Visual Results by Method
Results for methods appear here after users upload them and approve them for public display.
Return to numerical results table
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Perturbed Market 3, s0-10 = 0.821
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Perturbed Shaman 1, s0-10 = 0.948
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Ambush 1, s0-10 = 2.651
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Ambush 3, s0-10 = 1.471
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Bamboo 3, s0-10 = 0.870
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Cave 3, s0-10 = 2.242
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Market 1, s0-10 = 1.215
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Market 4, s0-10 = 1.957
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Mountain 2, s0-10 = 0.852
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Temple 1, s0-10 = 0.878
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Tiger, s0-10 = 0.610
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Wall, s0-10 = 2.434
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
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[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
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[2]
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[3]
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[4]
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[9]
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[10]
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[11]
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[12]
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[16]
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[29]
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[35]
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[37]
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[38]
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[39]
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[40]
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[41]
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[42]
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[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
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[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
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[45]
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[46]
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[47]
- CAR_100 Anonymous.
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[48]
- CARflow
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[49]
- CARflow-mv Anonymous.
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[50]
- CCAFlow Anonymous. Submission
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[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
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[52]
- CE_SKII Anonymous.
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[53]
- CFFlow Anonymous.
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[54]
- CGCV-GMA
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[55]
- CGCV-RAFT
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[56]
- CNet Anonymous.
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[57]
- COF H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
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[58]
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[59]
- COMBO Anonymous.
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[60]
- CPM-Flow Yinlin Hu, Rui Song, Yunsong Li. Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.
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[61]
- CPM2 Coarse-to-fine PatchMatch for dense correspondence, Yunsong Li, Yinlin Hu, Rui Song, Peng Rao, Yangli Wang, submitted to T-CSVT
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[62]
- CPM_AUG Anonymous. CVPR 18 submission #1939
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[63]
- CPNFlow Conditional Prior Networks for Optical Flow, Yanchao Yang, Stefano Soatto; The European Conference on Computer Vision (ECCV), 2018, pp. 271-287
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[64]
- CRAFT Anonymous. Cross-Attentional Flow Transformer
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[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.
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[66]
- CVE-RAFT ljp
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[67]
- CVEFlow ljp
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[68]
- CVENG22+Epic Anonymous.
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[69]
- CVENG22+RIC Anonymous.
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[70]
- CVPR-1235 Anonymous.
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[71]
- CasFlow Anonymous. CasFlow
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[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.
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[73]
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[74]
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[76]
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[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.
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[78]
- Compact-Unified-Model A model for two motion tasks: optical flow and rectified stereo-matching
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[79]
- CompactFlow Anonymous. ICCV submission.
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[80]
- CompactFlow-woscv Anonymous.
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[81]
- CompactFlowNet Anonymous.
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[82]
- ComponentFusion Anonymous. Fast Optical Flow by Component Fusion. Submitted to ECCV 2014. Paper ID 941.
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[83]
- ContFusion M. Stoll, D. Maurer, and A. Bruhn. Variational Large Displacement Optical Flow without Feature Matches. EMMCVPR 2017.
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[84]
- ContinualFlow_ROB Michal Neoral, Jan Šochman and Jiří Matas. Continual Occlusions and Optical Flow Estimation, ACCV 2018
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[85]
- CroCo-Flow CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. Weinzaepfel et al. ICCV 2023.
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[86]
- CrossFlow Anonymous.
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[87]
- DA_opticalflow Anonymous.
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[88]
- DC-RAFT Anonymous.
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[89]
- DCFlow Jia Xu, René Ranftl, Vladlen Koltun. Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
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[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.
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[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.
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[92]
- DCN-Flow DCN-Flow
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[93]
- DCVNet Anonymous. 0.03s with a GTX 1080ti GPU.
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[94]
- DDCNet_B0_tf_sintel Anonymous. DDCNet_B0 fine-tuned on Sintel
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[95]
- DDCNet_B1_ft-sintel DDCNet B1 finetuned on Sintel
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[96]
- DDCNet_Multires_ft_sintel DDCNet Multires fine tuned on Sintel
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[97]
- DDCNet_Stacked Anonymous. Two blocks of simple DDCNet
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[98]
- DDCNet_stacked2 Anonymous. two times down-sampling of feature maps, 128 filters each layer, fine-tuned on sintel
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[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
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[100]
- DDVM Anonymous. NeurIPS 2023 submission #14395
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[101]
- DEQ-Flow-H Deep Equilibrium Optical Flow Estimation
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[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
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[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
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[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
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[105]
- DICL-Flow Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
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[106]
- DICL-Flow+ Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
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[107]
- DICL_update Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation (NeurIPS 2020)
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[108]
- DIP Anonymous. Deep Inverse Patchmatch for High-Resolution Optical Flow
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[109]
- DIP-Flow D. Maurer, M. Stoll, A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
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[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.
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[111]
- DMF_ROB Baseline submission for ROB flow challenge: Deepflow with Deepmatching as prior
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[112]
- DPCTF Anonymous. Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow
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[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.
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[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.
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[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.
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[116]
- Data-Flow Anonymous. CSAD data cost + second order smoothness
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[117]
- Deep+R B. Drayer and T. Brox. Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation. BMVC 2015
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[118]
- Deep-EIP Anonymous. End-2-End learning for energy based inpainting of optical flow. ACCV submission 572
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[119]
- DeepDiscreteFlow F. Güney and A. Geiger. Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
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[120]
- DeepFlow P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. ICCV 2013.
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[121]
- DeepFlow2 J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. DeepMatching: Hierarchical Deformable Dense Matching. Submitted to IJCV.
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[122]
- DefFlowP Anonymous.
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[123]
- Deformable_RAFT Anonymous. RAFT with deformable
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[124]
- Devon Anonymous. CVPR submission #1906
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[125]
- DictFlowS Anonymous.
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[126]
- DiscreteFlow M. Menze, C. Heipke and A. Geiger. Discrete Optimization for Optical Flow. GCPR 2015
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[127]
- DiscreteFlow+OIR D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.
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[128]
- DistillFlow Anonymous. Unsupervise result
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[129]
- DistillFlow+ft Anonymous. Supervised result.
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[130]
- EFlow-M Anonymous.
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[131]
- EFlow-M-tile Anonymous.
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[132]
- EMD-L Anonymous. Anonymous
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[133]
- EMD-M Anonymous. Anonymous
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[134]
- EMD-OER Anonymous.
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[135]
- EMSFlow Anonymous.
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[136]
- EPIflow Deep Epipolar Flow
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[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.
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[138]
- EPPM L. Bao, Q. Yang, and H. Jin. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. CVPR 2014.
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[139]
- ER-FLOW2 Anonymous. Adjusted ERFlow
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[140]
- ERFlow Anonymous.
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[141]
- EdgeFlow Anonymous.
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[142]
- EgFlow-cl Anonymous. edge-guided, small parameter optical flow network based on CNN
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[143]
- EpicFlow J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid. EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015.
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[144]
- ErrorMatch-GMA Anonymous. tba
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[145]
- ErrorMatch-RAFT Anonymous. tba
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[146]
- F2PD_JJN Anonymous. With Deformable Convolutional Networks
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[147]
- F3-MPLF Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh: Filter Flow made Practical: Massively Parallel and Lock-Free -- CVPR 2017
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[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
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[149]
- FAOP-Flow Anonymous.
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[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
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[151]
- FCTR Anonymous.
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[152]
- FCTR-m Anonymous.
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[153]
- FDFlowNet Lingtong Kong and Jie Yang. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network, ICIP 2020.
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[154]
- FF++_ROB Accurate matching and robust interpolation. KITTI parameters as given by the paper. Edge detector trained on KITTI + Sintel + BSDS500.
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[155]
- FGI Y. Li, D. Min, M. N. Do, and J. Lu. Fast Guided Global Interpolation for Depth and Motion. ECCV 2016
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[156]
- FPCR-Net Anonymous.
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[157]
- FPCR-Net2 Anonymous.
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[158]
- FTGAN The Application of Counter Learning of Reverse Residual Attention in Optical Flow Estimation
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[159]
- FastFlow Anonymous.
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[160]
- FastFlow2 Anonymous.
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[161]
- FastFlowNet Lingtong Kong, Chunhua Shen and Jie Yang. FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation, ICRA 2021.
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[162]
- FastFlowNet-ft+ Anonymous.
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[163]
- Flow1D Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong. High-Resolution Optical Flow from 1D Attention and Correlation. ICCV 2021, Oral
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[164]
- Flow1D-OER Anonymous.
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[165]
- FlowDiffuser FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models, CVPR 2024
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[166]
- FlowFields C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. ICCV 2015.
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[167]
- FlowFields+ C. Bailer, B. Taetz and D. Stricker. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. T-PAMI.
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[168]
- FlowFields++ R. Schuster, C. Bailer, O. Wasenmüller, D. Stricker. FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018
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[169]
- FlowFieldsCNN C. Bailer, K Varanasi and D. Stricker. CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
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[170]
- FlowFormer FlowFormer: A Transformer Architecture for Optical Flow
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[171]
- FlowFormer++ FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
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[172]
- FlowNet2 Anonymous. CVPR Submission #900
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[173]
- FlowNet2-ft-sintel Anonymous. CVPR Submission #900
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[174]
- FlowNetADF Lightweight Probabilistic Deep Networks
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[175]
- FlowNetC+OFR Anonymous.
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[176]
- FlowNetC+ft+v Anonymous. ICCV sumbmission 235
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[177]
- FlowNetC-MD Anonymous.
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[178]
- FlowNetProbOut Lightweight Probabilistic Deep Networks
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[179]
- FlowNetS+ft+v Anonymous. ICCV submission 235
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[180]
- FlowSAC_dcf Anonymous.
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[181]
- FlowSAC_ff Anonymous
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[182]
- Flownet2-IA Anonymous. Flownet2 combining with illumination adjustment
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[183]
- Flownet2-IAER Anonymous. Flownet2 combining with illumination adjustment and edge refinement
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[184]
- FullFlow Q. Chen, V. Koltun. Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
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[185]
- FullFlow+KF W. Bao, Y. Xiao, L. Chen, and Z. Gao. Kalmanflow: Efficient Kalman Filtering for Video Optical Flow. ICIP 2018.
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[186]
- GAFlow Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
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[187]
- GAFlow-FF Anonymous. GAFlow: Incorporating Gaussian Attention into Optical Flow, ICCV 2023.
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[188]
- GANFlow Anonymous.
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[189]
- GCA-Net Anonymous.
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[190]
- GCA-Net-ft+ Anonymous. finetune GCA-Net with a better data augmentation method
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[191]
- GMA Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley. Learning to Estimate Hidden Motions with Global Motion Aggregation, ICCV 2021.
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[192]
- GMA+LCT-Flow GMA+LCT-Flow
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[193]
- GMA+TCU+aug Anonymous.
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[194]
- GMA+TCU-aug Anonymous.
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[195]
- GMA-FS Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
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[196]
- GMA-base Anonymous. GMA-base
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[197]
- GMA-two_img Anonymous.
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[198]
- GMFlow Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao. GMFlow: Learning Optical Flow via Global Matching. CVPR 2022, Oral
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[199]
- GMFlow+ Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, Andreas Geiger. Unifying Flow, Stereo and Depth Estimation. TPAMI 2023
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[200]
- GMFlowNet Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou and Dimitris Metaxas. Global Matching with Overlapping Attention for Optical Flow Estimation. CVPR 2022
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[201]
- GMFlow_RVC GMFlow RVC 2022 submission.
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[202]
- GPNet Anonymous.
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[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.
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[204]
- GeoViT
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[205]
- GlobalPatchCollider Shenlong Wang, Sean Fanello, Christoph Rhemann, Shahram Izadi and Pushmeet Kohli, The Global Patch Collider, CVPR, 2016
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[206]
- GroundTruth
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[207]
- Grts-Flow-V2 En Zhu, Yuanwei Li, Yanling Shi. Fast Optical Flow Estimation without Parallel Architectures. TCSVT
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[208]
- H+S_ROB Baseline submission for the ROB flow challenge: Horn-Schunck Optical Flow with a Multi-Scale Strategy
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[209]
- H+S_RVC RVC 2020 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann.
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[210]
- H-1px Anonymous.
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[211]
- H-v3 Anonymous.
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[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
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[213]
- HCOF+multi R. Kennedy and C. J. Taylor. Hierarchically-Constrained Optical Flow. CVPR 2015.
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[214]
- HCVNet Anonymous.
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[215]
- HD3-Flow Zhichao Yin, Trevor Darrell, Fisher Yu. Hierarchical Discrete Distribution Decomposition for Match Density Estimation (CVPR 2019)
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[216]
- HD3-Flow-OER Anonymous.
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[217]
- HD3F+MSDRNet Anonymous. HD3F+MSDRNet
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[218]
- HMAFlow Anonymous.
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[219]
- HMFlow HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
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[220]
- HSVFlow Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
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[221]
- Horn+Schunck A modern Matlab implementation of the Horn & Schunck method by Deqing Sun.
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[222]
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[223]
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[224]
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[225]
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[226]
- IOFPL-CVr8-ft Anonymous. IOFPL - Improving Optical Flow on a Pyramid Level (ECCV2020)
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[227]
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[228]
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[229]
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[230]
- IRR-PWC-OER Anonymous.
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[231]
- IRR-PWC_RVC RVC 2020 submission
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[232]
- ISDAFlow Anonymous. ISDAFlow: Self-supervised Infusion of Segmented and Depth Anything Models into Optical Flow
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[233]
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[234]
- IWarp Anonymous.
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[235]
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[236]
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[237]
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[240]
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[241]
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[242]
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[243]
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[244]
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[245]
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[246]
- LSHRAFT Anonymous.
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[247]
- LSM_FLOW_RVC LSM: Learning Subspace Minimization for Low-level Vision for RVC2020
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[248]
- Lavon Anonymous.
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[249]
- LiteFlowNet Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018.
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[250]
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[253]
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[254]
- M-1px Anonymous.
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[255]
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[256]
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[257]
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[258]
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[259]
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[260]
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[261]
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[262]
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[263]
- MF2C Anonymous
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[264]
- MFCFlow Anonymous.
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[265]
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[266]
- MFFC Anonymous
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[267]
- MFR Anonymous. Motion Feature Recovery
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[268]
- MFlow Anonymous.
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[269]
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[270]
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[271]
- MMAFlow Anonymous.
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[272]
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[273]
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[274]
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[275]
- MRDFlow Anonymous.
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[276]
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[277]
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[278]
- MVFlow Anonymous.
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[279]
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[280]
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[281]
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[282]
- MatchFlow_GMA_2-view Anonymous.
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[283]
- MatchFlow_RAFT Qiaole Dong, Chenjie Cao, Yanwei Fu. Rethinking Optical Flow from Geometric Matching Consistent Perspective. CVPR 2023.
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[284]
- MeFlow Anonymous.
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[285]
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[286]
- MemFlow-T Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
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[287]
- MemoFlow Anonymous.
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[288]
- MirrorFlow Junhwa Hur and Stefan Roth. MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017
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[289]
- MoEFlow
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[290]
- MobileFlow Anonymous. Optical flow estimation framework using an enhanced MobileNet and Transformer with a large-kernel upsampler.
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[291]
- Model_model Anonymous. this is a model
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[292]
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[293]
- MotionFlow+ Anonymous.
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[294]
- Muun-RAFT
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[295]
- NASFlow Anonymous.
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[296]
- NASFlow-PWC Anonymous.
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[297]
- NASFlow-RAFT Anonymous.
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[298]
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[299]
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[300]
- NccFlow Anonymous.
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[301]
- OADFlow Anonymous.
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[302]
- OAR-Flow D. Maurer, M. Stoll, A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.
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[303]
- OAS-Net Lingtong Kong, Xiaohang Yang and Jie Yang. OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow, ICASSP 2021.
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[304]
- OCRAFT
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[305]
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[306]
- OFM Anonymous.
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[307]
- OFM Anonymous.
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[308]
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[309]
- OIFlow occlusion-inpainting Flow
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[310]
- OM_CRAFT Anonymous.
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[311]
- OM_GMA Anonymous.
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[312]
- OM_GMFlow Anonymous.
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[313]
- OM_RAFT Anonymous. Used method with channel shuffle to enhance feature.
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[314]
- OPPFlow Anonymous.
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[315]
- OatNet01 Anonymous.
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[316]
- PCA-Flow J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
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[317]
- PCA-Layers J. Wulff and M. J. Black: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. CVPR 2015.
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[318]
- PGM-C Anonymous
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[319]
- PH-Flow Jiaolong Yang and Hongdong Li. Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015
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[320]
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[322]
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[323]
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[324]
- PPM Parametric PatchMatch, Fangjun Kuang, master thesis, 2017
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[325]
- PRAFlow_RVC Anonymous.
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[326]
- PRichFlow Anonymous.
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[327]
- PST Anonymous. ACCV2018 submission #1195
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[328]
- PVTFlow Anonymous.
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[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.
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[330]
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[331]
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[332]
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[333]
- PWC-Net-OER Anonymous.
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[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.
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[335]
- PWC_acn Anonymous.
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[336]
- PatchBatch+Inter T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.
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[337]
- PatchBatch-CENT+SD Anonymous.
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[338]
- PatchWMF-OF Zhigang Tu, Coert Van Gemeren and Remco C. Veltkamp. Improved Color Patch Similarity Measure Based Weighted Median Filter. ACCV2014
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[339]
- PosetOptimization Anonymous.
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[340]
- PredFlow
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[341]
- PredFlow
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[342]
- ProFlow D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.
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[343]
- ProFlow_ROB D. Maurer, A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018. (ROB setting)
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[344]
- ProMotion Anonymous. Anonymous submission
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[345]
- ProbFlowFields Anne S. Wannenwetsch, Margret Keuper, Stefan Roth. ProbFlow: Joint Optical Flow and Uncertainty Estimation. ICCV 2017.
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[346]
- ProtoFormer
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[347]
- ProtoFormer Anonymous.
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[348]
- Pwc_ps Anonymous.
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[349]
- RAFT Zachary Teed and Jia Deng. RAFT: Recurrent All-Pairs Field Transforms for Optical Flow, ECCV 2020.
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[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.
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[351]
- RAFT+ConvUp Anonymous.
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[352]
- RAFT+LCT-Flow RAFT+LCT-Flow
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[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
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[354]
- RAFT+NCUP Normalized Convolution Upsampling for Refined Optical Flow Estimation
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[355]
- RAFT+OBS Anonymous. We change the dataset to train RAFT.
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[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
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[357]
- RAFT-CF
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[358]
- RAFT-DFlow Anonymous.
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[359]
- RAFT-FS Anonymous. Semi-Supervised Learning of Optical Flow by Flow Supervisor
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[360]
- RAFT-GT Anonymous. CVPR 2021 submission
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[361]
- RAFT-GT-ft Anonymous. CVPR 2021 submission
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[362]
- RAFT-OCTC Anonymous.
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[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.
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[364]
- RAFT-VM
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[365]
- RAFT-base Anonymous. RAFT-base
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[366]
- RAFT-illumination Anonymous. RAFT-illumination
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[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.
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[368]
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[369]
- RAFT_Chairs_Things Anonymous.
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[370]
- RAFTv1-OER-2-view Anonymous.
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[371]
- RAFTv2-OER-2-view Anonymous.
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[372]
- RAFTv2-OER-warm-start Anonymous.
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[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.
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[374]
- RAFTwarm+OBS Anonymous. train the RAFT network on new our datasets and applying warmup
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[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.
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[376]
- RC-LSTM-1dir Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
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[377]
- RC-LSTM-4dir Learning Contextual Dependencies for Optical Flow with Recurrent Neural Networks (ACCV 2016)
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[378]
- RFPM Anonymous.
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[379]
- RGBFlow Syed Tafseer Haider Shah, Xiang Xuezhi, Harbin Engineering University. Photometric Invariance of Optical Flow With Color Space Transformations
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[380]
- RICBCDN Anonymous.
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[381]
- RLOF_DENSE Jonas Geistert, Tobias Senst and Thomas Sikora, Robust Local Optical Flow: Dense Motion Vector Field Interpolation, PCS 2016
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[382]
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[384]
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[385]
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[386]
- RichFlow-ft Anonymous.
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[387]
- RichFlow-ft-fnl Anonymous. final pass version
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[388]
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[390]
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[391]
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- SAMFlow SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model. Accepted in AAAI 2024.
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[393]
- SAnet Anonymous.
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[394]
- SCAR Anonymous.
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[395]
- SCFlow Anonymous.
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[396]
- SCV Anonymous. CVPR 2021 submission #3221
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[397]
- SDFlow Anonymous.
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[398]
- SEA-RAFT-L Anonymous.
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[399]
- SEA-RAFT-M Anonymous.
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[400]
- SENSE Anonymous. TBA
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[401]
- SFL Jingchun Cheng, Yi-Hsuan, Tsai, Shengjin Wang, Ming-Hsuan Yang. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow. ICCV 2017
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[402]
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[403]
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[404]
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[405]
- SKII Anonymous.
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[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
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[407]
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[408]
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[410]
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[411]
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[418]
- STC-Flow Anonymous.
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[419]
- STDC-Flow STDC-Flow: large displacement flow field estimation using similarity transformationbased dense correspondence, IET Computer Vision, 2020
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[420]
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[422]
- Scale-flow++_GS58 Efficient and Accurate Monocular 3D Motion Estimation Trained by GS58
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[423]
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[424]
- ScaleFlow++_SAG ScaleFlow trained using GS58 in SAG++
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[425]
- ScaleFlow_GS58 Train ScaleFlow by GS58
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[426]
- ScaleRAFT Anonymous. ScaleRAFT
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[427]
- ScopeFlow Aviram Bar-Haim and Lior Wolf. ScopeFlow: Dynamic Scene Scoping for Optical Flow, CVPR 2020.
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[428]
- SegFlow113 Efficient Segmentation-Based PatchMatch for Large displacement Optical Flow Estimation. 2019 TCSVT. (d0=11,d1=3)(Matlab code is available.)
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[429]
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[430]
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[431]
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[432]
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[433]
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[435]
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[436]
- SemFlow-2view Anonymous.
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[437]
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[441]
- SparseFlow Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
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[442]
- SparseFlowFused Radu Timofte and Luc Van Gool. SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow. WACV 2015
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[443]
- SplatFlow SplatFlow: Learning Multi-frame Optical Flow via Splatting; https://arxiv.org/abs/2306.08887; IJCV 2024
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[444]
- Steered-L1 Anonymous.
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[445]
- StreamFlow Shangkun Sun, Jiaming Liu, Thomas Li, Huaxia Li, Guoqing Liu, Wei Gao, NeurIPS 2024
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[446]
- StreamFlow-Baseline
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[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.
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[449]
- Sun-RAFT
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[450]
- SwinTR-RAFT Anonymous.
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[451]
- TDFlow Anonymous.
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[452]
- TF+OFM R. Kennedy and C.J. Taylor. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.
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[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
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[454]
- TSA Anonymous.
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[455]
- TSA_ Anonymous.
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[456]
- TSGFlow Anonymous. Tri-branch self-guided Transformer for optical flow estimation.
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[457]
- TV-L1 Anonymous. It is a well-known model that uses TV-L1 (coupled) as the regularization term and an L1 data term.
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[458]
- TV-L1+EM V. Lazcano. EXHAUSTIVE MATCHING EMPIRICAL STUDY FOR IMPROVING THE MOTION FIELD ESTIMATION
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[459]
- TVL1_BWMFilter Balanced Weighted Median Filter and Bilateral Filter.
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[460]
- TVL1_LD_GF V. Lazcano. TVL1 to handle large displacements using gradient patches. Parameter where optimized using PSO.
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[461]
- TVL1_ROB Baseline submission for the robust vision flow challenge: TV-L1 Optical Flow Estimation
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[462]
- TVL1_RVC RVC 2020 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo
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[463]
- TransFlow Anonymous. CVPR 2023 anonymous submission
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[464]
- TrepFlow Anonymous. Submission
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[465]
- 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)
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[466]
- UPFlow
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[467]
- UlDENet Anonymous.
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[468]
- UnFlow Anonymous.
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[469]
- UnProbFlow Anonymous.
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[470]
- UnProbFlow_v2 Anonymous.
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[471]
- UnSAMFlow Accepted by CVPR 2024
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[472]
- UnsupSimFlow Unsupervised Learning of Optical Flow with Deep Feature Similarity, ECCV 2020
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[473]
- VCN Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019
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[474]
- 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
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[475]
- VCN-OER Anonymous.
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[476]
- VCN-WARP Anonymous.
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[477]
- VCN_RVC Gengshan Yang and Deva Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019
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[478]
- ViCo_VideoFlow_MOF
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[479]
- VideoFlow-BOF VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, ICCV 2023
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[480]
- VideoFlow-MOF VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, ICCV 2023
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[481]
- WAFTv2-DAv2 Anonymous.
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[482]
- WAFTv2-DINOv3 Anonymous.
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[483]
- WAFTv2-Twins Anonymous.
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[484]
- WKSparse
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[485]
- WLIF-Flow Z. Tu, R. Poppe, and R. C. Veltkamp. Weighted local intensity fusion method for variational optical flow estimation.Pattern Recognition,2015
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[486]
- WOLF_ROB
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[487]
- WRTflow We proposed a weighted regularization transform to reduce the influence of illumination variations of optical flow estimation. We submit it to TCSVT.
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[488]
- Win-Win Anonymous. Win-Win: Training High-Resolution Vision Transformers from Two Windows ICLR 2024 submission
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[489]
- YOIO Anonymous.
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[490]
- ZZZ Anonymous.
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[491]
- cascade Anonymous.
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[492]
- ce_skii_skii Anonymous.
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[493]
- ce_v214 Anonymous.
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[494]
- flowformer_val Anonymous.
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[495]
- flownetnew
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[496]
- gma+allcv
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[497]
- htjnewfull Anonymous. a
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[498]
- htjwarp2 Anonymous. htjwarp2
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[499]
- less_iter_fine Anonymous. optimization with fewer iterations used.
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[500]
- less_iteration Anonymous.
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[501]
- mask Anonymous. mask
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[502]
- metaFlow Anonymous.
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[503]
- pwc_xx
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[504]
- raft+allcv
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[505]
- raft-jm Anonymous. mix raft_test
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[506]
- ricom20201202 Anonymous.
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[507]
- risc Anonymous.
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[508]
- sdex00 Anonymous.
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[509]
- sdex001 Anonymous.
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[510]
- submission5367 Anonymous.
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[511]
- testS
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[512]
- test_mem Anonymous.
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[513]
- tfFlowNet2 Anonymous.
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[514]
- tfFlowNet2+GLR Anonymous.
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[515]
- vcn+MSDRNet Anonymous. None