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

Return to numerical results table

- Perturbed Market 3, EPE matched = 0.704
- Perturbed Shaman 1, EPE matched = 0.982
- Ambush 1, EPE matched = 13.480
- Ambush 3, EPE matched = 5.444
- Bamboo 3, EPE matched = 0.672
- Cave 3, EPE matched = 5.831
- Market 1, EPE matched = 3.060
- Market 4, EPE matched = 16.870
- Mountain 2, EPE matched = 0.523
- Temple 1, EPE matched = 1.062
- Tiger, EPE matched = 0.603
- Wall, EPE matched = 3.500

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 |

- 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

- A-A Anonymous.

- ADLAB-PRFlow Anonymous.

- ADW Anonymous.

- ADW-Net Anonymous. ADW-Net，20201024 submit

- AGF-Flow Anonymous. AGF

- AGIF+OF Anonymous. Signal Processing 2015

- ALNF Anonymous. ALNF

- 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.

- 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.

- 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.

- AVG_FLOW_ROB Average flow field of ROB2018 training set. No image information used!

- AggregFlow D. Fortun, P. Bouthemy and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. CVIU 2016

- AnisoHuber.L1 M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009.

- AnyFlow Anonymous. PAMI pending review

- AtrousFlow Anonymous. Real-time dense optical flow using CUDA

- AugFNG_ROB Anonymous.

- AutoScaler+ Anonymous. AutoScaler+

- BASELINE-Mean

- BASELINE-zero

- 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

- 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

- Back2FutureFlow_UFO J. Janai, F. Güney, A. Ranjan, M. Black and A. Geiger. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. ECCV, 2018.

- C-2px Anonymous.

- C-RAFT_RVC RVC 2020 submission.

- CNet Anonymous.

- COF H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.

- COF_2019 H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.

- COMBO Anonymous.

- CPM-Flow Yinlin Hu, Rui Song, Yunsong Li. Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.

- CPM2 Coarse-to-fine PatchMatch for dense correspondence, Yunsong Li, Yinlin Hu, Rui Song, Peng Rao, Yangli Wang, submitted to T-CSVT

- CPM_AUG Anonymous. CVPR 18 submission #1939

- CPNFlow Conditional Prior Networks for Optical Flow, Yanchao Yang, Stefano Soatto; The European Conference on Computer Vision (ECCV), 2018, pp. 271-287

- CRAFT Anonymous. Cross-Attentional Flow Transformer

- CVPR-1235 Anonymous.

- Channel-Flow L. Sevilla-Lara, D. Sun, E. Learned-Miller, and M. Black. Optical Flow Estimation with Channel Constancy. ECCV, pages 423-438, 2014.

- Classic++ D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.

- Classic+NL D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.

- Classic+NL-fast D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.

- 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.

- 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.

- CompactFlow Anonymous. ICCV submission.

- CompactFlow-woscv Anonymous.

- ComponentFusion Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.

- ContFusion M. Stoll, D. Maurer, and A. Bruhn. Variational Large Displacement Optical Flow without Feature Matches. EMMCVPR 2017.

- ContinualFlow_ROB Michal Neoral, Jan Šochman and Jiří Matas. Continual Occlusions and Optical Flow Estimation, ACCV 2018

- CosTR Anonymous.

- DA_opticalflow Anonymous.

- DCFlow Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.

- 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.

- 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.

- DCVNet Anonymous. 0.014s with a GTX 1080ti GPU.

- DDCNet_B1_ft-sintel DDCNet B1 finetuned on Sintel

- DDCNet_Multires_ft_sintel DDCNet Multires fine tuned on Sintel

- DDCNet_stacked2 Anonymous. two times down-sampling of feature maps, 128 filters each layer, fine-tuned on sintel

- 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

- 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

- 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

- 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

- DICL-Flow Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)

- DICL-Flow+ Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)

- DICL_update Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)

- DIP Anonymous. Deep Inverse Patch Match for High-Resolution Optical Flow

- DIP-Flow D. Maurer, M. Stoll, A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.

- 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.

- DMF_ROB Baseline submission for ROB flow challenge: Deepflow with Deepmatching as prior

- DPCTF Anonymous. Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow

- 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.

- 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.

- Data-Flow Anonymous. CSAD data cost + second order smoothness

- Deep+R B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015

- Deep-EIP Anonymous. End-2-End learning for energy based inpainting of optical flow. ACCV submission 572

- DeepDiscreteFlow F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.

- DeepFlow P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.

- DeepFlow2 J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.

- DefFlowP Anonymous.

- Deformable_RAFT Anonymous. RAFT with deformable

- Devon Anonymous. CVPR submission #1906

- DictFlowS Anonymous.

- DiscreteFlow M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015

- DiscreteFlow+OIR D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.

- DistillFlow Anonymous. Unsupervise result

- DistillFlow+ft Anonymous. Supervised result.

- EPIflow Deep Epipolar Flow

- 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.

- EPPM L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.

- ER-FLOW2 Anonymous. Adjusted ERFlow

- ERFlow Anonymous.

- EgFlow-cl Anonymous. edge-guided, small parameter optical flow network based on CNN

- EpicFlow J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.

- F2PD_JJN Anonymous. With Deformable Convolutional Networks

- F3-MPLF Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh: Filter Flow made Practical: Massively Parallel and Lock-Free -- CVPR 2017

- 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

- FAOP-Flow Anonymous.

- 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

- FDFlowNet Lingtong Kong and Jie Yang. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network, ICIP 2020.

- FF++_ROB Accurate matching and robust interpolation. KITTI parameters as given by the paper. Edge detector trained on KITTI + Sintel + BSDS500.

- FGI Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016

- FPCR-Net Anonymous.

- FPCR-Net2 Anonymous.

- FastFlow Anonymous.

- FastFlow2 Anonymous.

- FastFlowNet Lingtong Kong, Chunhua Shen and Jie Yang. FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation, ICRA 2021.

- FastFlowNet-ft+ Anonymous.

- Flow1D Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong. High-Resolution Optical Flow from 1D Attention and Correlation. ICCV 2021, Oral

- FlowFields C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.

- FlowFields+ C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. T-PAMI.

- FlowFields++ R. Schuster, C. Bailer, O. Wasenmüller, D. Stricker. FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018

- FlowFieldsCNN C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.

- FlowNet2 Anonymous. CVPR Submission #900

- FlowNet2-ft-sintel Anonymous. CVPR Submission #900

- FlowNetADF Lightweight Probabilistic Deep Networks

- FlowNetC+OFR Anonymous.

- FlowNetC+ft+v Anonymous. ICCV sumbmission 235

- FlowNetC-MD Anonymous.

- FlowNetProbOut Lightweight Probabilistic Deep Networks

- FlowNetS+ft+v Anonymous. ICCV submission 235

- FlowSAC_dcf Anonymous.

- FlowSAC_ff Anonymous

- Flownet2-IA Anonymous. Flownet2 combining with illumination adjustment

- Flownet2-IAER Anonymous. Flownet2 combining with illumination adjustment and edge refinement

- FrequencyFlow FrequencyFlow+SegPM

- FullFlow Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.

- FullFlow+KF W. Bao, Y. Xiao, L. Chen, and Z. Gao. Kalmanflow: Efficient Kalman Filtering for Video Optical Flow. ICIP 2018.

- GCA-Net Anonymous.

- GCA-Net-ft+ Anonymous. finetune GCA-Net with a better data augmentation method

- GMA Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley. Learning to Estimate Hidden Motions with Global Motion Aggregation, ICCV 2021.

- GMFlowNet Anonymous. Global Matching Flow Network. No warm up.

- GPNet Anonymous.

- 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.

- GlobalPatchCollider Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016

- GroundTruth

- Grts-Flow-V2 En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT

- H+S_ROB Baseline submission for the ROB flow challenge: Horn-Schunck Optical Flow with a Multi-Scale Strategy

- H+S_RVC RVC 2020 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann.

- H-1px Anonymous.

- H-v3 Anonymous.

- 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

- HCOF+multi R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.

- HD3-Flow Zhichao Yin, Trevor Darrell, Fisher Yu. Hierarchical Discrete Distribution Decomposition for Match Density Estimation (CVPR 2019)

- HD3-Flow-OER Anonymous.

- HD3F+MSDRNet Anonymous. HD3F+MSDRNet

- HMFlow HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects

- HSVFlow Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations

- Horn+Schunck A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.

- ICALD M. Stoll, D. Maurer, S. Volz and A. Bruhn. Illumination-Aware Large Displacement Optical Flow. EMMCVPR 2017.

- IHBPFlow Anonymous.

- IIOF-NLDP Dinh-Hoan Trinh, Walter Blondel, and Christian Daul. A General Form of Illumination-Invariant Descriptors in Variational Optical Flow Estimation. ICIP 2017.

- IOFPL-CVr8-ft Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)

- IOFPL-ft Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)

- IPOL_Brox J. Sánchez, N. Monzón, and A. Salgado, Robust Optical Flow Estimation, Image Processing On Line (IPOL), 3 (2013), pp. 252–270.

- IRR-PWC Junhwa Hur and Stefan Roth. Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019

- IRR-PWC-OER Anonymous.

- IRR-PWC_RVC RVC 2020 submission

- InterpoNet_cpm S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.

- InterpoNet_df S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.

- InterpoNet_dm S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.

- InterpoNet_ff S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.

- JOF Anonymous.

- L0-norm-Flow2 OPL

- L0-normFlow OPC

- L2L-Flow-ext Anonymous.

- L2L-Flow-ext-warm Anonymous.

- LCT-Flow Anonymous. LCT-Flow

- LCT-Flow2 Anonymous. LCT-Flow2

- LDOF T. Brox, J. Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3): 500-513, 2011.

- LMM Anonymous.

- LSM_FLOW_RVC LSM: Learning Subspace Minimization for Low-level Vision for RVC2020

- Lavon Anonymous.

- LiteFlowNet Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018.

- LiteFlowNet2 Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020.

- LiteFlowNet3 Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.

- LiteFlowNet3-S Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.

- LocalLayering D. Sun, C. Liu, and H. Pfister. Local Layering for Joint Motion Estimation and Occlusion Detection. CVPR 2014.

- M-1px Anonymous.

- MDFlow Anonymous.

- MDP-Flow2 L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012.

- MF2C Anonymous

- 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)

- MFFC Anonymous

- MFR Anonymous. Motion Feature Recovery

- 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.

- MPIF Anonymous. multi-level interpolation for optical flow estimation

- MR-Flow J. Wulff, L. Sevilla-Lara, M. J. Black: Optical Flow in Mostly Rigid Scenes. CVPR 2017.

- 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).

- 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).

- MirrorFlow Junhwa Hur and Stefan Roth. MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017

- MixSup Anonymous.

- Model_model Anonymous. this is a model

- NASFlow Anonymous.

- NASFlow-PWC Anonymous.

- NASFlow-RAFT Anonymous.

- NLTGV-SC R. Ranftl, K. Bredies, T. Pock. Non-Local Total Generalized Variation for Optical Flow Estimation, ECCV 2014

- NNF-Local Anonymous. Large Displacement Optical Flow from Nearest Neighbor Fields

- NccFlow Anonymous.

- OAR-Flow D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.

- OAS-Net Lingtong Kong, Xiaohang Yang and Jie Yang. OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow, ICASSP 2021.

- OF-OEF Anonymous. Optical flow estimation combining with objects edge features

- OFTR Anonymous.

- OF_OCC_LD V. Lazcano, L. Garrido, C. Ballester. Jointly Optical flow and Occlusion Estimation for images with Large Displacements.

- OIFlow occlusion-inpainting Flow

- OatNet01 Anonymous.

- PCA-Flow J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.

- PCA-Layers J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.

- PGM-C Anonymous

- PH-Flow Jiaolong Yang and Hongdong Li. Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015

- 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

- PPAC-HD3 Anne S. Wannenwetsch, Stefan Roth. Probabilistic Pixel-Adaptive Refinement Networks. CVPR 2020.

- PPM Parametric PatchMatch, Fangjun Kuang, master thesis, 2017

- PRAFlow_RVC Anonymous.

- PRichFlow Anonymous.

- PST Anonymous. ACCV2018 submission #1195

- 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.

- 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

- 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.

- 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.

- PWC-Net-OER Anonymous.

- 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.

- PatchBatch+Inter T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.

- PatchBatch-CENT+SD Anonymous.

- PatchWMF-OF Zhigang Tu, Coert Van Gemeren and Remco C. Veltkamp. Improved Color Patch Similarity Measure Based Weighted Median Filter. ACCV2014

- PosetOptimization Anonymous.

- ProFlow D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.

- ProFlow_ROB D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018. (ROB setting)

- ProbFlowFields Anne S. Wannenwetsch, Margret Keuper, Stefan Roth. ProbFlow: Joint Optical Flow and Uncertainty Estimation. ICCV 2017.

- Pwc_ps Anonymous.

- RAFT Zachary Teed and Jia Deng. RAFT: Recurrent All-Pairs Field Transforms for Optical Flow, ECCV 2020.

- 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.

- RAFT+ConvUp Anonymous.

- 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

- RAFT+NCUP Normalized Convolution Upsampling for Refined Optical Flow Estimation

- RAFT+OBS Anonymous. We change the dataset to train RAFT.

- 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

- RAFT-GT Anonymous. CVPR 2021 submission

- RAFT-GT-ft Anonymous. CVPR 2021 submission

- 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.

- RAFT_Chairs_Things Anonymous.

- RAFTv1-OER-2-view Anonymous.

- RAFTv2-OER-2-view Anonymous.

- RAFTv2-OER-warm-start Anonymous.

- 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.

- RAFTwarm+OBS Anonymous. train the RAFT network on new our datasets and applying warmup

- RC-LSTM-1dir Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)

- RC-LSTM-4dir Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)

- RFPM Anonymous.

- RGBFlow Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations

- RICBCDN Anonymous.

- RLOF_DENSE Jonas Geistert, Tobias Senst and Thomas Sikora, Robust Local Optical Flow: Dense Motion Vector Field Interpolation, PCS 2016

- 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.

- ResPWCR_ROB Anonymous.

- RicFlow Yinlin Hu, Yunsong Li, Rui Song. Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.

- RichFlow-ft Anonymous.

- RichFlow-ft-fnl Anonymous. final pass version

- S2D-Matching M. Leordeanu, A. Zanfir, C. Sminchisescu. Locally Affine Sparse-to-Dense Matching for Motion and Occlusion estimation. ICCV 2013

- S2F-IF Yanchao Yang, Stefano Soatto. S2F: Slow-To-Fast Interpolator Flow. CVPR 2017.

- 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.

- SAnet Anonymous.

- SBFlow Fast Optical Flow Estimation Based on the Split Bregman Method. TCSVT 2016.(Matlab code is available.)

- SCV Anonymous. CVPR 2021 submission #3221

- SDFlow Anonymous.

- SENSE Anonymous. TBA

- SFL Jingchun Cheng, Yi-Hsuan, Tsai, Shengjin Wang, Ming-Hsuan Yang. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow. ICCV 2017

- SJTU_PAMI418

- SMURF Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski. SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping, CVPR 2021

- 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

- 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.

- 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

- 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

- STC-Flow Anonymous.

- STDC-Flow STDC-Flow: large displacement flow field estimation using similarity transformationbased dense correspondence, IET Computer Vision, 2020

- 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)

- SVFilterOh Mohamed A. Helala, Faisal Z. Qureshi, Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering, CRV 2017

- ScopeFlow Aviram Bar-Haim and Lior Wolf. ScopeFlow: Dynamic Scene Scoping for Optical Flow, CVPR 2020.

- SegFlow-CNN SegFlow-CNN

- SegFlow113 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=11,d1=3)(Matlab code is available.)

- SegFlow153 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=15,d1=3)(Matlab code is available.)

- SegFlow193 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=19,d1=3)(Matlab code is available.)

- SegFlow33 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=3, d1=3)(Matlab code is available.)

- SegFlow73 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=7,d1=3)(Matlab code is available.)

- SegPM+Interpolation SegPM+Interpolation

- SelFlow Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)

- SelFlow Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)

- Semantic_Lattice Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler, Stefan Roth. Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice. GCPR 2019.

- SeparableFlow Anonymous. Described in the paper.

- SfM-PM D. Maurer, N. Marniok, B. Goldluecke, A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation. ECCV 2018.

- 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)

- SparseFlow Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015

- SparseFlowFused Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015

- Steered-L1 Anonymous.

- 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.

- 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.

- TF+OFM R. Kennedy and C.J. Taylor. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.

- TIMCflow Fei Yang, Yongmei Cheng, Joost Van de Weijer, Mikhail G. Mozerov. 'Improved Discrete Optical Flow Estimation with Triple Image Matching Cost', IEEE Access

- TV-L1 Anonymous. It is a well-known model that uses TV-L1 (coupled) as the regularization term and an L1 data term.

- TV-L1+EM V. Lazcano. EXHAUSTIVE MATCHING EMPIRICAL STUDY FOR IMPROVING THE MOTION FIELD ESTIMATION

- TV-Wavelet-Flow SegPM+TV-Wavelet-Flow(Matlab code is available.)

- TVL1_BWMFilter Balanced Weighted Median Filter and Bilateral Filter.

- TVL1_LD_GF V. Lazcano. TVL1 to handle large displacements using gradient patches. Parameter where optimized using PSO.

- TVL1_ROB Baseline submission for the robust vision flow challenge: TV-L1 Optical Flow Estimation

- TVL1_RVC RVC 2020 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo

- 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)

- UPFlow

- UnDAF-RAFT Anonymous.

- UnFlow Anonymous.

- UnsupSimFlow Unsupervised Learning of Optical Flow with Deep Feature Similarity, ECCV 2020

- VCN Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019

- 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

- VCN-OER Anonymous.

- VCN-WARP Anonymous.

- VCN_RVC Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019

- WKSparse

- WLIF-Flow Z. Tu, R. Poppe, and R. C. Veltkamp. Weighted local intensity fusion method for variational optical flow estimation.Pattern Recognition,2015

- WOLF_ROB

- WRTflow We proposed a weighted regularization transform to reduce the influence of illumination variations of optical flow estimation. We submit it to TCSVT.

- ZZZ Anonymous.

- consflow-mv Anonymous.

- flownetnew

- htjnewfull Anonymous. a

- htjwarp2 Anonymous. htjwarp2

- mask Anonymous. mask

- metaFlow Anonymous.

- prnflow

- pwc_xx

- raft-jm Anonymous. mix raft_test

- ricom20201202 Anonymous.

- risc Anonymous.

- testS

- tfFlowNet2 Anonymous.

- tfFlowNet2+GLR Anonymous.

- vcn+MSDRNet Anonymous. None