Visual Results by Method
Results for methods appear here after users upload them and approve them for public display.
Return to numerical results table
-
Perturbed Market 3, d10-60 = 0.676
-
Perturbed Shaman 1, d10-60 = 0.203
-
Ambush 1, d10-60 = 4.718
-
Ambush 3, d10-60 = 2.246
-
Bamboo 3, d10-60 = 0.134
-
Cave 3, d10-60 = 0.746
-
Market 1, d10-60 = 0.529
-
Market 4, d10-60 = 2.519
-
Mountain 2, d10-60 = 0.112
-
Temple 1, d10-60 = 0.365
-
Tiger, d10-60 = 0.285
-
Wall, d10-60 = 1.074
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]
- CVE-RAFT ljp
-
[67]
- CVEFlow ljp
-
[68]
- CVENG22+Epic Anonymous.
-
[69]
- CVENG22+RIC Anonymous.
-
[70]
- CVPR-1235 Anonymous.
-
[71]
- CasFlow Anonymous. CasFlow
-
[72]
- Channel-Flow L. Sevilla-Lara, D. Sun, E. Learned-Miller, and M. Black. Optical Flow Estimation with Channel Constancy. ECCV, pages 423-438, 2014.
-
[73]
- Classic++ D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
-
[74]
- Classic+NL D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
-
[75]
- Classic+NL-fast D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR, pages 2432-2439, 2010.
-
[76]
- 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.
-
[77]
- 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.
-
[78]
- Compact-Unified-Model A model for two motion tasks: optical flow and rectified stereo-matching
-
[79]
- CompactFlow Anonymous. ICCV submission.
-
[80]
- CompactFlow-woscv Anonymous.
-
[81]
- CompactFlowNet Anonymous.
-
[82]
- ComponentFusion Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.
-
[83]
- ContFusion M. Stoll, D. Maurer, and A. Bruhn. Variational Large Displacement Optical Flow without Feature Matches. EMMCVPR 2017.
-
[84]
- ContinualFlow_ROB Michal Neoral, Jan Šochman and Jiří Matas. Continual Occlusions and Optical Flow Estimation, ACCV 2018
-
[85]
- CroCo-Flow CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. Weinzaepfel et al. ICCV 2023.
-
[86]
- CrossFlow Anonymous.
-
[87]
- DA_opticalflow Anonymous.
-
[88]
- DC-RAFT Anonymous.
-
[89]
- DCFlow Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
-
[90]
- 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.
-
[91]
- 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.
-
[92]
- DCN-Flow DCN-Flow
-
[93]
- DCVNet Anonymous. 0.03s with a GTX 1080ti GPU.
-
[94]
- DDCNet_B0_tf_sintel Anonymous. DDCNet_B0 fine-tuned on Sintel
-
[95]
- DDCNet_B1_ft-sintel DDCNet B1 finetuned on Sintel
-
[96]
- DDCNet_Multires_ft_sintel DDCNet Multires fine tuned on Sintel
-
[97]
- DDCNet_Stacked Anonymous. Two blocks of simple DDCNet
-
[98]
- DDCNet_stacked2 Anonymous. two times down-sampling of feature maps, 128 filters each layer, fine-tuned on sintel
-
[99]
- 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
-
[100]
- DDVM Anonymous. NeurIPS 2023 submission #14395
-
[101]
- DEQ-Flow-H Deep Equilibrium Optical Flow Estimation
-
[102]
- 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
-
[103]
- 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
-
[104]
- 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
-
[105]
- DICL-Flow Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
-
[106]
- DICL-Flow+ Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
-
[107]
- DICL_update Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
-
[108]
- DIP Anonymous. Deep Inverse Patchmatch for High-Resolution Optical Flow
-
[109]
- DIP-Flow D. Maurer, M. Stoll, A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
-
[110]
- 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.
-
[111]
- DMF_ROB Baseline submission for ROB flow challenge: Deepflow with Deepmatching as prior
-
[112]
- DPCTF Anonymous. Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow
-
[113]
- 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.
-
[114]
- 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.
-
[115]
- 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.
-
[116]
- Data-Flow Anonymous. CSAD data cost + second order smoothness
-
[117]
- Deep+R B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015
-
[118]
- Deep-EIP Anonymous. End-2-End learning for energy based inpainting of optical flow. ACCV submission 572
-
[119]
- DeepDiscreteFlow F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
-
[120]
- DeepFlow P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.
-
[121]
- DeepFlow2 J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.
-
[122]
- DefFlowP Anonymous.
-
[123]
- Deformable_RAFT Anonymous. RAFT with deformable
-
[124]
- Devon Anonymous. CVPR submission #1906
-
[125]
- DictFlowS Anonymous.
-
[126]
- DiscreteFlow M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015
-
[127]
- DiscreteFlow+OIR D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.
-
[128]
- DistillFlow Anonymous. Unsupervise result
-
[129]
- DistillFlow+ft Anonymous. Supervised result.
-
[130]
- EFlow-M Anonymous.
-
[131]
- EFlow-M-tile Anonymous.
-
[132]
- EMD-L Anonymous. Anonymous
-
[133]
- EMD-M Anonymous. Anonymous
-
[134]
- EMD-OER Anonymous.
-
[135]
- EMSFlow Anonymous.
-
[136]
- EPIflow Deep Epipolar Flow
-
[137]
- 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.
-
[138]
- EPPM L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.
-
[139]
- ER-FLOW2 Anonymous. Adjusted ERFlow
-
[140]
- ERFlow Anonymous.
-
[141]
- EdgeFlow Anonymous.
-
[142]
- EgFlow-cl Anonymous. edge-guided, small parameter optical flow network based on CNN
-
[143]
- EpicFlow J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.
-
[144]
- ErrorMatch-GMA Anonymous. tba
-
[145]
- ErrorMatch-RAFT Anonymous. tba
-
[146]
- F2PD_JJN Anonymous. With Deformable Convolutional Networks
-
[147]
- F3-MPLF Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh: Filter Flow made Practical: Massively Parallel and Lock-Free -- CVPR 2017
-
[148]
- 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
-
[149]
- FAOP-Flow Anonymous.
-
[150]
- 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
-
[151]
- FCTR Anonymous.
-
[152]
- FCTR-m Anonymous.
-
[153]
- FDFlowNet Lingtong Kong and Jie Yang. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network, ICIP 2020.
-
[154]
- FF++_ROB Accurate matching and robust interpolation. KITTI parameters as given by the paper. Edge detector trained on KITTI + Sintel + BSDS500.
-
[155]
- FGI Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016
-
[156]
- FPCR-Net Anonymous.
-
[157]
- FPCR-Net2 Anonymous.
-
[158]
- FTGAN The Application of Counter Learning of Reverse Residual Attention in Optical Flow Estimation
-
[159]
- FastFlow Anonymous.
-
[160]
- FastFlow2 Anonymous.
-
[161]
- FastFlowNet Lingtong Kong, Chunhua Shen and Jie Yang. FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation, ICRA 2021.
-
[162]
- FastFlowNet-ft+ Anonymous.
-
[163]
- Flow1D Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong. High-Resolution Optical Flow from 1D Attention and Correlation. ICCV 2021, Oral
-
[164]
- Flow1D-OER Anonymous.
-
[165]
- FlowDiffuser FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models, CVPR 2024
-
[166]
- FlowFields C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.
-
[167]
- FlowFields+ C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. T-PAMI.
-
[168]
- FlowFields++ R. Schuster, C. Bailer, O. Wasenmüller, D. Stricker. FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018
-
[169]
- FlowFieldsCNN C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
-
[170]
- FlowFormer FlowFormer: A Transformer Architecture for Optical Flow
-
[171]
- FlowFormer++ FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
-
[172]
- FlowNet2 Anonymous. CVPR Submission #900
-
[173]
- FlowNet2-ft-sintel Anonymous. CVPR Submission #900
-
[174]
- FlowNetADF Lightweight Probabilistic Deep Networks
-
[175]
- FlowNetC+OFR Anonymous.
-
[176]
- FlowNetC+ft+v Anonymous. ICCV sumbmission 235
-
[177]
- FlowNetC-MD Anonymous.
-
[178]
- FlowNetProbOut Lightweight Probabilistic Deep Networks
-
[179]
- FlowNetS+ft+v Anonymous. ICCV submission 235
-
[180]
- FlowSAC_dcf Anonymous.
-
[181]
- FlowSAC_ff Anonymous
-
[182]
- Flownet2-IA Anonymous. Flownet2 combining with illumination adjustment
-
[183]
- Flownet2-IAER Anonymous. Flownet2 combining with illumination adjustment and edge refinement
-
[184]
- FullFlow Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
-
[185]
- FullFlow+KF W. Bao, Y. Xiao, L. Chen, and Z. Gao. Kalmanflow: Efficient Kalman Filtering for Video Optical Flow. ICIP 2018.
-
[186]
- GAFlow Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
-
[187]
- GAFlow-FF Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
-
[188]
- GANFlow Anonymous.
-
[189]
- GCA-Net Anonymous.
-
[190]
- GCA-Net-ft+ Anonymous. finetune GCA-Net with a better data augmentation method
-
[191]
- GMA Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley. Learning to Estimate Hidden Motions with Global Motion Aggregation, ICCV 2021.
-
[192]
- GMA+LCT-Flow GMA+LCT-Flow
-
[193]
- GMA+TCU+aug Anonymous.
-
[194]
- GMA+TCU-aug Anonymous.
-
[195]
- GMA-FS Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
-
[196]
- GMA-base Anonymous. GMA-base
-
[197]
- GMA-two_img Anonymous.
-
[198]
- GMFlow Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao. GMFlow: Learning Optical Flow via Global Matching. CVPR 2022, Oral
-
[199]
- GMFlow+ Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, Andreas Geiger. Unifying Flow, Stereo and Depth Estimation. TPAMI 2023
-
[200]
- GMFlowNet Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou and Dimitris Metaxas. Global Matching with Overlapping Attention for Optical Flow Estimation. CVPR 2022
-
[201]
- GMFlow_RVC GMFlow RVC 2022 submission.
-
[202]
- GPNet Anonymous.
-
[203]
- 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.
-
[204]
- GeoViT
-
[205]
- GlobalPatchCollider Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016
-
[206]
- GroundTruth
-
[207]
- Grts-Flow-V2 En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT
-
[208]
- H+S_ROB Baseline submission for the ROB flow challenge: Horn-Schunck Optical Flow with a Multi-Scale Strategy
-
[209]
- H+S_RVC RVC 2020 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann.
-
[210]
- H-1px Anonymous.
-
[211]
- H-v3 Anonymous.
-
[212]
- 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
-
[213]
- HCOF+multi R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.
-
[214]
- HCVNet Anonymous.
-
[215]
- HD3-Flow Zhichao Yin, Trevor Darrell, Fisher Yu. Hierarchical Discrete Distribution Decomposition for Match Density Estimation (CVPR 2019)
-
[216]
- HD3-Flow-OER Anonymous.
-
[217]
- HD3F+MSDRNet Anonymous. HD3F+MSDRNet
-
[218]
- HMAFlow Anonymous.
-
[219]
- HMFlow HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
-
[220]
- HSVFlow Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
-
[221]
- Horn+Schunck A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.
-
[222]
- ICALD M. Stoll, D. Maurer, S. Volz and A. Bruhn. Illumination-Aware Large Displacement Optical Flow. EMMCVPR 2017.
-
[223]
- ICIMG-Net Anonymous.
-
[224]
- IHBPFlow Anonymous.
-
[225]
- IIOF-NLDP Dinh-Hoan Trinh, Walter Blondel, and Christian Daul. A General Form of Illumination-Invariant Descriptors in Variational Optical Flow Estimation. ICIP 2017.
-
[226]
- IOFPL-CVr8-ft Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
-
[227]
- IOFPL-ft Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
-
[228]
- IPOL_Brox J. Sánchez, N. Monzón, and A. Salgado, Robust Optical Flow Estimation, Image Processing On Line (IPOL), 3 (2013), pp. 252–270.
-
[229]
- IRR-PWC Junhwa Hur and Stefan Roth. Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019
-
[230]
- IRR-PWC-OER Anonymous.
-
[231]
- IRR-PWC_RVC RVC 2020 submission
-
[232]
- ISDAFlow Anonymous. ISDAFlow: Self-supervised Infusion of Segmented and Depth Anything Models into Optical Flow
-
[233]
- ISDAFlowRAFT Anonymous. ISDAFlow+RAFT
-
[234]
- IWarp Anonymous.
-
[235]
- InterpoNet_cpm S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
-
[236]
- InterpoNet_df S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
-
[237]
- InterpoNet_dm S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
-
[238]
- InterpoNet_ff S. Zweig, L Wolf. InterpoNet, A brain inspired neural network for optical flow dense interpolation. CVPR 2017.
-
[239]
- 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.
-
[240]
- KPA-Flow Anonymous. Learning Optical Flow with Kernel Patch Attention, CVPR 2022.
-
[241]
- L2L-Flow-ext Anonymous.
-
[242]
- L2L-Flow-ext-warm Anonymous.
-
[243]
- LDOF T. Brox, J. Malik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3): 500-513, 2011.
-
[244]
- LLA-FLOW+GMA LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation. ICIP 2023.
-
[245]
- LLA-Flow LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation. ICIP 2023.
-
[246]
- LSHRAFT Anonymous.
-
[247]
- LSM_FLOW_RVC LSM: Learning Subspace Minimization for Low-level Vision for RVC2020
-
[248]
- Lavon Anonymous.
-
[249]
- LiteFlowNet Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018.
-
[250]
- LiteFlowNet2 Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020.
-
[251]
- LiteFlowNet3 Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.
-
[252]
- LiteFlowNet3-S Tak-Wai Hui and Chen Change Loy. LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020.
-
[253]
- LocalLayering D. Sun, C. Liu, and H. Pfister. Local Layering for Joint Motion Estimation and Occlusion Detection. CVPR 2014.
-
[254]
- M-1px Anonymous.
-
[255]
- 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.
-
[256]
- MCPFlow_RVC RVC 2022 submission
-
[257]
- MDFlow Lingtong Kong and Jie Yang. MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation, TCSVT 2022.
-
[258]
- MDFlow-Fast Lingtong Kong and Jie Yang. MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation, TCSVT 2022.
-
[259]
- MDP-Flow2 L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012.
-
[260]
- MEMFOF V. Bargatin, E. Chistov, A. Yakovenko, and D. Vatolin. "MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation", ICCV 2025.
-
[261]
- MEMFOF-L V. Bargatin, E. Chistov, A. Yakovenko, and D. Vatolin. "MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation", ICCV 2025.
-
[262]
- MEMFOF-XL V. Bargatin, E. Chistov, A. Yakovenko, and D. Vatolin. "MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation", ICCV 2025. [32 iterations]
-
[263]
- MF2C Anonymous
-
[264]
- MFCFlow Anonymous.
-
[265]
- 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)
-
[266]
- MFFC Anonymous
-
[267]
- MFR Anonymous. Motion Feature Recovery
-
[268]
- MFlow Anonymous.
-
[269]
- 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.
-
[270]
- MMAFlow Anonymous.
-
[271]
- MMAFlow Anonymous.
-
[272]
- MODFlow_ft Anonymous. A Matching-Guided and Occlusion-Aware Decoder for Accurate Optical Flow Estimation
-
[273]
- MPIF Anonymous. multi-level interpolation for optical flow estimation
-
[274]
- MR-Flow J. Wulff, L. Sevilla-Lara, M. J. Black: Optical Flow in Mostly Rigid Scenes. CVPR 2017.
-
[275]
- MRDFlow Anonymous.
-
[276]
- MS_RAFT Azin Jahedi, Lukas Mehl, Marc Rivinius, Andrés Bruhn, "Multi-scale RAFT: Combining Hierarchical Concepts for Learning-based Optical Flow Estimation", ICIP 2022
-
[277]
- MS_RAFT+_RVC Azin Jahedi, Maximilian Luz, Marc Rivinius, Lukas Mehl, and Andrés Bruhn: "MS-RAFT+: High Resolution Multi-Scale RAFT", IJCV 2023
-
[278]
- MVFlow Anonymous.
-
[279]
- 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).
-
[280]
- 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).
-
[281]
- MatchFlow_GMA Qiaole Dong, Chenjie Cao, Yanwei Fu. Rethinking Optical Flow from Geometric Matching Consistent Perspective. CVPR 2023.
-
[282]
- MatchFlow_GMA_2-view Anonymous.
-
[283]
- MatchFlow_RAFT Qiaole Dong, Chenjie Cao, Yanwei Fu. Rethinking Optical Flow from Geometric Matching Consistent Perspective. CVPR 2023.
-
[284]
- MeFlow Anonymous.
-
[285]
- MemFlow Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
-
[286]
- MemFlow-T Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
-
[287]
- MemoFlow Anonymous.
-
[288]
- MirrorFlow Junhwa Hur and Stefan Roth. MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017
-
[289]
- MoEFlow
-
[290]
- MobileFlow Anonymous. Optical flow estimation framework using an enhanced MobileNet and Transformer with a large-kernel upsampler.
-
[291]
- Model_model Anonymous. this is a model
-
[292]
- MotionFlow Anonymous. Z. Wang, C. Zhang, C. Zhen, H. Chen, L. Ge and K. Lu: MotionFlow: Joint Motion Priors and Appearance Enhancement for High-Accuracy Optical Flow Estimation. ICASSP 2025 .
-
[293]
- MotionFlow+ Anonymous.
-
[294]
- Muun-RAFT
-
[295]
- NASFlow Anonymous.
-
[296]
- NASFlow-PWC Anonymous.
-
[297]
- NASFlow-RAFT Anonymous.
-
[298]
- NLTGV-SC R. Ranftl, K. Bredies, T. Pock. Non-Local Total Generalized Variation for Optical Flow Estimation, ECCV 2014
-
[299]
- NNF-Local Anonymous. Large Displacement Optical Flow from Nearest Neighbor Fields
-
[300]
- NccFlow Anonymous.
-
[301]
- OADFlow Anonymous.
-
[302]
- OAR-Flow D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.
-
[303]
- OAS-Net Lingtong Kong, Xiaohang Yang and Jie Yang. OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow, ICASSP 2021.
-
[304]
- OCRAFT
-
[305]
- OF-OEF Anonymous. Optical flow estimation combining with objects edge features
-
[306]
- OFM Anonymous.
-
[307]
- OFM Anonymous.
-
[308]
- OF_OCC_LD V. Lazcano, L. Garrido, C. Ballester. Jointly Optical flow and Occlusion Estimation for images with Large Displacements.
-
[309]
- OIFlow occlusion-inpainting Flow
-
[310]
- OM_CRAFT Anonymous.
-
[311]
- OM_GMA Anonymous.
-
[312]
- OM_GMFlow Anonymous.
-
[313]
- OM_RAFT Anonymous. Used method with channel shuffle to enhance feature.
-
[314]
- OPPFlow Anonymous.
-
[315]
- OatNet01 Anonymous.
-
[316]
- PCA-Flow J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
-
[317]
- PCA-Layers J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
-
[318]
- PGM-C Anonymous
-
[319]
- PH-Flow Jiaolong Yang and Hongdong Li. Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015
-
[320]
- 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
-
[321]
- 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
-
[322]
- 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
-
[323]
- PPAC-HD3 Anne S. Wannenwetsch, Stefan Roth. Probabilistic Pixel-Adaptive Refinement Networks. CVPR 2020.
-
[324]
- PPM Parametric PatchMatch, Fangjun Kuang, master thesis, 2017
-
[325]
- PRAFlow_RVC Anonymous.
-
[326]
- PRichFlow Anonymous.
-
[327]
- PST Anonymous. ACCV2018 submission #1195
-
[328]
- PVTFlow Anonymous.
-
[329]
- 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.
-
[330]
- 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
-
[331]
- 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.
-
[332]
- 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.
-
[333]
- PWC-Net-OER Anonymous.
-
[334]
- 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.
-
[335]
- PWC_acn Anonymous.
-
[336]
- PatchBatch+Inter T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.
-
[337]
- PatchBatch-CENT+SD Anonymous.
-
[338]
- PatchWMF-OF Zhigang Tu, Coert Van Gemeren and Remco C. Veltkamp. Improved Color Patch Similarity Measure Based Weighted Median Filter. ACCV2014
-
[339]
- PosetOptimization Anonymous.
-
[340]
- PredFlow
-
[341]
- PredFlow
-
[342]
- ProFlow D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.
-
[343]
- ProFlow_ROB D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018. (ROB setting)
-
[344]
- ProMotion Anonymous. Anonymous submission
-
[345]
- ProbFlowFields Anne S. Wannenwetsch, Margret Keuper, Stefan Roth. ProbFlow: Joint Optical Flow and Uncertainty Estimation. ICCV 2017.
-
[346]
- ProtoFormer
-
[347]
- ProtoFormer Anonymous.
-
[348]
- Pwc_ps Anonymous.
-
[349]
- RAFT Zachary Teed and Jia Deng. RAFT: Recurrent All-Pairs Field Transforms for Optical Flow, ECCV 2020.
-
[350]
- 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.
-
[351]
- RAFT+ConvUp Anonymous.
-
[352]
- RAFT+LCT-Flow RAFT+LCT-Flow
-
[353]
- 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
-
[354]
- RAFT+NCUP Normalized Convolution Upsampling for Refined Optical Flow Estimation
-
[355]
- RAFT+OBS Anonymous. We change the dataset to train RAFT.
-
[356]
- 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
-
[357]
- RAFT-CF
-
[358]
- RAFT-DFlow Anonymous.
-
[359]
- RAFT-FS Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
-
[360]
- RAFT-GT Anonymous. CVPR 2021 submission
-
[361]
- RAFT-GT-ft Anonymous. CVPR 2021 submission
-
[362]
- RAFT-OCTC Anonymous.
-
[363]
- 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.
-
[364]
- RAFT-VM
-
[365]
- RAFT-base Anonymous. RAFT-base
-
[366]
- RAFT-illumination Anonymous. RAFT-illumination
-
[367]
- RAFT-it D. Sun, C. Herrmann, F. Reda, M. Rubinstein, D. Fleet and W. Freeman: Disentangling Architecture and Training for Optical Flow. ECCV 2022.
-
[368]
- 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.
-
[369]
- RAFT_Chairs_Things Anonymous.
-
[370]
- RAFTv1-OER-2-view Anonymous.
-
[371]
- RAFTv2-OER-2-view Anonymous.
-
[372]
- RAFTv2-OER-warm-start Anonymous.
-
[373]
- 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.
-
[374]
- RAFTwarm+OBS Anonymous. train the RAFT network on new our datasets and applying warmup
-
[375]
- 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.
-
[376]
- RC-LSTM-1dir Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
-
[377]
- RC-LSTM-4dir Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
-
[378]
- RFPM Anonymous.
-
[379]
- RGBFlow Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
-
[380]
- RICBCDN Anonymous.
-
[381]
- RLOF_DENSE Jonas Geistert, Tobias Senst and Thomas Sikora, Robust Local Optical Flow: Dense Motion Vector Field Interpolation, PCS 2016
-
[382]
- 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.
-
[383]
- RPKNet Henrique Morimitsu, Xiaobin Zhu, Xiangyang Ji, and Xu-Cheng Yin. Recurrent Partial Kernel Network for Efficient Optical Flow Estimation. AAAI, 2024.
-
[384]
- ResPWCR_ROB Anonymous.
-
[385]
- RicFlow Yinlin Hu, Yunsong Li, Rui Song. Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.
-
[386]
- RichFlow-ft Anonymous.
-
[387]
- RichFlow-ft-fnl Anonymous. final pass version
-
[388]
- S2D-Matching M. Leordeanu, A. Zanfir, C. Sminchisescu. Locally Affine Sparse-to-Dense Matching for Motion and Occlusion estimation. ICCV 2013
-
[389]
- S2F-IF Yanchao Yang, Stefano Soatto. S2F: Slow-To-Fast Interpolator Flow. CVPR 2017.
-
[390]
- SAG156+RAFT Anonymous. SAG156+RAFT
-
[391]
- 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.
-
[392]
- SAMFlow SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model. Accepted in AAAI 2024.
-
[393]
- SAnet Anonymous.
-
[394]
- SCAR Anonymous.
-
[395]
- SCFlow Anonymous.
-
[396]
- SCV Anonymous. CVPR 2021 submission #3221
-
[397]
- SDFlow Anonymous.
-
[398]
- SEA-RAFT-L Anonymous.
-
[399]
- SEA-RAFT-M Anonymous.
-
[400]
- SENSE Anonymous. TBA
-
[401]
- SFL Jingchun Cheng, Yi-Hsuan, Tsai, Shengjin Wang, Ming-Hsuan Yang. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow. ICCV 2017
-
[402]
- SJTU_PAMI418
-
[403]
- SKFlow Shangkun Sun, Yuanqi Chen, Yu Zhu, Guodong Guo, Ge Li. Learning Optical Flow with Super Kernels. NeurIPS 2022.
-
[404]
- SKFlow_RAFT Anonymous.
-
[405]
- SKII Anonymous.
-
[406]
- SMURF Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski. SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping, CVPR 2021
-
[407]
- 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
-
[408]
- 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.
-
[409]
- 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
-
[410]
- 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
-
[411]
- SSTM++3kt
-
[412]
- SSTM++_reconst
-
[413]
- SSTM++_ttt_nws Fisseha Admasu Ferede, Madhusudhanan Balasubramanian. SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation. Neurocomputing 2023.
-
[414]
- SSTM++_ttt_ws Fisseha Admasu Ferede, Madhusudhanan Balasubramanian. SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation. Neurocomputing 2023.
-
[415]
- SSTM++nws-main
-
[416]
- SSTM++warm-main
-
[417]
- SSTM-nws
-
[418]
- STC-Flow Anonymous.
-
[419]
- STDC-Flow STDC-Flow: large displacement flow field estimation using similarity transformationbased dense correspondence, IET Computer Vision, 2020
-
[420]
- 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)
-
[421]
- SVFilterOh Mohamed A. Helala, Faisal Z. Qureshi, Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering, CRV 2017
-
[422]
- Scale-flow++_GS58 Efficient and Accurate Monocular 3D Motion Estimation Trained by GS58
-
[423]
- ScaleFLow++ Improved version of Scale-flow with added flight foreground, sports field initialization, and improved iterators
-
[424]
- ScaleFlow++_SAG ScaleFlow trained using GS58 in SAG++
-
[425]
- ScaleFlow_GS58 Train ScaleFlow by GS58
-
[426]
- ScaleRAFT Anonymous. ScaleRAFT
-
[427]
- ScopeFlow Aviram Bar-Haim and Lior Wolf. ScopeFlow: Dynamic Scene Scoping for Optical Flow, CVPR 2020.
-
[428]
- SegFlow113 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=11,d1=3)(Matlab code is available.)
-
[429]
- SegFlow153 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=15,d1=3)(Matlab code is available.)
-
[430]
- SegFlow193 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=19,d1=3)(Matlab code is available.)
-
[431]
- SegFlow33 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=3, d1=3)(Matlab code is available.)
-
[432]
- SegFlow73 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=7,d1=3)(Matlab code is available.)
-
[433]
- SegPM+Interpolation SegPM+Interpolation
-
[434]
- SelFlow Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)
-
[435]
- SelFlow Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu. SelFlow: Self-Supervised Learning of Optical Flow (CVPR 2019)
-
[436]
- SemFlow-2view Anonymous.
-
[437]
- Semantic_Lattice Anne S. Wannenwetsch, Martin Kiefel, Peter V. Gehler, Stefan Roth. Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice. GCPR 2019.
-
[438]
- SeparableFlow-2views Feihu Zhang, Oliver Woodford, Victor Prisacariu, Philip Torr, "Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation", ICCV 2021
-
[439]
- SfM-PM D. Maurer, N. Marniok, B. Goldluecke, A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation. ECCV 2018.
-
[440]
- 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)
-
[441]
- SparseFlow Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
-
[442]
- SparseFlowFused Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
-
[443]
- SplatFlow SplatFlow: Learning Multi-frame Optical Flow via Splatting; https://arxiv.org/abs/2306.08887; IJCV 2024
-
[444]
- Steered-L1 Anonymous.
-
[445]
- StreamFlow Shangkun Sun, Jiaming Liu, Thomas Li, Huaxia Li, Guoqing Liu, Wei Gao, NeurIPS 2024
-
[446]
- StreamFlow-Baseline
-
[447]
- 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.
-
[448]
- 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.
-
[449]
- Sun-RAFT
-
[450]
- SwinTR-RAFT Anonymous.
-
[451]
- TDFlow Anonymous.
-
[452]
- TF+OFM R. Kennedy and C.J. Taylor. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.
-
[453]
- TIMCflow Fei Yang, Yongmei Cheng, Joost Van de Weijer, Mikhail G. Mozerov. 'Improved Discrete Optical Flow Estimation with Triple Image Matching Cost', IEEE Access
-
[454]
- TSA Anonymous.
-
[455]
- TSA_ Anonymous.
-
[456]
- TSGFlow Anonymous. Tri-branch self-guided Transformer for optical flow estimation.
-
[457]
- TV-L1 Anonymous. It is a well-known model that uses TV-L1 (coupled) as the regularization term and an L1 data term.
-
[458]
- TV-L1+EM V. Lazcano. EXHAUSTIVE MATCHING EMPIRICAL STUDY FOR IMPROVING THE MOTION FIELD ESTIMATION
-
[459]
- TVL1_BWMFilter Balanced Weighted Median Filter and Bilateral Filter.
-
[460]
- TVL1_LD_GF V. Lazcano. TVL1 to handle large displacements using gradient patches. Parameter where optimized using PSO.
-
[461]
- TVL1_ROB Baseline submission for the robust vision flow challenge: TV-L1 Optical Flow Estimation
-
[462]
- TVL1_RVC RVC 2020 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo
-
[463]
- TransFlow Anonymous. CVPR 2023 anonymous submission
-
[464]
- TrepFlow Anonymous. Submission
-
[465]
- U2Flow Anonymous. Uncertainty-Aware Unsupervised Optical Flow Estimation
-
[466]
- U2Flow+MF Anonymous. Uncertainty-Aware Unsupervised Optical Flow Estimation
-
[467]
- 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)
-
[468]
- UPFlow
-
[469]
- UlDENet Anonymous.
-
[470]
- UnFlow Anonymous.
-
[471]
- UnProbFlow Anonymous.
-
[472]
- UnProbFlow_v2 Anonymous.
-
[473]
- UnSAMFlow Accepted by CVPR 2024
-
[474]
- UnsupSimFlow Unsupervised Learning of Optical Flow with Deep Feature Similarity, ECCV 2020
-
[475]
- VCN Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019
-
[476]
- 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
-
[477]
- VCN-OER Anonymous.
-
[478]
- VCN-WARP Anonymous.
-
[479]
- VCN_RVC Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019
-
[480]
- ViCo_VideoFlow_MOF
-
[481]
- VideoFlow-BOF VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, ICCV 2023
-
[482]
- VideoFlow-MOF VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, ICCV 2023
-
[483]
- WAFT-DAv2-a2 Anonymous.
-
[484]
- WAFT-DINOv3-a2 Anonymous.
-
[485]
- WAFT-Twins-a2 Anonymous.
-
[486]
- WKSparse
-
[487]
- WLIF-Flow Z. Tu, R. Poppe, and R. C. Veltkamp. Weighted local intensity fusion method for variational optical flow estimation.Pattern Recognition,2015
-
[488]
- WOLF_ROB
-
[489]
- WRTflow We proposed a weighted regularization transform to reduce the influence of illumination variations of optical flow estimation. We submit it to TCSVT.
-
[490]
- Win-Win Anonymous. Win-Win: Training High-Resolution Vision Transformers from Two Windows ICLR 2024 submission
-
[491]
- YOIO Anonymous.
-
[492]
- ZZZ Anonymous.
-
[493]
- cascade Anonymous.
-
[494]
- ce_skii_skii Anonymous.
-
[495]
- ce_v214 Anonymous.
-
[496]
- flowformer_val Anonymous.
-
[497]
- flownetnew
-
[498]
- gma+allcv
-
[499]
- htjnewfull Anonymous. a
-
[500]
- htjwarp2 Anonymous. htjwarp2
-
[501]
- less_iter_fine Anonymous. optimization with fewer iterations used.
-
[502]
- less_iteration Anonymous.
-
[503]
- mask Anonymous. mask
-
[504]
- metaFlow Anonymous.
-
[505]
- pwc_xx
-
[506]
- raft+allcv
-
[507]
- raft-jm Anonymous. mix raft_test
-
[508]
- ricom20201202 Anonymous.
-
[509]
- risc Anonymous.
-
[510]
- sdex00 Anonymous.
-
[511]
- sdex001 Anonymous.
-
[512]
- submission5367 Anonymous.
-
[513]
- testS
-
[514]
- test_mem Anonymous.
-
[515]
- tfFlowNet2 Anonymous.
-
[516]
- tfFlowNet2+GLR Anonymous.
-
[517]
- vcn+MSDRNet Anonymous. None