\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Fl-bg} & {\bf Fl-fg} & {\bf Fl-all} & {\bf Density} & {\bf Runtime} & {\bf Environment}\\ \hline
RBO & st & 2.13 \% & 6.15 \% & 2.80 \% & 100.00 \% & 1 s / 1 core & \\
ScaleRAFTRBO & st & 2.27 \% & 5.63 \% & 2.83 \% & 100.00 \% & 0.1 s / 1 core & \\
SplatFlow3D & st & 2.27 \% & 6.02 \% & 2.89 \% & 100.00 \% & 0.2 s / GPU & \\
GAOSF & st & 2.08 \% & 7.37 \% & 2.96 \% & 100.00 \% & 1 s / GPU & \\
CamLiRAFT & st & 2.08 \% & 7.37 \% & 2.96 \% & 100.00 \% & 1 s / GPU & H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion. TPAMI 2023.\\
OAMaskFlow & st & 2.10 \% & 7.80 \% & 3.05 \% & 100.00 \% & 0.5 s / GPU & \\
EFLOW & st & 2.27 \% & 7.10 \% & 3.07 \% & 100.00 \% & 0.06 s / 1 core & \\
CamLiFlow & st & 2.31 \% & 7.04 \% & 3.10 \% & 100.00 \% & 1.2 s / GPU & H. Liu, T. Lu, Y. Xu, J. Liu, W. Li and L. Chen: CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation. CVPR 2022.\\
RAFT-3D++ & st & 2.09 \% & 8.55 \% & 3.16 \% & 100.00 \% & 0.5 s / 1 core & \\
DDVM & & 2.90 \% & 5.05 \% & 3.26 \% & 100.00 \% & / & S. Saxena, C. Herrmann, J. Hur, A. Kar, M. Norouzi, D. Sun and D. Fleet: The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation. NeurIPS 2023.\\
CamLiRAFT-NR & st & 2.76 \% & 6.78 \% & 3.43 \% & 100.00 \% & 1 s / GPU & H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion. arXiv preprint arXiv:2303.12017 2023.\\
M-FUSE & st mv & 2.66 \% & 7.47 \% & 3.46 \% & 100.00 \% & 1.3 s / & L. Mehl, A. Jahedi, J. Schmalfuss and A. Bruhn: M-FUSE: Multi-frame Fusion for Scene Flow Estimation. Proc. Winter Conference on Applications of Computer Vision (WACV) 2023.\\
RigidMask+ISF & st & 2.63 \% & 7.85 \% & 3.50 \% & 100.00 \% & 3.3 s / GPU & G. Yang and D. Ramanan: Learning to Segment Rigid Motions from Two Frames. CVPR 2021.\\
CroCo-Flow & & 3.18 \% & 5.94 \% & 3.64 \% & 100.00 \% & 3s / & P. Weinzaepfel, T. Lucas, V. Leroy, Y. Cabon, V. Arora, R. Br\'egier, G. Csurka, L. Antsfeld, B. Chidlovskii and J. Revaud: CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. ICCV 2023.\\
RAFT3DMR & st & 2.52 \% & 9.81 \% & 3.74 \% & 100.00 \% & 1 s / 1 core & \\
CCMR+ & & 3.39 \% & 6.21 \% & 3.86 \% & 100.00 \% & 1.5 s / GPU & A. Jahedi, M. Luz, M. Rivinius and A. Bruhn: CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning. WACV 2024.\\
MemFlow-T & mv & 3.44 \% & 6.09 \% & 3.88 \% & 100.00 \% & / & Q. Dong and Y. Fu: MemFlow: Optical Flow Estimation and Prediction with Memory. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.\\
RAFT-it+\_RVC & & 3.62 \% & 5.33 \% & 3.90 \% & 100.00 \% & 0.14 s / 1 core & D. Sun, C. Herrmann, F. Reda, M. Rubinstein, D. Fleet and W. Freeman: Disentangling Architecture and Training for Optical Flow. ECCV 2022.\\
RRTC & & 3.77 \% & 4.70 \% & 3.93 \% & 100.00 \% & 0.3 s / 1 core & \\
RAFT-OCTC & & 3.72 \% & 5.39 \% & 4.00 \% & 100.00 \% & 0.2 s / GPU & J. Jeong, J. Lin, F. Porikli and N. Kwak: Imposing Consistency for Optical Flow Estimation (Qualcomm AI Research). CVPR 2022.\\
app & & 3.95 \% & 4.74 \% & 4.08 \% & 100.00 \% & 0.1 s / 1 core & \\
MemFlow & mv & 3.67 \% & 6.27 \% & 4.10 \% & 100.00 \% & / & Q. Dong and Y. Fu: MemFlow: Optical Flow Estimation and Prediction with Memory. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.\\
SF2SE3 & st & 3.17 \% & 8.79 \% & 4.11 \% & 100.00 \% & 2.7 s / GPU & L. Sommer, P. Schröppel and T. Brox: SF2SE3: Clustering Scene Flow into SE (3)-Motions via Proposal and Selection. DAGM German Conference on Pattern Recognition 2022.\\
MS\_RAFT+\_corr\_RVC & & 3.83 \% & 5.71 \% & 4.15 \% & 100.00 \% & 0.65 s / GPU & A. Jahedi, M. Luz, M. Rivinius, L. Mehl and A. Bruhn: High Resolution Multi-Scale RAFT. International Journal of Computer Vision (IJCV) 2023.\\
app+mo1 & & 3.95 \% & 5.21 \% & 4.16 \% & 100.00 \% & 1 s / 1 core & \\
FlowDiffuser\_sub & & 3.68 \% & 6.64 \% & 4.17 \% & 100.00 \% & 0.4 s / 1 core & \\
DIP & & 3.86 \% & 5.96 \% & 4.21 \% & 100.00 \% & 0.15 s / 1 core & Z. Zheng, N. Nie, Z. Ling, P. Xiong, J. Liu, H. Wang and J. Li: DIP: Deep Inverse Patchmatch for High- Resolution Optical Flow. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.\\
StreamFlow & & 3.91 \% & 5.91 \% & 4.24 \% & 100.00 \% & 0.08 s / 1 core & \\
ACR-Net & & 4.06 \% & 5.16 \% & 4.25 \% & 100.00 \% & 0.08 s / 1 core & \\
MonoFusion & st & 3.93 \% & 5.97 \% & 4.27 \% & 100.00 \% & 0.7 s / GPU & \\
RAFT-3D & st & 3.39 \% & 8.79 \% & 4.29 \% & 100.00 \% & 2 s / GPU & Z. Teed and J. Deng: RAFT-3D: Scene Flow using Rigid-Motion Embeddings. arXiv preprint arXiv:2012.00726 2020.\\
CCAFlow & & 4.05 \% & 5.56 \% & 4.30 \% & 100.00 \% & 0.2 s / GPU & \\
SEA-RAFT(L) & & 4.08 \% & 5.37 \% & 4.30 \% & 100.00 \% & 0.1 s / 1 core & \\
Promotion & & 4.02 \% & 5.73 \% & 4.30 \% & 100.00 \% & 0.25 s / 1 core & \\
ap+m & & 3.96 \% & 6.11 \% & 4.32 \% & 100.00 \% & 1 s / 1 core & \\
SGFlow & & 4.04 \% & 5.82 \% & 4.34 \% & 100.00 \% & 0.15 s / 1 core & \\
ProtoFormer & & 4.05 \% & 5.81 \% & 4.34 \% & 100.00 \% & 0.2 s / 1 core & \\
DF-Flow & & 4.10 \% & 5.60 \% & 4.35 \% & 100.00 \% & 0.2 s / 1 core & \\
GMFlow\_RVC & & 4.16 \% & 5.67 \% & 4.41 \% & 100.00 \% & 0.2 s / & H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao and A. Geiger: Unifying Flow, Stereo and Depth Estimation. arXiv preprint arXiv:2211.05783 2022.\\
AnyFlow & & 4.15 \% & 5.76 \% & 4.41 \% & 100.00 \% & 0.1 s / 1 core & H. Jung, Z. Hui, L. Luo, H. Yang, F. Liu, S. Yoo, R. Ranjan and D. Demandolx: AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation. arXiv preprint arXiv:2303.16493 2023.\\
ASFlow & & 4.17 \% & 5.68 \% & 4.42 \% & 100.00 \% & 0.4 s / 1 core & \\
MMAFlow & & 4.21 \% & 5.90 \% & 4.49 \% & 100.00 \% & 0.3 s / 1 core & \\
PFlowFormer & & 4.24 \% & 5.76 \% & 4.49 \% & 100.00 \% & 0.44 s / & \\
EMD-L & & 4.16 \% & 6.15 \% & 4.49 \% & 100.00 \% & 0.14 s / GPU & \\
GMFlow+ & & 4.27 \% & 5.60 \% & 4.49 \% & 100.00 \% & 0.2 s / & H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao and A. Geiger: Unifying Flow, Stereo and Depth Estimation. arXiv preprint arXiv:2211.05783 2022.\\
ScaleRAFT & st & 4.45 \% & 4.76 \% & 4.50 \% & 100.00 \% & 0.1 s / 1 core & \\
SeparableFlow & & 4.25 \% & 5.92 \% & 4.53 \% & 100.00 \% & 0.5 s / & F. Zhang, O. Woodford, V. Prisacariu and P. Torr: Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
HD-Flow & & 4.10 \% & 6.67 \% & 4.53 \% & 100.00 \% & 0.2 s / 1 core & \\
f & & 4.38 \% & 5.61 \% & 4.59 \% & 100.00 \% & 1 s / 1 core & \\
DFFlow & & 4.30 \% & 6.08 \% & 4.60 \% & 100.00 \% & 1 s / 1 core & \\
KPA-Flow & & 4.17 \% & 6.77 \% & 4.60 \% & 100.00 \% & 0.2 s / GPU & A. Luo, F. Yang, X. Li and S. Liu: Learning Optical Flow With Kernel Patch Attention. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.\\
SplatFlow & & 4.26 \% & 6.34 \% & 4.61 \% & 100.00 \% & 0.1 s / GPU & B. Wang, Y. Zhang, J. Li, Y. Yu, Z. Sun, L. Liu and D. Hu: SplatFlow: Learning Multi-frame Optical Flow via Splatting. International Journal of Computer Vision 2024.\\
MatchFlow(G) & & 4.33 \% & 6.11 \% & 4.63 \% & 100.00 \% & 0.3 s / & Q. Dong, C. Cao and Y. Fu: Rethinking Optical Flow from Geometric Matching Consistent Perspective. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.\\
SCFlow & & 4.49 \% & 5.30 \% & 4.63 \% & 100.00 \% & 1 s / 1 core & \\
PVTFlow & & 4.27 \% & 6.44 \% & 4.63 \% & 100.00 \% & 0.2 s / 1 core & \\
RPKNet & & 4.63 \% & 4.69 \% & 4.64 \% & 100.00 \% & 0.6 s / GPU & H. Morimitsu, X. Zhu, X. Ji and X. Yin: Recurrent Partial Kernel Network for Efficient Optical Flow Estimation. AAAI 2024.\\
FlowFormer & & 4.37 \% & 6.18 \% & 4.68 \% & 100.00 \% & 0.3 s / & Z. Huang, X. Shi, C. Zhang, Q. Wang, K. Cheung, H. Qin, J. Dai and H. Li: FlowFormer: A Transformer Architecture for Optical Flow. European conference on computer vision 2022.\\
SSTM\_T [MV] & & 4.39 \% & 6.40 \% & 4.72 \% & 100.00 \% & 0.4 s / GPU & F. Ferede and M. Balasubramanian: SSTM: Spatiotemporal recurrent transformers for multi-frame optical flow estimation. Neurocomputing 2023.\\
MatchFlow(R) & & 4.51 \% & 5.78 \% & 4.72 \% & 100.00 \% & 0.26 s / & Q. Dong, C. Cao and Y. Fu: Rethinking Optical Flow from Geometric Matching Consistent Perspective. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.\\
UberATG-DRISF & st & 3.59 \% & 10.40 \% & 4.73 \% & 100.00 \% & 0.75 s / CPU+GPU & W. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun: Deep Rigid Instance Scene Flow. CVPR 2019.\\
SSTMT++-tt-main [mv] & & 4.36 \% & 6.65 \% & 4.74 \% & 100.00 \% & 0.4 s / GPU & F. Ferede and M. Balasubramanian: SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation. arXiv preprint arXiv:2304.14418 2023.\\
RAFT-A & & 4.54 \% & 5.99 \% & 4.78 \% & 100.00 \% & 0.7 s / GPU & D. Sun, D. Vlasic, C. Herrmann, V. Jampani, M. Krainin, H. Chang, R. Zabih, W. Freeman and C. Liu: AutoFlow: Learning a Better Training Set for Optical Flow. CVPR 2021.\\
CRAFT & & 4.58 \% & 5.85 \% & 4.79 \% & 100.00 \% & 0.2 s / GPU & X. Sui, S. Li, X. Geng, Y. Wu, X. Xu, Y. Liu, R. Goh and H. Zhu: CRAFT: Cross-Attentional Flow Transformers for Robust Optical Flow. CVPR 2022.\\
GMFlowNet & & 4.39 \% & 6.84 \% & 4.79 \% & 100.00 \% & 0.5 s / GPU & S. Zhao, L. Zhao, Z. Zhang, E. Zhou and D. Metaxas: Global Matching with Overlapping Attention for Optical Flow Estimation. CVPR 2022.\\
Scale-flow-ADF58 & st & 4.36 \% & 7.00 \% & 4.80 \% & 100.00 \% & 0.1 s / 1 core & \\
ce\_skii & & 4.53 \% & 6.22 \% & 4.81 \% & 100.00 \% & 0.08 s / 1 core & \\
PLKNet & & 4.81 \% & 4.90 \% & 4.82 \% & 100.00 \% & 0.2 s / GPU & \\
SSTM++\_ttt [mv] & & 4.45 \% & 6.71 \% & 4.83 \% & 100.00 \% & 0.3 s / GPU & F. Ferede and M. Balasubramanian: SSTM: Spatiotemporal recurrent transformers for multi-frame optical flow estimation. Neurocomputing 2023.\\
llatst & & 4.51 \% & 6.57 \% & 4.85 \% & 100.00 \% & 2..4 s / 1 core & \\
CE\_SKFlow & & 4.55 \% & 6.39 \% & 4.85 \% & 100.00 \% & 0.08 s / 1 core & \\
raft-sd & & 4.55 \% & 6.40 \% & 4.86 \% & 100.00 \% & 2.4 s / 1 core & \\
MS\_RAFT & & 4.58 \% & 6.38 \% & 4.88 \% & 100.00 \% & 0.3 s / & A. Jahedi, L. Mehl, M. Rivinius and A. Bruhn: Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation. IEEE International Conference on Image Processing (ICIP) 2022.\\
AGFlow & & 4.52 \% & 6.75 \% & 4.89 \% & 100.00 \% & 0.2 s / 8 cores & A. Luo, F. Yang, K. Luo, X. Li, H. Fan and S. Liu: Learning Optical Flow with Adaptive Graph Reasoning. AAAI 2022.\\
raft-aug & & 4.56 \% & 6.56 \% & 4.89 \% & 100.00 \% & 2.4 s / 1 core & \\
OPM(C) & & 4.66 \% & 6.10 \% & 4.90 \% & 100.00 \% & ** s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DEQ-Flow-H & & 4.68 \% & 6.06 \% & 4.91 \% & 100.00 \% & 0.5 s / GPU & S. Bai, Z. Geng, Y. Savani and Z. Kolter: Deep Equilibrium Optical Flow Estimation. CVPR 2022.\\
LLA-Flow+GMA & & 4.57 \% & 6.68 \% & 4.92 \% & 100.00 \% & 0.24 s / 1 core & \\
LLA-Flow+GMAv2 & & 4.57 \% & 6.76 \% & 4.93 \% & 100.00 \% & 2.4 s / 1 core & \\
MeFlow & & 4.59 \% & 6.75 \% & 4.95 \% & 100.00 \% & 0.12 s / 1 core & \\
CSFlow & & 4.71 \% & 6.46 \% & 5.00 \% & 100.00 \% & 0.2 s / GPU & H. Shi, Y. Zhou, K. Yang, X. Yin and K. Wang: CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving. arXiv preprint arXiv:2202.00909 2022.\\
LLA-Flow & & 4.74 \% & 6.37 \% & 5.01 \% & 100.00 \% & 0.24 s / 1 core & \\
SSTM\_thes\_[mv] & & 4.58 \% & 7.20 \% & 5.02 \% & 100.00 \% & 0.3 s / GPU & F. Ferede: Multi-Frame Optical Flow Estimation Using Spatio-Temporal Transformers. 2022.\\
SSTM++\_thes\_[mv] & & 4.64 \% & 7.04 \% & 5.04 \% & 100.00 \% & 0.4 s / GPU & F. Ferede: Multi-Frame Optical Flow Estimation Using Spatio-Temporal Transformers. 2022.\\
RAFT+AOIR & & 4.68 \% & 6.99 \% & 5.07 \% & 100.00 \% & 10 s / GPU & L. Mehl, C. Beschle, A. Barth and A. Bruhn: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. SSVM 2021.\\
RAFT & & 4.74 \% & 6.87 \% & 5.10 \% & 100.00 \% & 0.2 s / GPU & Z. Teed and J. Deng: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow. ECCV 2020.\\
LOF\_S & & 4.80 \% & 6.68 \% & 5.11 \% & 100.00 \% & 0.07 s / 1 core & \\
LKCMA & & 4.85 \% & 6.65 \% & 5.15 \% & 100.00 \% & 0.2 s / GPU & \\
Scale-flow & st & 5.24 \% & 5.71 \% & 5.32 \% & 100.00 \% & 0.8 s / GPU & H. Ling, Q. Sun, Z. Ren, Y. Liu, H. Wang and Z. Wang: Scale-flow: Estimating 3D Motion from Video. Proceedings of the 30th ACM International Conference on Multimedia 2022.\\
RAFT-ADF & & 5.02 \% & 6.93 \% & 5.34 \% & 100.00 \% & 0.05 s / GPU & \\
PRAFlow\_RVC & & 5.08 \% & 7.21 \% & 5.43 \% & 100.00 \% & 0.5 s / GPU & Z. Wan, Y. Mao and Y. Dai: PRAFlow\_RVC: Pyramid Recurrent All- Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020. 2020.\\
HCVNet & & 5.24 \% & 7.01 \% & 5.54 \% & 100.00 \% & 0.24 s / 1 core & \\
RAFT-TF\_RVC & & 5.32 \% & 6.75 \% & 5.56 \% & 100.00 \% & 0.7 s / GPU & D. Sun, C. Herrmann, V. Jampani, M. Krainin, F. Cole, A. Stone, R. Jonschkowski, R. Zabih, W. Freeman and C. Liu: A TensorFlow implementation of RAFT. 2020.\\
MobileFlow & & 5.41 \% & 7.53 \% & 5.76 \% & 100.00 \% & 0.1 s / 1 core & \\
ACOSF & st & 4.56 \% & 12.00 \% & 5.79 \% & 100.00 \% & 5 min / 1 core & C. Li, H. Ma and Q. Liao: Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model. International Conference on Pattern Recognition (ICPR) 2020.\\
PPAC-HD3 & & 5.78 \% & 7.48 \% & 6.06 \% & 100.00 \% & 0.19 s / & A. Wannenwetsch and S. Roth: Probabilistic Pixel-Adaptive Refinement Networks. CVPR 2020.\\
MaskFlownet & & 5.79 \% & 7.70 \% & 6.11 \% & 100.00 \% & 0.06 s / & S. Zhao, Y. Sheng, Y. Dong, E. Chang and Y. Xu: MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
RAFT+LCT-Flow & & 5.49 \% & 9.19 \% & 6.11 \% & 100.00 \% & 0.65 s / GPU & J. Chen: Motion Estimation with L0 norm Regularization (Extended Version). IEEE 7th International Conference on Virtual Reality(ICVR) 2021.\\
RAPIDFlow & & 6.11 \% & 6.19 \% & 6.12 \% & 100.00 \% & 0.04 s / GPU & H. Morimitsu, X. Zhu, R. Cesar-Jr, X. Ji and X. Yin: RAPIDFlow: {Recurrent Adaptable Pyramids with Iterative Decoding} for Efficient Optical Flow Estimation. ICRA 2024.\\
ISF & st & 5.40 \% & 10.29 \% & 6.22 \% & 100.00 \% & 10 min / 1 core & A. Behl, O. Jafari, S. Mustikovela, H. Alhaija, C. Rother and A. Geiger: Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. International Conference on Computer Vision (ICCV) 2017.\\
VCN+LCV & & 5.75 \% & 8.80 \% & 6.25 \% & 100.00 \% & 0.26 s / 1 core & T. Xiao, J. Yuan, D. Sun, Q. Wang, X. Zhang, K. Xu and M. Yang: Learnable Cost Volume using the Cayley Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
RAFT+LCV & & 5.73 \% & 8.90 \% & 6.26 \% & 100.00 \% & 0.1 s / 1 core & T. Xiao, J. Yuan, D. Sun, Q. Wang, X. Zhang, K. Xu and M. Yang: Learnable Cost Volume using the Cayley Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
PRichFlow & & 6.18 \% & 6.89 \% & 6.30 \% & 100.00 \% & 0.1 s / & X. Wang, D. Zhu, J. Song, Y. Liu, J. Li and X. Zhang: Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement. .\\
VCN & & 5.83 \% & 8.66 \% & 6.30 \% & 100.00 \% & 0.18 s / & G. Yang and D. Ramanan: Volumetric Correspondence Networks for Optical Flow. NeurIPS 2019.\\
Stereo expansion & st & 5.83 \% & 8.66 \% & 6.30 \% & 100.00 \% & 2 s / GPU & G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow through Optical Expansion. CVPR 2020.\\
Binary TTC & st & 5.84 \% & 8.67 \% & 6.31 \% & 100.00 \% & 2 s / GPU & A. Badki, O. Gallo, J. Kautz and P. Sen: Binary TTC: A Temporal Geofence for Autonomous Navigation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
MonoComb & st & 5.84 \% & 8.67 \% & 6.31 \% & 100.00 \% & 0.58 s / & R. Schuster, C. Unger and D. Stricker: MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow. ACM Computer Science in Cars Symposium (CSCS) 2020.\\
HD^3-Flow & & 6.05 \% & 9.02 \% & 6.55 \% & 100.00 \% & 0.10 s / & Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition for Match Density Estimation. CVPR 2019.\\
HOR-RAFT & & 6.22 \% & 8.41 \% & 6.59 \% & 100.00 \% & 0.1 s / 1 core & \\
PRSM & st mv & 5.33 \% & 13.40 \% & 6.68 \% & 100.00 \% & 300 s / 1 core & C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. ijcv 2015.\\
MaskFlownet-S & & 6.53 \% & 8.21 \% & 6.81 \% & 100.00 \% & 0.03 s / & S. Zhao, Y. Sheng, Y. Dong, E. Chang and Y. Xu: MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
ScopeFlow & & 6.72 \% & 7.36 \% & 6.82 \% & 100.00 \% & -1 s / & A. Bar-Haim and L. Wolf: ScopeFlow: Dynamic Scene Scoping for Optical Flow. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
SMURF & & 6.04 \% & 10.75 \% & 6.83 \% & 100.00 \% & .2 s / 1 core & A. Stone, D. Maurer, A. Ayvaci, A. Angelova and R. Jonschkowski: SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
OSF+TC & st mv & 5.76 \% & 13.31 \% & 7.02 \% & 100.00 \% & 50 min / 1 core & M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.\\
DPCTF-F & & 7.22 \% & 6.47 \% & 7.09 \% & 100.00 \% & 0.07 s / GPU & Y. Deng, J. Xiao, S. Zhou and J. Feng: Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow. IEEE Transactions on Image Processing 2021.\\
SSF & st & 5.63 \% & 14.71 \% & 7.14 \% & 100.00 \% & 5 min / 1 core & Z. Ren, D. Sun, J. Kautz and E. Sudderth: Cascaded Scene Flow Prediction using Semantic Segmentation. International Conference on 3D Vision (3DV) 2017.\\
MFF & mv & 7.15 \% & 7.25 \% & 7.17 \% & 100.00 \% & 0.05 s / & Z. Ren, O. Gallo, D. Sun, M. Yang, E. Sudderth and J. Kautz: A Fusion Approach for Multi-Frame Optical Flow Estimation. IEEE Winter Conference on Applications of Computer Vision 2019.\\
LiteFlowNet3-S & & 7.27 \% & 6.96 \% & 7.22 \% & 100.00 \% & 0.07s / & T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation. European Conference on Computer Vision (ECCV) 2020.\\
PMC-PWC & & 7.27 \% & 6.94 \% & 7.22 \% & 100.00 \% & TBD s / GPU & C. Zhang, C. Feng, Z. Chen, W. Hu and M. Li: Parallel multiscale context-based edge- preserving optical flow estimation with occlusion detection. Signal Processing: Image Communication 2022.\\
SwiftFlow & & 6.85 \% & 9.11 \% & 7.23 \% & 100.00 \% & 0.03 s / GPU & H. Wang, Y. Liu, H. Huang, Y. Pan, W. Yu, J. Jiang, D. Lyu, M. Bocus, M. Liu, I. Pitas and others: ATG-PVD: Ticketing parking violations on a drone. European Conference on Computer Vision 2020.\\
LiteFlowNet3 & & 7.26 \% & 7.75 \% & 7.34 \% & 100.00 \% & 0.07s / & T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation. European Conference on Computer Vision (ECCV) 2020.\\
UFD-PRiME & st & 5.85 \% & 14.91 \% & 7.36 \% & 100.00 \% & 0.56 s / GPU & \\
OSF 2018 & st & 5.38 \% & 17.61 \% & 7.41 \% & 100.00 \% & 390 s / 1 core & M. Menze, C. Heipke and A. Geiger: Object Scene Flow. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 2018.\\
LiteFlowNet2 & & 7.62 \% & 7.64 \% & 7.62 \% & 100.00 \% & 0.0486 s / & T. Hui, X. Tang and C. Loy: A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization. TPAMI 2020.\\
UFD-PRiME & st & 5.96 \% & 15.96 \% & 7.63 \% & 100.00 \% & 0.55 s / GPU & \\
SENSE & st & 7.30 \% & 9.33 \% & 7.64 \% & 100.00 \% & 0.32s / & H. Jiang, D. Sun, V. Jampani, Z. Lv, E. Learned-Miller and J. Kautz: SENSE: A Shared Encoder Network for Scene-Flow Estimation. The IEEE International Conference on Computer Vision (ICCV) 2019.\\
IRR-PWC & & 7.68 \% & 7.52 \% & 7.65 \% & 100.00 \% & 0.18 s / & J. Hur and S. Roth: Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019.\\
STaRFlow & & 7.51 \% & 8.35 \% & 7.65 \% & 100.00 \% & 0.24 s / GPU & P. Godet, A. Boulch, A. Plyer and G. Besnerais: STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation. ICPR 2020.\\
DTF\_SENSE & st mv & 7.31 \% & 9.48 \% & 7.67 \% & 100.00 \% & 0.76 s / 1 core & R. Schuster, C. Unger and D. Stricker: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.\\
PWC-Net+ & & 7.69 \% & 7.88 \% & 7.72 \% & 100.00 \% & 0.03 s / & D. Sun, X. Yang, M. Liu and J. Kautz: Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation. arXiv preprint arXiv:1809.05571 2018.\\
raft-s & & 7.30 \% & 10.02 \% & 7.76 \% & 100.00 \% & 0.1 s / 1 core & \\
UnSAMFlow & & 6.40 \% & 14.98 \% & 7.83 \% & 100.00 \% & 0.03 s / GPU & \\
OSF & st & 5.62 \% & 18.92 \% & 7.83 \% & 100.00 \% & 50 min / 1 core & M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.\\
Separable-Sim2real & & 7.30 \% & 11.01 \% & 7.92 \% & 100.00 \% & 0.25 s / & F. Zhang, O. Woodford, V. Prisacariu and P. Torr: Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
LSM\_FLOW\_RVC & & 7.33 \% & 13.06 \% & 8.28 \% & 100.00 \% & 0.2 s / 1 core & C. Tang, L. Yuan and P. Tan: LSM: Learning Subspace Minimization for Low-Level Vision. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
AL-OF\_r0.2 & & 7.25 \% & 13.53 \% & 8.30 \% & 100.00 \% & 0.1 s / 1 core & S. Yuan, X. Sun, H. Kim, S. Yu and C. Tomasi: Optical Flow Training Under Limited Label Budget via Active Learning. ECCV 2022.\\
IRR-PWC\_RVC & & 7.61 \% & 12.22 \% & 8.38 \% & 100.00 \% & 0.18 s / & J. Hur and S. Roth: Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019.\\
SemARFlow & & 7.48 \% & 12.91 \% & 8.38 \% & 100.00 \% & 0.0168s / GPU & S. Yuan, S. Yu, H. Kim and C. Tomasi: SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving. ICCV 2023.\\
SelFlow & mv & 7.61 \% & 12.48 \% & 8.42 \% & 100.00 \% & 0.09 s / GPU & P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical Flow. CVPR 2019.\\
MDFlow & & 8.14 \% & 12.80 \% & 8.91 \% & 100.00 \% & 0.03 s / & L. Kong and J. Yang: MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation. IEEE Transactions on Circuits and Systems for Video Technology 2022.\\
GMFlow & & 9.67 \% & 7.57 \% & 9.32 \% & 100.00 \% & 0.071 s / & H. Xu, J. Zhang, J. Cai, H. Rezatofighi and D. Tao: GMFlow: Learning Optical Flow via Global Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.\\
FDFlowNet & & 9.31 \% & 9.71 \% & 9.38 \% & 100.00 \% & 0.02 s / & L. Kong and J. Yang: FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network. IEEE International Conference on Image Processing (ICIP) 2020.\\
LiteFlowNet & & 9.66 \% & 7.99 \% & 9.38 \% & 100.00 \% & 0.0885 s / & T. Hui, X. Tang and C. Loy: LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.\\
PWC-Net & & 9.66 \% & 9.31 \% & 9.60 \% & 100.00 \% & 0.03 s / & D. Sun, X. Yang, M. Liu and J. Kautz: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. CVPR 2018.\\
NeuFlow & & 9.49 \% & 11.82 \% & 9.88 \% & 100.00 \% & 0.01 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
ContinualFlow\_ROB & mv & 8.54 \% & 17.48 \% & 10.03 \% & 100.00 \% & 0.15 s / & M. Neoral, J. Šochman and J. Matas: Continual Occlusions and Optical Flow Estimation. 14th Asian Conference on Computer Vision (ACCV) 2018.\\
VCN\_RVC & & 8.53 \% & 18.30 \% & 10.15 \% & 100.00 \% & 0.36 s / GPU & G. Yang and D. Ramanan: Volumetric Correspondence Networks for Optical Flow. NeurIPS 2019.\\
NccFLow & & 8.81 \% & 17.36 \% & 10.24 \% & 100.00 \% & 0.04 s / 1 core & G. Wang, S. Ren and H. Wang: NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry. arXiv preprint arXiv:2107.03610 2021.\\
MirrorFlow & & 8.93 \% & 17.07 \% & 10.29 \% & 100.00 \% & 11 min / 4 core & J. Hur and S. Roth: MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017.\\
CoT-AMFlow & & 10.02 \% & 11.95 \% & 10.34 \% & 100.00 \% & 0.06 s / GPU & H. Wang, R. Fan and M. Liu: CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation. Conference on Robot Learning (CoRL) 2020.\\
DWARF & st & 9.80 \% & 13.37 \% & 10.39 \% & 100.00 \% & 0.14s - 1.43s / & F. Aleotti, M. Poggi, F. Tosi and S. Mattoccia: Learning end-to-end scene flow by distilling single tasks knowledge. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) 2020.\\
FlowNet2 & & 10.75 \% & 8.75 \% & 10.41 \% & 100.00 \% & 0.1 s / GPU & E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy and T. Brox: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.\\
SDF & & 8.61 \% & 23.01 \% & 11.01 \% & 100.00 \% & TBA / 1 core & M. Bai*, W. Luo*, K. Kundu and R. Urtasun: Exploiting Semantic Information and Deep Matching for Optical Flow. ECCV 2016.\\
Flow2Stereo & & 9.99 \% & 16.67 \% & 11.10 \% & 100.00 \% & 0.05 s / GPU & P. Liu, I. King, M. Lyu and J. Xu: Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching. CVPR 2020.\\
UnFlow & & 10.15 \% & 15.93 \% & 11.11 \% & 100.00 \% & 0.12 s / GPU & S. Meister, J. Hur and S. Roth: UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss. AAAI 2018.\\
UFlow & & 9.78 \% & 17.87 \% & 11.13 \% & 100.00 \% & 0.04 s / 1 core & R. Jonschkowski, A. Stone, J. Barron, A. Gordon, K. Konolige and A. Angelova: What Matters in Unsupervised Optical Flow. ECCV 2020.\\
FastFlowNet & & 11.20 \% & 11.30 \% & 11.22 \% & 100.00 \% & 0.01 s / & L. Kong, C. Shen and J. Yang: FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation. 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021.\\
FSF+MS & st ms mv & 8.48 \% & 25.43 \% & 11.30 \% & 100.00 \% & 2.7 s / 4 cores & T. Taniai, S. Sinha and Y. Sato: Fast Multi-frame Stereo Scene Flow with Motion Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) 2017.\\
MDFlow-Fast & & 10.75 \% & 14.81 \% & 11.43 \% & 100.00 \% & 0.01 s / & L. Kong and J. Yang: MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation. IEEE Transactions on Circuits and Systems for Video Technology 2022.\\
CNNF+PMBP & & 10.08 \% & 18.56 \% & 11.49 \% & 100.00 \% & 45 min / 1 cores & F. Zhang and B. Wah: Fundamental Principles on Learning New Features for Effective Dense Matching. IEEE Transactions on Image Processing 2018.\\
MaxFlow & & 12.02 \% & 9.05 \% & 11.52 \% & 100.00 \% & 1 s / GPU & \\
PWC-Net\_RVC & & 11.22 \% & 13.69 \% & 11.63 \% & 100.00 \% & 0.03 s / & D. Sun, X. Yang, M. Liu and J. Kautz: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. CVPR 2018.\\
SFF++ & st mv & 10.63 \% & 17.48 \% & 11.77 \% & 100.00 \% & 78 s / 4 cores & R. Schuster, O. Wasenmüller, C. Unger, G. Kuschk and D. Stricker: SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation. International Journal of Computer Vision (IJCV) 2019.\\
SfM-PM & mv & 9.66 \% & 22.73 \% & 11.83 \% & 100.00 \% & 69 s / 3 cores & D. Maurer, N. Marniok, B. Goldluecke and A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation. ECCV 2018.\\
Self-SuperFlow-ft & st & 10.65 \% & 19.44 \% & 12.12 \% & 100.00 \% & 0.13 s / & K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. International Conference on Image Processing (ICIP) 2022.\\
MR-Flow & mv & 10.13 \% & 22.51 \% & 12.19 \% & 100.00 \% & 8 min / 1 core & J. Wulff, L. Sevilla-Lara and M. Black: Optical Flow in Mostly Rigid Scenes. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2017.\\
DTF\_PWOC & st mv & 10.78 \% & 19.99 \% & 12.31 \% & 100.00 \% & 0.38 s / & R. Schuster, C. Unger and D. Stricker: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.\\
Mono-SF & st & 11.40 \% & 19.64 \% & 12.77 \% & 100.00 \% & 41 s / 1 core & F. Brickwedde, S. Abraham and R. Mester: Mono-SF: Multi-View Geometry meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes. Proc. of International Conference on Computer Vision (ICCV) 2019.\\
SceneFFields & st & 10.58 \% & 24.41 \% & 12.88 \% & 100.00 \% & 65 s / 4 cores & R. Schuster, O. Wasenmüller, G. Kuschk, C. Bailer and D. Stricker: SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences. IEEE Winter Conference on Applications of Computer Vision (WACV) 2018.\\
CSF & st & 10.40 \% & 25.78 \% & 12.96 \% & 100.00 \% & 80 s / 1 core & Z. Lv, C. Beall, P. Alcantarilla, F. Li, Z. Kira and F. Dellaert: A Continuous Optimization Approach for Efficient and Accurate Scene Flow. European Conf. on Computer Vision (ECCV) 2016.\\
PWOC-3D & st & 12.40 \% & 15.78 \% & 12.96 \% & 100.00 \% & 0.13 s / & R. Saxena, R. Schuster, O. Wasenmüller and D. Stricker: PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation. Intelligent Vehicles Symposium (IV) 2019.\\
Multi-Mono-SF-ft & st mv & 12.41 \% & 18.20 \% & 13.37 \% & 100.00 \% & 0.06 s / & J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.\\
UnsupSimFlow & & 12.60 \% & 17.27 \% & 13.38 \% & 100.00 \% & 0.03 s / 8 cores & W. Im, T. Kim and S. Yoon: Unsupervised Learning of Optical Flow with Deep Feature Similarity. The European Conference on Computer Vision (ECCV) 2020.\\
CompactFlowNet & & 12.36 \% & 18.51 \% & 13.39 \% & 100.00 \% & 0.01 s / 1 core & \\
Self-scale-flow-nerf & st & 13.08 \% & 15.45 \% & 13.47 \% & 100.00 \% & 0.2 s / 1 core & \\
Nerf-self & & 13.35 \% & 14.65 \% & 13.57 \% & 100.00 \% & 0.1 s / 1 core & \\
PR-Sceneflow & st & 11.73 \% & 24.33 \% & 13.83 \% & 100.00 \% & 150 s / 4 core & C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.\\
DDFlow+LCV & & 12.98 \% & 19.83 \% & 14.12 \% & 100.00 \% & 0.1 s / GPU & T. Xiao, J. Yuan, D. Sun, Q. Wang, X. Zhang, K. Xu and M. Yang: Learnable Cost Volume using the Cayley Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
SelFlow & mv & 12.68 \% & 21.74 \% & 14.19 \% & 100.00 \% & 0.09 s / GPU & P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical Flow. CVPR 2019.\\
DDFlow & & 13.08 \% & 20.40 \% & 14.29 \% & 100.00 \% & 0.06 s / GPU & P. Liu, I. King, M. Lyu and J. Xu: DDFlow: Learning Optical Flow with Unlabeled Data Distillation. AAAI 2019.\\
DCFlow & & 13.10 \% & 23.70 \% & 14.86 \% & 100.00 \% & 8.6 s / GPU & J. Xu, R. Ranftl and V. Koltun: Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.\\
ProFlow & mv & 13.86 \% & 20.91 \% & 15.04 \% & 100.00 \% & 112 s / GPU+CPU & D. Maurer and A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.\\
SwiftStream & & 15.32 \% & 13.83 \% & 15.07 \% & 100.00 \% & 0.01 s / GPU & \\
FlowFields++ & & 14.82 \% & 17.77 \% & 15.31 \% & 100.00 \% & 29 s / 1 core & R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. International Conference on Image Processing (ICIP) 2018.\\
ProFlow\_ROB & mv & 14.15 \% & 21.82 \% & 15.42 \% & 100.00 \% & 112 s / GPU+CPU & D. Maurer and A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.\\
Self-Mono-SF-ft & st & 15.51 \% & 17.96 \% & 15.91 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
FF++\_ROB & & 15.32 \% & 19.27 \% & 15.97 \% & 100.00 \% & 29 s / 1 core & R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. International Conference on Image Processing (ICIP) 2018.\\
SOF & & 14.63 \% & 22.83 \% & 15.99 \% & 100.00 \% & 6 min / 1 core & L. Sevilla-Lara, D. Sun, V. Jampani and M. Black: Optical Flow with Semantic Segmentation and Localized Layers. CVPR 2016.\\
GMFlow+ADF58 & & 15.70 \% & 18.41 \% & 16.15 \% & 100.00 \% & 0.1 s / 1 core & \\
DIP-Flow-DF & mv & 14.93 \% & 23.37 \% & 16.33 \% & 100.00 \% & 104s / 2 cores & D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.\\
JFS & ms & 15.90 \% & 19.31 \% & 16.47 \% & 100.00 \% & 13 min / 1 core & J. Hur and S. Roth: Joint Optical Flow and Temporally Consistent Semantic Segmentation. ECCV Workshops 2016.\\
DF+OIR & & 15.11 \% & 23.45 \% & 16.50 \% & 100.00 \% & 3 min / 1 core & D. Maurer, M. Stoll and A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.\\
SPS+FF++ & st & 15.91 \% & 20.27 \% & 16.64 \% & 100.00 \% & 36 s / 1 core & R. Schuster, O. Wasenmüller and D. Stricker: Dense Scene Flow from Stereo Disparity and Optical Flow. ACM Computer Science in Cars Symposium (CSCS) 2018.\\
DIP-Flow-CPM & mv & 15.57 \% & 23.84 \% & 16.95 \% & 100.00 \% & 52 s / 2 core & D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.\\
ImpPB+SPCI & & 17.25 \% & 20.44 \% & 17.78 \% & 100.00 \% & 60 s / GPU & T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.\\
Anonymous & st & 17.91 \% & 18.08 \% & 17.93 \% & 100.00 \% & 0.1 s / GPU & \\
PCOF-LDOF & st & 14.34 \% & 38.32 \% & 18.33 \% & 100.00 \% & 50 s / 1 core & M. Derome, A. Plyer, M. Sanfourche and G. Le Besnerais: A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. German Conference on Pattern Recognition 2016.\\
RAFT-MSF & st & 17.98 \% & 20.33 \% & 18.37 \% & 100.00 \% & 0.18 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
FlowFieldCNN & & 18.33 \% & 20.42 \% & 18.68 \% & 100.00 \% & 23 s / GPU/CPU 4 core & C. Bailer, K. Varanasi and D. Stricker: CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.\\
RicFlow & & 18.73 \% & 19.09 \% & 18.79 \% & 100.00 \% & 5 s / 1 core & Y. Hu, Y. Li and R. Song: Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.\\
HCSH & & 18.05 \% & 26.23 \% & 19.41 \% & 100.00 \% & 3.5 s / 1 core & J. Fan, Y. Wang and L. Guo: Hierarchical coherency sensitive hashing and interpolation with RANSAC for large displacement optical flow. Computer Vision and Image Understanding 2018.\\
OmegaNet & & 17.43 \% & 29.69 \% & 19.47 \% & 100.00 \% & 0.01 s / GPU & F. Tosi, F. Aleotti, P. Ramirez, M. Poggi, S. Salti, L. Di Stefano and S. Mattoccia: Distilled semantics for comprehensive scene understanding from videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2020.\\
UJG & & 18.57 \% & 24.02 \% & 19.48 \% & 100.00 \% & 0.03 s / GPU & J. Li, J. Zhao, S. Song and T. Feng: Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video. arXiv preprint arXiv:2105.14520 2021.\\
Multi-Mono-SF & st mv & 18.13 \% & 26.59 \% & 19.54 \% & 100.00 \% & 0.06 s / & J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.\\
PGM-G & & 18.90 \% & 23.43 \% & 19.66 \% & 100.00 \% & 5.05 s / 1 core & Y. Li: Pyramidal Gradient Matching for Optical Flow Estimation. CoRR 2017.\\
FlowFields+ & & 19.51 \% & 21.26 \% & 19.80 \% & 100.00 \% & 28s / 1 core & C. Bailer, B. Taetz and D. Stricker: Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. .\\
EPC++ (stereo) & st & 19.24 \% & 26.93 \% & 20.52 \% & 100.00 \% & 0.05 s / GPU & C. Luo, Z. Yang, P. Wang, Y. Wang, W. Xu, R. Nevatia and A. Yuille: Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding. IEEE transactions on pattern analysis and machine intelligence 2019.\\
PatchBatch & & 19.98 \% & 26.50 \% & 21.07 \% & 100.00 \% & 50 s / GPU & D. Gadot and L. Wolf: PatchBatch: a Batch Augmented Loss for Optical Flow. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.\\
DDF & & 20.36 \% & 25.19 \% & 21.17 \% & 100.00 \% & ~1 min / GPU & F. G\"uney and A. Geiger: Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.\\
SODA-Flow & & 20.01 \% & 29.14 \% & 21.53 \% & 100.00 \% & 96 s / 2 cores & D. Maurer, M. Stoll, S. Volz, P. Gairing and A. Bruhn: A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow. SSVM 2017.\\
DiscreteFlow & & 21.53 \% & 21.76 \% & 21.57 \% & 100.00 \% & 3 min / 1 core & M. Menze, C. Heipke and A. Geiger: Discrete Optimization for Optical Flow. German Conference on Pattern Recognition (GCPR) 2015.\\
SGM+SF & st & 20.91 \% & 25.50 \% & 21.67 \% & 100.00 \% & 45 min / 16 core & H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.M. Hornacek, A. Fitzgibbon and C. Rother: SphereFlow: 6 DoF Scene Flow from RGB-D Pairs. CVPR 2014.\\
OAR-Flow & & 20.62 \% & 27.67 \% & 21.79 \% & 100.00 \% & 100 s / 2 cores & D. Maurer, M. Stoll and A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.\\
CPM-Flow & & 22.32 \% & 22.81 \% & 22.40 \% & 100.00 \% & 4.2 s / 1 core & Y. Hu, R. Song and Y. Li: Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.\\
PCOF + ACTF & st & 14.89 \% & 60.15 \% & 22.43 \% & 100.00 \% & 0.08 s / GPU & M. Derome, A. Plyer, M. Sanfourche and G. Le Besnerais: A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. German Conference on Pattern Recognition 2016.\\
SegFlow(d0=3) & & 22.21 \% & 23.72 \% & 22.46 \% & 100.00 \% & 6.6 s / 1 core & J. Chen, Z. Cai, J. Lai and X. Xie: Efficient Segmentation-based PatchMatch for Large displacement Optical Flow Estimation. IEEE TCSVT 2018.\\
IntrpNt-df & & 22.15 \% & 26.03 \% & 22.80 \% & 100.00 \% & 3 min / GPU & S. Zweig and L. Wolf: InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.\\
SGM&FlowFie+ & st & 22.83 \% & 22.75 \% & 22.82 \% & 81.24 \% & 29 s / 1 core & R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: Combining Stereo Disparity and Optical Flow for Basic Scene Flow. Commercial Vehicle Technology Symposium (CVTS) 2018.\\
Back2FutureFlow(UFO) & mv & 22.67 \% & 24.27 \% & 22.94 \% & 100.00 \% & 0.12 s / GPU & J. Janai, F. Güney, A. Ranjan, M. Black and A. Geiger: Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. Proc. of the European Conf. on Computer Vision (ECCV) 2018.\\
MotionSLIC & ms & 14.86 \% & 64.44 \% & 23.11 \% & 100.00 \% & 30 s / 4 cores & K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.\\
IntrpNt-cpm & & 22.51 \% & 26.54 \% & 23.18 \% & 100.00 \% & 5.6 s / GPU & S. Zweig and L. Wolf: InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.\\
FullFlow & & 23.09 \% & 24.79 \% & 23.37 \% & 100.00 \% & 4 min / 4 cores & Q. Chen and V. Koltun: Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.\\
HiLM & & 23.73 \% & 21.79 \% & 23.41 \% & 100.00 \% & 8 sec / & M. Fathy, Q. Tran, M. Zia, P. Vernaza and M. Chandraker: Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences. European Conference on Computer Vision (ECCV) 2018.\\
Self-Mono-SF & st & 23.26 \% & 24.93 \% & 23.54 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
Self-SuperFlow & st & 22.70 \% & 28.55 \% & 23.67 \% & 100.00 \% & 0.13 s / & K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. International Conference on Image Processing (ICIP) 2022.\\
IntrpNt-dm & & 23.46 \% & 26.27 \% & 23.93 \% & 100.00 \% & 15 s / GPU & S. Zweig and L. Wolf: InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.\\
SPM-BP & & 24.06 \% & 24.97 \% & 24.21 \% & 100.00 \% & 10 s / 2 cores & Y. Li, D. Min, M. Brown, M. Do and J. Lu: SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs. Proceedings of the IEEE International Conference on Computer Vision 2015.\\
PPM & & 25.87 \% & 23.67 \% & 25.50 \% & 100.00 \% & 17.3 s / 1 core & F. Kuang: PatchMatch algorithms for motion estimation and stereo reconstruction. 2017.\\
3DFlow & & 25.56 \% & 29.33 \% & 26.19 \% & 100.00 \% & 448s / & J. Chen, Z. Cai, J. Lai and X. Xie: A Filtering Based Framework for Optical Flow Estimation. IEEE TCSVT 2018.\\
EpicFlow & & 25.81 \% & 28.69 \% & 26.29 \% & 100.00 \% & 15 s / 1 core & J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid: EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015 - IEEE Conference on Computer Vision \& Pattern Recognition 2015.\\
SegFlow(d0=11) & & 28.97 \% & 22.64 \% & 27.91 \% & 100.00 \% & 4.5 s / 1 core & J. Chen, Z. Cai, J. Lai and X. Xie: Efficient Segmentation-based PatchMatch for Large displacement Optical Flow Estimation. IEEE TCSVT 2018.\\
DeepFlow & & 27.96 \% & 31.06 \% & 28.48 \% & 100.00 \% & 17 s / 1 core & P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid: DeepFlow: Large displacement optical flow with deep matching. IEEE Intenational Conference on Computer Vision (ICCV) 2013.\\
CPNFlow & & 31.05 \% & 27.16 \% & 30.40 \% & 100.00 \% & 0.1 s / GPU & Y. Yang and S. Soatto: Conditional prior networks for optical flow. Proceedings of the European Conference on Computer Vision (ECCV) 2018.\\
IIOF-NLDP & & 30.23 \% & 32.44 \% & 30.60 \% & 100.00 \% & 350 s / 4 cores & D. Trinh, W. Blondel and C. Daul: A General Form of Illumination- Invariant Descriptors in Variational Optical Flow Estimation. IEEE Int. Conf. on Image Processing (ICIP) 2017.\\
DMF\_ROB & & 30.74 \% & 30.07 \% & 30.63 \% & 100.00 \% & 150 s / 1 core & P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid: DeepFlow: Large displacement optical flow with deep matching. ICCV - IEEE International Conference on Computer Vision 2013.\\
SPyNet & & 33.36 \% & 43.62 \% & 35.07 \% & 100.00 \% & 0.16 s / 1 core & A. Ranjan and M. Black: Optical Flow Estimation using a Spatial Pyramid Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.\\
SGM+C+NL & st & 34.24 \% & 42.46 \% & 35.61 \% & 93.83 \% & 4.5 min / 1 core & H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them. IJCV 2013.\\
3DG-DVO & st & 34.04 \% & 50.52 \% & 36.78 \% & 100.00 \% & 0.04 s / GPU & \\
DWBSF & st & 40.74 \% & 31.16 \% & 39.14 \% & 100.00 \% & 7 min / 4 cores & C. Richardt, H. Kim, L. Valgaerts and C. Theobalt: Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras. 3DV 2016.\\
SGM+LDOF & st & 40.81 \% & 31.92 \% & 39.33 \% & 95.89 \% & 86 s / 1 core & H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.T. Brox and J. Malik: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. PAMI 2011.\\
HS & & 39.90 \% & 51.39 \% & 41.81 \% & 100.00 \% & 2.6 min / 1 core & D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and The Principles Behind Them. 2014.\\
GCSF & st & 47.38 \% & 41.50 \% & 46.40 \% & 100.00 \% & 2.4 s / 1 core & J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.\\
DB-TV-L1 & & 47.52 \% & 48.27 \% & 47.64 \% & 100.00 \% & 16 s / 1 core & C. Zach, T. Pock and H. Bischof: A Duality Based Approach for Realtime TV- L1 Optical Flow. DAGM 2007.\\
VSF & st & 50.06 \% & 45.40 \% & 49.28 \% & 100.00 \% & 125 min / 1 core & F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.\\
HAOF & & 49.89 \% & 50.74 \% & 50.04 \% & 100.00 \% & 16.2 s / 1 core & T. Brox, A. Bruhn, N. Papenberg and J. Weickert: High accuracy optical flow estimation based on a theory for warping. ECCV 2004.\\
TVL1\_ROB & & 51.15 \% & 51.12 \% & 51.14 \% & 100.00 \% & 3 s / 4 cores & J. Sánchez Pérez, E. Meinhardt-Llopis and G. Facciolo: TV-L1 Optical Flow Estimation. Image Processing On Line 2013.\\
PolyExpand & & 52.00 \% & 58.56 \% & 53.09 \% & 100.00 \% & 1 s / 1 core & G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion. SCIA 2003.\\
H+S\_ROB & & 68.22 \% & 76.49 \% & 69.60 \% & 100.00 \% & 8 s / 4 cores & E. Meinhardt-Llopis, J. Sánchez Pérez and D. Kondermann: Horn-Schunck Optical Flow with a Multi-Scale Strategy. Image Processing On Line 2013.\\
Stereo-RSSF & st & 70.68 \% & 73.60 \% & 71.17 \% & 9.26 \% & 2.5 s / 8 core & E. Salehi, A. Aghagolzadeh and R. Hosseini: Stereo-RSSF: stereo robust sparse scene-flow estimation. The Visual Computer 2023.\\
Pyramid-LK & & 71.84 \% & 76.82 \% & 72.67 \% & 100.00 \% & 1.5 min / 1 core & J. Bouguet: Pyramidal implementation of the Lucas Kanade feature tracker. Intel 2000.\\
MEDIAN & & 87.37 \% & 92.80 \% & 88.27 \% & 99.86 \% & 0.01 s / 1 core & \\
AVERAGE & & 88.47 \% & 92.08 \% & 89.07 \% & 99.86 \% & 0.01 s / 1 core &
\end{tabular}