]
RLOF_DENSE
Jonas Geistert, Tobias Senst and Thomas Sikora, Robust Local Optical Flow: Dense Motion Vector Field Interpolation, PCS 2016
[354]
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.
[355]
RPKNet
Henrique Morimitsu, Xiaobin Zhu, Xiangyang Ji, and Xu-Cheng Yin. Recurrent Partial Kernel Network for Efficient Optical Flow Estimation. AAAI, 2024.
[356]
ResPWCR_ROB
Anonymous.
[357]
RicFlow
Yinlin Hu, Yunsong Li, Rui Song. Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.
[358]
RichFlow-ft
Anonymous.
[359]
RichFlow-ft-fnl
Anonymous. final pass version
[360]
S2D-Matching
M. Leordeanu, A. Zanfir, C. Sminchisescu. Locally Affine Sparse-to-Dense Matching for Motion and Occlusion estimation. ICCV 2013
[361]
S2F-IF
Yanchao Yang, Stefano Soatto. S2F: Slow-To-Fast Interpolator Flow. CVPR 2017.
[362]
SAMFL
Zhang Congxuan, Zhou Zhongkai, Chen Zhen, Hu Weming, Li Ming, Jiang Shaofeng. Self-attention-based Multiscale Feature Learning Optical Flow with Occlusion Feature Map Prediction, IEEE Transactions on Multimedia, 2021, DOI: 10.1109/TMM.2021.3096083.
[363]
SAMFlow
SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model. Accepted in AAAI 2024.
[364]
SAnet
Anonymous.
[365]
SCAR
Anonymous.
[366]
SCFlow
Anonymous.
[367]
SCV
Anonymous. CVPR 2021 submission #3221
[368]
SDFlow
Anonymous.
[369]
SENSE
Anonymous. TBA
[370]
SFL
Jingchun Cheng, Yi-Hsuan, Tsai, Shengjin Wang, Ming-Hsuan Yang. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow. ICCV 2017
[371]
SJTU_PAMI418
[372]
SKFlow
Shangkun Sun, Yuanqi Chen, Yu Zhu, Guodong Guo, Ge Li. Learning Optical Flow with Super Kernels. NeurIPS 2022.
[373]
SKFlow_RAFT
Anonymous.
[374]
SKII
Anonymous.
[375]
SMURF
Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski. SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping, CVPR 2021
[376]
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
[377]
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.
[378]
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
[379]
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
[380]
SSTM++3kt
[381]
SSTM++_reconst
[382]
SSTM++_ttt_nws
Fisseha Admasu Ferede, Madhusudhanan Balasubramanian. SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation. Neurocomputing 2023.
[383]
SSTM++_ttt_ws
Fisseha Admasu Ferede, Madhusudhanan Balasubramanian. SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation. Neurocomputing 2023.
[384]
SSTM++nws-main
[385]
SSTM++warm-main
[386]
SSTM-nws
[387]
STC-Flow
Anonymous.
[388]
STDC-Flow
STDC-Flow: large displacement flow field estimation using similarity transformationbased dense correspondence, IET Computer Vision, 2020
[389]
STaRFlow
Pierre Godet, Alexandre Boulch, Aurélien Plyer, Guy Le Besnerais. STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation, ICPR 2020 (https://arxiv.org/abs/2007.05481)
[390]
SVFilterOh
Mohamed A. Helala, Faisal Z. Qureshi, Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering, CRV 2017
[391]
Scale-flow++_GS58
Efficient and Accurate Monocular 3D Motion Estimation Trained by GS58
[392]
ScaleFLow++
Improved version of Scale-flow with added flight foreground, sports field initialization, and improved iterators
[393]
ScaleFlow++_SAG
ScaleFlow trained using GS58 in SAG++
[394]
ScaleFlow_GS58
Train ScaleFlow by GS58
[395]
ScaleRAFT
Anonymous. ScaleRAFT
[396]
ScopeFlow
Aviram Bar-Haim and Lior Wolf. ScopeFlow: Dynamic Scene Scoping for Optical Flow, CVPR 2020.
[397]
SegFlow113
Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=11,d1=3)(Matlab code is available.)
[398]
SegFlow153
Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=15,d1=3)(Matlab code is available.)
[399]
SegFlow193
Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=19,d1=3)(Matlab code is available.)
[400]
SegFlow33
Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=3, d1=3)(Matlab code is available.)
[401]
SegFlow73
Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=7,d1=3)(Matlab code is available.)
[402]
SegPM+Interpolation
SegPM+Interpolation
[403]
SelFlow
Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)
[404]
SelFlow
Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)
[405]
Semantic_Lattice
Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler, Stefan Roth. Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice. GCPR 2019.
[406]
SeparableFlow-2views
Feihu Zhang, Oliver Woodford, Victor Prisacariu, Philip Torr, "Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation", ICCV 2021
[407]
SfM-PM
D. Maurer, N. Marniok, B. Goldluecke, A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation. ECCV 2018.
[408]
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)
[409]
SparseFlow
Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
[410]
SparseFlowFused
Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
[411]
SplatFlow
SplatFlow: Learning Multi-frame Optical Flow via Splatting; https://arxiv.org/abs/2306.08887; IJCV 2024
[412]
Steered-L1
Anonymous.
[413]
StreamFlow
[414]
StreamFlow-Baseline
[415]
StruPyNet
Zefeng Sun, Hanli Wang, Yun Yi, and Qinyu Li, Structural Pyramid Network for Cascaded Optical Flow Estimation, The 26th International Conference on Multimedia Modeling (MMM’20) , Daejeon, Korea, Jan. 5-8, 2020.
[416]
StruPyNet-ft
Zefeng Sun, Hanli Wang, Yun Yi, and Qinyu Li, Structural Pyramid Network for Cascaded Optical Flow Estimation, The 26th International Conference on Multimedia Modeling (MMM’20) , Daejeon, Korea, Jan. 5-8, 2020.
[417]
SwinTR-RAFT
Anonymous.
[418]
TF+OFM
R. Kennedy and C.J. Taylor. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.
[419]
TIMCflow
Fei Yang, Yongmei Cheng, Joost Van de Weijer, Mikhail G. Mozerov. 'Improved Discrete Optical Flow Estimation with Triple Image Matching Cost', IEEE Access
[420]
TSA
Anonymous.
[421]
TSGFlow
Anonymous. Tri-branch self-guided Transformer for optical flow estimation.
[422]
TV-L1
Anonymous. It is a well-known model that uses TV-L1 (coupled) as the regularization term and an L1 data term.
[423]
TV-L1+EM
V. Lazcano. EXHAUSTIVE MATCHING EMPIRICAL STUDY FOR IMPROVING THE MOTION FIELD ESTIMATION
[424]
TVL1_BWMFilter
Balanced Weighted Median Filter and Bilateral Filter.
[425]
TVL1_LD_GF
V. Lazcano. TVL1 to handle large displacements using gradient patches. Parameter where optimized using PSO.
[426]
TVL1_ROB
Baseline submission for the robust vision flow challenge: TV-L1 Optical Flow Estimation
[427]
TVL1_RVC
RVC 2020 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo
[428]
TransFlow
Anonymous. CVPR 2023 anonymous submission
[429]
TrepFlow
Anonymous. Submission
[430]
UFlow
R. Jonschkowski, A. Stone, J. Barron, A. Gordon, K. Konolige, and A. Angelova. What Matters in Unsupervised Optical Flow. ECCV 2020. (Code is available)
[431]
UPFlow
[432]
UlDENet
Anonymous.
[433]
UnFlow
Anonymous.
[434]
UnSAMFlow
Accepted by CVPR 2024
[435]
UnsupSimFlow
Unsupervised Learning of Optical Flow with Deep Feature Similarity, ECCV 2020
[436]
VCN
Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019
[437]
VCN+LCV
Taihong Xiao, Jinwei Yuan, Deqing Sun, Qifei Wang, Xin-Yu Zhang, Kehan Xu, Ming-Hsuan Yang. Learnable Cost Volume using the Cayley Representation, ECCV 2020
[438]
VCN-OER
Anonymous.
[439]
VCN-WARP
Anonymous.
[440]
VCN_RVC
Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019
[441]
ViCo_VideoFlow_MOF
[442]
VideoFlow-BOF
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, ICCV 2023
[443]
VideoFlow-MOF
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, ICCV 2023
[444]
WKSparse
[445]
WLIF-Flow
Z. Tu, R. Poppe, and R. C. Veltkamp. Weighted local intensity fusion method for variational optical flow estimation.Pattern Recognition,2015
[446]
WOLF_ROB
[447]
WRTflow
We proposed a weighted regularization transform to reduce the influence of illumination variations of optical flow estimation. We submit it to TCSVT.
[448]
Win-Win
Anonymous. Win-Win: Training High-Resolution Vision Transformers from Two Windows ICLR 2024 submission
[449]
YOIO
Anonymous.
[450]
ZZZ
Anonymous.
[451]
cascade
Anonymous.
[452]
ce_skii_skii
Anonymous.
[453]
ce_v214
Anonymous.
[454]
efficent_OF_test0
Anonymous.
[455]
flowformer_val
Anonymous.
[456]
flownetnew
[457]
htjnewfull
Anonymous. a
[458]
htjwarp2
Anonymous. htjwarp2
[459]
less_iter_fine
Anonymous. optimization with fewer iterations used.
[460]
less_iteration
Anonymous.
[461]
mask
Anonymous. mask
[462]
metaFlow
Anonymous.
[463]
pwc_xx
[464]
raft-jm
Anonymous. mix raft_test
[465]
ricom20201202
Anonymous.
[466]
risc
Anonymous.
[467]
sdex00
Anonymous.
[468]
sdex001
Anonymous.
[469]
submission5367
Anonymous.
[470]
testS
[471]
tfFlowNet2
Anonymous.
[472]
tfFlowNet2+GLR
Anonymous.
[473]
vcn+MSDRNet
Anonymous. None