Method
Setting
Code
Fl-bg
Fl-fg
Fl-all Density
Runtime
Environment
1
RBO 1.65 %
3.94 %
2.06 % 100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
2
CCMR+ code 1.89 %
2.88 %
2.07 %
100.00 %
1.5 s
GPU @ 2.5 Ghz (Python)
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.
3
GAOSF 1.61 % 4.48 %
2.13 %
100.00 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
4
ScaleRAFTRBO code 1.75 %
3.85 %
2.13 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
5
SplatFlow3D code 1.78 %
3.80 %
2.14 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
6
CamLiRAFT code 1.64 %
4.57 %
2.18 %
100.00 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow
with Bidirectional Camera-LiDAR Fusion . TPAMI 2023.
7
MS_RAFT+_corr_RVC code 2.07 %
2.69 %
2.18 %
100.00 %
0.65 s
GPU @ 2.5 Ghz (Python + C/C++)
A. Jahedi, M. Luz, M. Rivinius, L. Mehl and A. Bruhn: High Resolution Multi-Scale RAFT . International Journal of Computer Vision (IJCV) 2023.
8
DDVM 1.97 %
3.46 %
2.24 %
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.
9
EFLOW 1.77 %
4.51 %
2.27 %
100.00 %
0.06 s
1 core @ 2.5 Ghz (Python)
10
RRTC 2.24 %
2.84 %
2.35 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
11
MonoFusion 2.19 %
3.10 %
2.35 %
100.00 %
0.7 s
GPU @ 2.5 Ghz (Python)
12
RAFT-3D++ 1.63 %
5.71 %
2.37 %
100.00 %
0.5 s
1 core @ 2.5 Ghz (Python)
13
EMD-L 2.18 %
3.25 %
2.37 %
100.00 %
0.14 s
GPU @ 2.5 Ghz (Python)
14
CamLiRAFT-NR code 2.03 %
3.98 %
2.38 %
100.00 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
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.
15
CamLiFlow code 1.79 %
5.12 %
2.40 %
100.00 %
1.2 s
GPU @ 2.5 Ghz (Python + C/C++)
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.
16
GMFlow+ code 2.29 %
2.89 %
2.40 %
100.00 %
0.2 s
GPU (Python)
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.
17
CroCo-Flow code 2.05 %
3.98 %
2.40 %
100.00 %
3s
NVIDIA A100
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.
18
RAFT2-L 2.39 %
2.58 % 2.43 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
19
DIP code 2.31 %
3.00 %
2.43 %
100.00 %
0.15 s
1 core @ 2.5 Ghz (Python)
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.
20
MemFlow-T code 2.28 %
3.22 %
2.45 %
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.
21
RAFT-it+_RVC code 2.36 %
2.87 %
2.46 %
100.00 %
0.14 s
1 core @ 2.5 Ghz (Python)
D. Sun, C. Herrmann, F. Reda, M. Rubinstein, D. Fleet and W. Freeman: Disentangling Architecture and Training for
Optical Flow . ECCV 2022.
22
app+mo1 2.34 %
3.11 %
2.48 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
23
M-FUSE code 1.98 %
4.74 %
2.48 %
100.00 %
1.3 s
GPU
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.
24
Promotion 2.40 %
2.94 %
2.50 %
100.00 %
0.25 s
1 core @ 2.5 Ghz (Python)
25
CCAFlow 2.31 %
3.36 %
2.50 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
26
ap+m 2.38 %
3.27 %
2.54 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
27
ProtoFormer 2.43 %
3.04 %
2.54 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
28
RigidMask+ISF code 2.03 %
4.85 %
2.54 %
100.00 %
3.3 s
GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Learning to Segment Rigid Motions from Two
Frames . CVPR 2021.
29
MemFlow code 2.37 %
3.40 %
2.56 %
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.
30
SGFlow 2.46 %
3.02 %
2.57 %
100.00 %
0.15 s
1 core @ 2.5 Ghz (Python)
31
RAFT-OCTC 2.54 %
2.80 %
2.58 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
J. Jeong, J. Lin, F. Porikli and N. Kwak: Imposing Consistency for Optical Flow
Estimation (Qualcomm AI Research) . CVPR 2022.
32
app 2.50 %
3.08 %
2.61 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
33
DFFlow 2.47 %
3.29 %
2.62 %
100.00 %
1 s
1 core @ 2.5 Ghz (Python)
34
PFlowFormer 2.55 %
2.97 %
2.63 %
100.00 %
0.44 s
GPU (Python)
35
StreamFlow 2.50 %
3.23 %
2.63 %
100.00 %
0.08 s
1 core @ 2.5 Ghz (Python)
36
GMFlow_RVC code 2.55 %
2.98 %
2.63 %
100.00 %
0.2 s
GPU (Python)
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.
37
HD-Flow 2.45 %
3.66 %
2.67 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
38
MMAFlow 2.60 %
3.09 %
2.69 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
39
FlowFormer code 2.54 %
3.38 %
2.69 %
100.00 %
0.3 s
GPU (Python)
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.
40
AnyFlow 2.60 %
3.10 %
2.69 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
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.
41
RPKNet code 2.70 %
2.74 %
2.71 %
100.00 %
0.6 s
GPU @ 2.5 Ghz (Python)
H. Morimitsu, X. Zhu, X. Ji and X. Yin: Recurrent Partial Kernel Network for
Efficient Optical Flow Estimation . AAAI 2024.
42
ACR-Net 2.60 %
3.28 %
2.72 %
100.00 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
43
f 2.65 %
3.13 %
2.73 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
44
PVTFlow 2.56 %
3.60 %
2.75 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
45
GMFlowNet code 2.56 %
3.64 %
2.75 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (Python)
S. Zhao, L. Zhao, Z. Zhang, E. Zhou and D. Metaxas: Global Matching with Overlapping Attention
for Optical Flow Estimation . CVPR 2022.
46
DF-Flow 2.58 %
3.53 %
2.76 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
47
MatchFlow(R) code 2.59 %
3.54 %
2.76 %
100.00 %
0.26 s
GPU (Python)
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.
48
MatchFlow(G) code 2.61 %
3.48 %
2.77 %
100.00 %
0.3 s
GPU (Python)
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.
49
SeparableFlow code 2.56 %
3.75 %
2.78 %
100.00 %
0.5 s
GPU
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.
50
ASFlow 2.68 %
3.25 %
2.78 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
51
PLKNet 2.74 %
2.96 %
2.78 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
52
SCFlow 2.68 %
3.24 %
2.79 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
53
MS_RAFT 2.66 %
3.41 %
2.80 %
100.00 %
0.3 s
GPU: Nvidia A100 (Python)
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.
54
FlowDiffuser_sub 2.39 %
4.71 %
2.82 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (Python)
55
KPA-Flow 2.59 %
3.83 %
2.82 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
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.
56
llatst 2.65 %
3.69 %
2.84 %
100.00 %
2..4 s
1 core @ 2.5 Ghz (C/C++)
57
LLA-Flow+GMA 2.62 %
3.83 %
2.84 %
100.00 %
0.24 s
1 core @ 2.5 Ghz (C/C++)
58
SSTM_T [MV] 2.75 %
3.43 %
2.87 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
F. Ferede and M. Balasubramanian: SSTM: Spatiotemporal recurrent
transformers for multi-frame optical flow
estimation . Neurocomputing 2023.
59
ce_skii 2.73 %
3.59 %
2.89 %
100.00 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
60
SSTM++_ttt [mv] 2.71 %
3.66 %
2.89 %
100.00 %
0.3 s
GPU @ >3.5 Ghz (Python)
F. Ferede and M. Balasubramanian: SSTM: Spatiotemporal recurrent
transformers for multi-frame optical flow
estimation . Neurocomputing 2023.
61
LOF_S 2.68 %
3.84 %
2.89 %
100.00 %
0.07 s
1 core @ 2.5 Ghz (Python)
62
ScaleRAFT code 2.88 %
2.95 %
2.89 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
63
LLA-Flow+GMAv2 2.69 %
3.84 %
2.90 %
100.00 %
2.4 s
1 core @ 2.5 Ghz (C/C++)
64
SF2SE3 code 2.23 %
6.05 %
2.93 %
100.00 %
2.7 s
GPU @ >3.5 Ghz (Python)
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.
65
Scale-flow-ADF58 2.71 %
3.94 %
2.93 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
66
SSTMT++-tt-main [mv] code 2.78 %
3.64 %
2.94 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
F. Ferede and M. Balasubramanian: SSTM: Spatiotemporal Recurrent
Transformers for Multi-frame Optical Flow
Estimation . arXiv preprint arXiv:2304.14418 2023.
67
raft-sd 2.82 %
3.47 %
2.94 %
100.00 %
2.4 s
1 core @ 2.5 Ghz (C/C++)
68
OPM(C) code 2.82 %
3.55 %
2.95 %
100.00 %
** s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
69
SplatFlow code 2.81 %
3.60 %
2.96 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
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.
70
DEQ-Flow-H code 2.82 %
3.59 %
2.96 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (Python)
S. Bai, Z. Geng, Y. Savani and Z. Kolter: Deep Equilibrium Optical Flow
Estimation . CVPR 2022.
71
AGFlow code 2.79 %
3.77 %
2.97 %
100.00 %
0.2 s
8 cores @ 2.5 Ghz (Python)
A. Luo, F. Yang, K. Luo, X. Li, H. Fan and S. Liu: Learning Optical Flow with Adaptive Graph Reasoning . AAAI 2022.
72
CE_SKFlow 2.88 %
3.45 %
2.99 %
100.00 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
73
raft-aug 2.87 %
3.56 %
2.99 %
100.00 %
2.4 s
1 core @ 2.5 Ghz (C/C++)
74
CSFlow code 2.90 %
3.40 %
3.00 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
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.
75
RAFT3DMR 2.14 %
6.90 %
3.00 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
76
CRAFT code 2.87 %
3.68 %
3.02 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
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.
77
RAFT-A code 3.01 %
3.17 %
3.04 %
100.00 %
0.7 s
GPU @ 2.5 Ghz (Python + C/C++)
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.
78
RAFT+AOIR 2.80 %
4.13 %
3.04 %
100.00 %
10 s
GPU @ 2.5 Ghz (Python + C/C++)
L. Mehl, C. Beschle, A. Barth and A. Bruhn: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation . SSVM 2021.
79
RAFT code 2.87 %
3.98 %
3.07 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
Z. Teed and J. Deng: RAFT: Recurrent All-Pairs Field Transforms
for Optical Flow . ECCV 2020.
80
SSTM++_thes_[mv] 2.83 %
4.16 %
3.08 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
F. Ferede: Multi-Frame Optical Flow Estimation Using Spatio-Temporal Transformers . 2022.
81
LLA-Flow 2.93 %
3.79 %
3.08 %
100.00 %
0.24 s
1 core @ 2.5 Ghz (C/C++)
82
PRAFlow_RVC 2.82 %
4.40 %
3.11 %
100.00 %
0.5 s
GPU @ NVIDIA RTX 2080Ti (Python)
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.
83
DPCTF-F 3.01 %
3.58 %
3.11 %
100.00 %
0.07 s
GPU @ 2.5 Ghz (C/C++)
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.
84
SSTM_thes_[mv] 2.93 %
4.02 %
3.13 %
100.00 %
0.3 s
GPU @ 2.5 Ghz (Python)
F. Ferede: Multi-Frame Optical Flow Estimation Using Spatio-Temporal Transformers . 2022.
85
RAFT-re 3.02 %
3.69 %
3.14 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
86
RAFT-3D 2.69 %
5.70 %
3.23 %
100.00 %
2 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Teed and J. Deng: RAFT-3D: Scene Flow using Rigid-Motion
Embeddings . arXiv preprint arXiv:2012.00726 2020.
87
HCVNet 3.16 %
3.90 %
3.29 %
100.00 %
0.24 s
1 core @ 2.5 Ghz (C/C++)
88
RAFT-ADF 3.16 %
3.89 %
3.29 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python + C/C++)
89
PPAC-HD3 code 3.09 %
4.50 %
3.35 %
100.00 %
0.19 s
NVIDIA GTX 1080 Ti
A. Wannenwetsch and S. Roth: Probabilistic Pixel-Adaptive Refinement Networks . CVPR 2020.
90
Scale-flow code 3.26 %
3.79 %
3.36 %
100.00 %
0.8 s
GPU @ 2.5 Ghz (Python)
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.
91
RAFT-TF_RVC 3.45 %
3.81 %
3.52 %
100.00 %
0.7 s
GPU @ 2.5 Ghz (Python)
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.
92
MobileFlow 3.34 %
4.47 %
3.55 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
93
UberATG-DRISF 2.67 %
7.66 %
3.58 %
100.00 %
0.75 s
CPU+GPU @ 2.5 Ghz (Python)
W. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun: Deep Rigid Instance Scene Flow . CVPR 2019.
94
RAPIDFlow code 3.67 %
3.51 %
3.64 %
100.00 %
0.04 s
GPU @ 2.5 Ghz (Python)
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.
95
GMFlow code 3.65 %
4.46 %
3.80 %
100.00 %
0.071 s
A100 GPU (Python)
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.
96
Stereo expansion code 3.50 %
5.62 %
3.89 %
100.00 %
2 s
GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow
through Optical Expansion . CVPR 2020.
97
VCN code 3.50 %
5.62 %
3.89 %
100.00 %
0.18 s
Titan X Pascal
G. Yang and D. Ramanan: Volumetric Correspondence Networks for
Optical Flow . NeurIPS 2019.
98
VCN+LCV code 3.47 %
5.78 %
3.89 %
100.00 %
0.26 s
1 core @ 2.5 Ghz (Python)
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.
99
Binary TTC 3.51 %
5.63 %
3.89 %
100.00 %
2 s
GPU @ 1.0 Ghz (Python)
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.
100
MonoComb 3.51 %
5.63 %
3.89 %
100.00 %
0.58 s
RTX 2080 Ti
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.
101
MaskFlownet code 3.70 %
4.93 %
3.92 %
100.00 %
0.06 s
NVIDIA TITAN Xp
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.
102
PRichFlow 3.92 %
3.94 %
3.93 %
100.00 %
0.1 s
TITAN X MAXWELL
X. Wang, D. Zhu, J. Song, Y. Liu, J. Li and X. Zhang: Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement . .
103
HD^3-Flow code 3.39 %
6.39 %
3.93 %
100.00 %
0.10 s
NVIDIA Pascal Titan XP
Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition
for Match Density Estimation . CVPR 2019.
104
RAFT+LCT-Flow code 3.42 %
6.45 %
3.97 %
100.00 %
0.65 s
GPU @ 1.5 Ghz (Python + C/C++)
J. Chen: Motion Estimation with L0 norm
Regularization (Extended Version) . IEEE 7th International Conference on
Virtual Reality(ICVR) 2021.
105
LiteFlowNet3-S code 4.21 %
3.93 %
4.15 %
100.00 %
0.07s
GTX 1080 (slower than Titan X Pascal)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence
Ambiguity for More Accurate Optical Flow
Estimation . European Conference on Computer Vision
(ECCV) 2020.
106
HOR-RAFT 4.02 %
5.12 %
4.22 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
107
MaskFlownet-S code 4.03 %
5.39 %
4.27 %
100.00 %
0.03 s
NVIDIA TITAN Xp
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.
108
LiteFlowNet3 code 4.23 %
4.59 %
4.29 %
100.00 %
0.07s
GTX 1080 (slower than Titan X Pascal)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence
Ambiguity for More Accurate Optical Flow
Estimation . European Conference on Computer Vision
(ECCV) 2020.
109
SwiftFlow 3.92 %
6.22 %
4.34 %
100.00 %
0.03 s
GPU @ 2.5 Ghz (Python)
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.
110
LiteFlowNet2 code 4.38 %
4.59 %
4.42 %
100.00 %
0.0486 s
GTX 1080 (slower than Titan X Pascal)
T. Hui, X. Tang and C. Loy: A Lightweight Optical Flow CNN -
Revisiting Data
Fidelity and Regularization . TPAMI 2020.
111
RAFT+LCV code 4.04 %
6.16 %
4.43 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
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.
112
ScopeFlow code 4.44 %
4.49 %
4.45 %
100.00 %
-1 s
Nvidia GPU
A. Bar-Haim and L. Wolf: ScopeFlow: Dynamic Scene Scoping for
Optical Flow . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) 2020.
113
MFF 4.52 %
4.25 %
4.47 %
100.00 %
0.05 s
NVIDIA Pascal Titan X (Python)
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.
114
PMC-PWC code 4.58 %
4.12 %
4.50 %
100.00 %
TBD s
GPU @ 2.5 Ghz (Python)
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.
115
ACOSF 3.40 %
9.52 %
4.51 %
100.00 %
5 min
1 core @ 3.0 Ghz (Matlab + C/C++)
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.
116
ISF 4.21 %
6.83 %
4.69 %
100.00 %
10 min
1 core @ 3 Ghz (C/C++)
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.
117
IRR-PWC code 4.92 %
4.62 %
4.86 %
100.00 %
0.18 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Iterative Residual Refinement for
Joint Optical Flow and Occlusion Estimation . CVPR 2019.
118
PWC-Net+ code 4.91 %
4.88 %
4.91 %
100.00 %
0.03 s
NVIDIA Pascal Titan X
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.
119
Separable-Sim2real 4.46 %
7.78 %
5.06 %
100.00 %
0.25 s
GPU
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.
120
STaRFlow code 5.07 %
5.23 %
5.10 %
100.00 %
0.24 s
GPU @ 2.0 Ghz (Python)
P. Godet, A. Boulch, A. Plyer and G. Besnerais: STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation . ICPR 2020.
121
SMURF code 4.46 %
8.86 %
5.26 %
100.00 %
.2 s
1 core @ 2.5 Ghz (C/C++)
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.
122
OSF+TC 4.34 %
9.67 %
5.31 %
100.00 %
50 min
1 core @ 2.5 Ghz (C/C++)
M. Neoral and J. Šochman: Object Scene Flow with Temporal
Consistency . 22nd Computer Vision Winter
Workshop (CVWW) 2017.
123
SSF 4.20 %
10.81 %
5.40 %
100.00 %
5 min
1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Ren, D. Sun, J. Kautz and E. Sudderth: Cascaded Scene Flow Prediction using
Semantic Segmentation . International Conference on 3D Vision
(3DV) 2017.
124
SemARFlow code 4.58 %
9.30 %
5.43 %
100.00 %
0.0168s
GPU @ 2.5 Ghz (Python)
S. Yuan, S. Yu, H. Kim and C. Tomasi: SemARFlow: Injecting Semantics into
Unsupervised Optical Flow Estimation for Autonomous
Driving . ICCV 2023.
125
LiteFlowNet code 5.58 %
5.09 %
5.49 %
100.00 %
0.0885 s
GTX 1080 (slower than Titan X Pascal)
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.
126
PRSM code 4.33 %
10.80 %
5.50 %
100.00 %
300 s
1 core @ 2.5 Ghz (C/C++)
C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a
Piecewise Rigid Scene Model . ijcv 2015.
127
FDFlowNet 5.35 %
6.62 %
5.58 %
100.00 %
0.02 s
NVIDIA GTX 1080 Ti
L. Kong and J. Yang: FDFlowNet: Fast Optical Flow Estimation
using a Deep Lightweight Network . IEEE International Conference on
Image Processing (ICIP) 2020.
128
UnSAMFlow 4.30 %
11.83 %
5.67 %
100.00 %
0.03 s
GPU @ 2.5 Ghz (Python)
129
AL-OF_r0.2 code 4.67 %
10.29 %
5.69 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
S. Yuan, X. Sun, H. Kim, S. Yu and C. Tomasi: Optical Flow Training Under Limited Label Budget via Active
Learning . ECCV 2022.
130
LSM_FLOW_RVC code 4.67 %
10.40 %
5.70 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
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.
131
OSF 2018 code 4.02 %
14.14 %
5.86 %
100.00 %
390 s
1 core @ 2.5 Ghz (Matlab + C/C++)
M. Menze, C. Heipke and A. Geiger: Object Scene Flow . ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 2018.
132
IRR-PWC_RVC code 5.19 %
8.92 %
5.87 %
100.00 %
0.18 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Iterative Residual Refinement for
Joint Optical Flow and Occlusion Estimation . CVPR 2019.
133
SelFlow 5.12 %
9.41 %
5.90 %
100.00 %
0.09 s
GPU @ 2.5 Ghz (Python)
P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical
Flow . CVPR 2019.
134
UFD-PRiME 4.61 %
11.89 %
5.93 %
100.00 %
0.56 s
GPU @ 2.5 Ghz (Python)
135
SENSE code 5.90 %
6.37 %
5.98 %
100.00 %
0.32s
GPU, GTX 2080Ti
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.
136
DTF_SENSE 5.90 %
6.41 %
5.99 %
100.00 %
0.76 s
1 core @ 2.5 Ghz (C/C++)
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.
137
PWC-Net code 6.14 %
5.98 %
6.12 %
100.00 %
0.03 s
NVIDIA Pascal Titan X
D. Sun, X. Yang, M. Liu and J. Kautz: PWC-Net: CNNs for Optical
Flow Using Pyramid, Warping, and Cost Volume . CVPR 2018.
138
OSF code 4.21 %
15.49 %
6.26 %
100.00 %
50 min
1 core @ 2.5 Ghz (C/C++)
M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles . Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
139
NeuFlow code 5.73 %
8.63 %
6.26 %
100.00 %
0.01 s
GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
140
CoT-AMFlow 5.83 %
8.30 %
6.28 %
100.00 %
0.06 s
GPU @ 2.5 Ghz (Python)
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.
141
MDFlow 5.72 %
10.34 %
6.56 %
100.00 %
0.03 s
NVIDIA GTX 1080 Ti
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.
142
FastFlowNet code 6.29 %
7.78 %
6.56 %
100.00 %
0.01 s
NVIDIA GTX 1080 Ti
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.
143
MaxFlow code 6.94 %
5.64 %
6.70 %
100.00 %
1 s
GPU @ >3.5 Ghz (Python)
144
FlowNet2 code 7.24 %
5.60 %
6.94 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (C/C++)
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.
145
VCN_RVC code 5.33 %
14.97 %
7.08 %
100.00 %
0.36 s
GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Volumetric Correspondence Networks for
Optical Flow . NeurIPS 2019.
146
hhx 5.81 %
12.87 %
7.09 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
147
DWARF 6.67 %
10.11 %
7.29 %
100.00 %
0.14s - 1.43s
TitanXP - JetsonTX2
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.
148
CNNF+PMBP 5.64 %
14.96 %
7.33 %
100.00 %
45 min
1 cores @ 3.5 Ghz (C/C++)
F. Zhang and B. Wah: Fundamental Principles on Learning New
Features for Effective Dense Matching . IEEE Transactions on Image
Processing 2018.
149
MDFlow-Fast 6.42 %
11.90 %
7.42 %
100.00 %
0.01 s
NVIDIA GTX 1080 Ti
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.
150
MirrorFlow code 6.24 %
12.95 %
7.46 %
100.00 %
11 min
4 core @ 2.2 Ghz (C/C++)
J. Hur and S. Roth: MirrorFlow: Exploiting Symmetries
in Joint Optical Flow and Occlusion Estimation . ICCV 2017.
151
UnFlow code 6.38 %
12.36 %
7.46 %
100.00 %
0.12 s
GPU @ 1.5 Ghz (Python + C/C++)
S. Meister, J. Hur and S. Roth: UnFlow: Unsupervised Learning of Optical
Flow with a Bidirectional Census Loss . AAAI 2018.
152
ContinualFlow_ROB 5.90 %
14.99 %
7.55 %
100.00 %
0.15 s
GPU - NVidia 1080Ti
M. Neoral, J. Šochman and J. Matas: Continual Occlusions and Optical Flow
Estimation . 14th Asian Conference on Computer
Vision (ACCV) 2018.
153
PWC-Net_RVC code 7.12 %
10.29 %
7.69 %
100.00 %
0.03 s
NVIDIA Pascal Titan X
D. Sun, X. Yang, M. Liu and J. Kautz: PWC-Net: CNNs for Optical Flow
Using Pyramid, Warping, and Cost Volume . CVPR 2018.
154
NccFLow 6.43 %
14.67 %
7.93 %
100.00 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
G. Wang, S. Ren and H. Wang: NccFlow: Unsupervised Learning of Optical
Flow With Non-occlusion from Geometry . arXiv preprint arXiv:2107.03610 2021.
155
Self-scale-flow-nerf 6.94 %
12.65 %
7.98 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
156
SDF 5.75 %
18.38 %
8.04 %
100.00 %
TBA
1 core @ 2.5 Ghz (C/C++)
M. Bai*, W. Luo*, K. Kundu and R. Urtasun: Exploiting Semantic Information and Deep
Matching for Optical Flow . ECCV 2016.
157
Flow2Stereo 6.84 %
13.46 %
8.04 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
P. Liu, I. King, M. Lyu and J. Xu: Flow2Stereo: Effective Self-Supervised
Learning of Optical Flow and Stereo Matching . CVPR 2020.
158
Nerf-self 7.25 %
11.98 %
8.10 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
159
UnsupSimFlow code 7.06 %
13.36 %
8.21 %
100.00 %
0.03 s
8 cores @ 3.0 Ghz (Python + C/C++)
W. Im, T. Kim and S. Yoon: Unsupervised Learning of Optical Flow
with Deep Feature Similarity . The European Conference on Computer
Vision (ECCV) 2020.
160
Self-SuperFlow-ft 6.82 %
15.27 %
8.36 %
100.00 %
0.13 s
GTX 1080 Ti
K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in
Stereo Sequences . International Conference on Image Processing (ICIP) 2022.
161
UFlow code 7.01 %
14.75 %
8.41 %
100.00 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
R. Jonschkowski, A. Stone, J. Barron, A. Gordon, K. Konolige and A. Angelova: What Matters in Unsupervised Optical
Flow . ECCV 2020.
162
Mono-SF 6.67 %
16.48 %
8.45 %
100.00 %
41 s
1 core @ 3.5 Ghz (Matlab + C/C++)
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.
163
PWOC-3D code 7.70 %
11.96 %
8.47 %
100.00 %
0.13 s
GTX 1080 Ti
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.
164
FlowFields++ code 7.31 %
13.83 %
8.49 %
100.00 %
29 s
1 core @ 3.5 Ghz (C/C++)
R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation . International Conference on Image Processing (ICIP) 2018.
165
CompactFlowNet 7.08 %
15.09 %
8.54 %
100.00 %
0.01 s
1 core @ >3.5 Ghz (Python)
166
GMFlow+ADF58 7.14 %
15.00 %
8.56 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
167
Multi-Mono-SF-ft code 7.54 %
14.05 %
8.72 %
100.00 %
0.06 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Multi-Frame
Monocular Scene Flow . CVPR 2021.
168
MR-Flow code 6.86 %
17.91 %
8.86 %
100.00 %
8 min
1 core @ 2.5 Ghz (Python + C/C++)
J. Wulff, L. Sevilla-Lara and M. Black: Optical Flow in Mostly Rigid Scenes . IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2017.
169
DTF_PWOC 7.37 %
16.42 %
9.01 %
100.00 %
0.38 s
RTX 2080 Ti
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.
170
Anonymous 7.93 %
14.02 %
9.03 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
171
SFF++ 7.97 %
14.10 %
9.08 %
100.00 %
78 s
4 cores @ 3.5 Ghz (C/C++)
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.
172
FSF+MS 6.53 %
20.72 %
9.11 %
100.00 %
2.7 s
4 cores @ 3.5 Ghz (C/C++)
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.
173
JFS 7.85 %
14.97 %
9.14 %
100.00 %
13 min
1 core @ 3.2 Ghz (C/C++)
J. Hur and S. Roth: Joint Optical Flow and Temporally
Consistent Semantic Segmentation . ECCV Workshops 2016.
174
FF++_ROB 7.82 %
15.33 %
9.18 %
100.00 %
29 s
1 core @ 3.5 Ghz (C/C++)
R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation . International Conference on Image Processing (ICIP) 2018.
175
ImpPB+SPCI code 7.70 %
16.25 %
9.25 %
100.00 %
60 s
GPU @ 2.5 Ghz (Python)
T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies
(but only one network) . CVPR 2017.
176
SfM-PM 6.94 %
19.94 %
9.30 %
100.00 %
69 s
3 cores @ 3.6 Ghz (C/C++)
D. Maurer, N. Marniok, B. Goldluecke and A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation . ECCV 2018.
177
PR-Sceneflow code 6.94 %
20.24 %
9.36 %
100.00 %
150 s
4 core @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow . ICCV 2013.
178
DDFlow+LCV 7.83 %
16.31 %
9.37 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
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.
179
DDFlow 7.95 %
16.77 %
9.55 %
100.00 %
0.06 s
GPU @ >3.5 Ghz (Python + C/C++)
P. Liu, I. King, M. Lyu and J. Xu: DDFlow: Learning Optical Flow with Unlabeled
Data Distillation . AAAI 2019.
180
SelFlow 7.73 %
18.34 %
9.65 %
100.00 %
0.09 s
GPU @ 2.5 Ghz (Python)
P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical
Flow . CVPR 2019.
181
SOF code 8.11 %
18.16 %
9.93 %
100.00 %
6 min
1 core @ 2.5 Ghz (Matlab)
L. Sevilla-Lara, D. Sun, V. Jampani and M. Black: Optical Flow with Semantic Segmentation and
Localized Layers . CVPR 2016.
182
SwiftStream 10.02 %
10.22 %
10.06 %
100.00 %
0.01 s
GPU @ 2.5 Ghz (Python)
183
ProFlow 8.44 %
17.90 %
10.15 %
100.00 %
112 s
GPU+CPU @ 3.6 Ghz (Python + C/C++)
D. Maurer and A. Bruhn: ProFlow: Learning to Predict Optical Flow . BMVC 2018.
184
DCFlow code 8.04 %
19.84 %
10.18 %
100.00 %
8.6 s
GPU @ 3.0 Ghz (Matlab + C/C++)
J. Xu, R. Ranftl and V. Koltun: Accurate Optical Flow via Direct
Cost Volume Processing . CVPR 2017.
185
FlowFieldCNN 8.91 %
16.06 %
10.21 %
100.00 %
23 s
GPU/CPU 4 core @ 3.5 Ghz (C/C++)
C. Bailer, K. Varanasi and D. Stricker: CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss . CVPR 2017.
186
RicFlow 9.27 %
14.88 %
10.29 %
100.00 %
5 s
1 core @ 3.5 Ghz (C/C++)
Y. Hu, Y. Li and R. Song: Robust Interpolation of Correspondences for
Large Displacement Optical Flow . CVPR 2017.
187
SPS+FF++ code 9.09 %
15.91 %
10.33 %
100.00 %
36 s
1 core @ 3.5 Ghz (C/C++)
R. Schuster, O. Wasenmüller and D. Stricker: Dense Scene Flow from Stereo Disparity and Optical Flow . ACM Computer Science in Cars Symposium (CSCS) 2018.
188
SceneFFields 8.29 %
19.85 %
10.39 %
100.00 %
65 s
4 cores @ 3.7 Ghz (C/C++)
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.
189
ProFlow_ROB 8.56 %
18.71 %
10.40 %
100.00 %
112 s
GPU+CPU @ 3.6 Ghz (Python + C/C++)
D. Maurer and A. Bruhn: ProFlow: Learning to Predict Optical Flow . BMVC 2018.
190
DIP-Flow-DF 8.61 %
19.25 %
10.54 %
100.00 %
104s
2 cores @ 3.6 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow . BMVC 2018.
191
DF+OIR 8.73 %
19.18 %
10.63 %
100.00 %
3 min
1 core @ 3.5 Ghz (Matlab + C/C++)
D. Maurer, M. Stoll and A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement . BMVC 2017.
192
Self-Mono-SF-ft code 10.13 %
14.04 %
10.84 %
100.00 %
0.09 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene
Flow Estimation . CVPR 2020.
193
FlowFields+ 9.69 %
16.82 %
10.98 %
100.00 %
28s
1 core @ 3.5 Ghz (C/C++)
C. Bailer, B. Taetz and D. Stricker: Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation . .
194
PGM-G 9.24 %
19.06 %
11.02 %
100.00 %
5.05 s
1 core @ 3.1 Ghz (C/C++)
Y. Li: Pyramidal Gradient Matching for
Optical Flow Estimation . CoRR 2017.
195
CSF 8.72 %
22.38 %
11.20 %
100.00 %
80 s
1 core @ 2.5 Ghz (C/C++)
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.
196
DiscreteFlow code 9.96 %
17.03 %
11.25 %
100.00 %
3 min
1 core @ 2.5 Ghz (Matlab + C/C++)
M. Menze, C. Heipke and A. Geiger: Discrete Optimization for Optical Flow . German Conference on Pattern
Recognition (GCPR) 2015.
197
DIP-Flow-CPM 9.35 %
19.89 %
11.26 %
100.00 %
52 s
2 core @ 3.6 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow . BMVC 2018.
198
RAFT-MSF 10.73 %
15.85 %
11.66 %
100.00 %
0.18 s
GPU @ 2.5 Ghz (Python)
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199
HCSH 9.39 %
22.05 %
11.69 %
100.00 %
3.5 s
1 core @ 3.0 Ghz (C/C++)
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.
200
SGM&FlowFie+ 10.38 %
17.97 %
11.75 %
91.79 %
29 s
1 core @ 3.5 Ghz (C/C++)
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.
201
PatchBatch code 10.06 %
22.29 %
12.28 %
100.00 %
50 s
GPU @ 2.5 Ghz (Python)
D. Gadot and L. Wolf: PatchBatch: a Batch Augmented Loss for
Optical Flow . The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR) 2016.
202
DDF code 10.44 %
21.32 %
12.41 %
100.00 %
~1 min
GPU @ 2.5 Ghz (C/C++)
F. G\"uney and A. Geiger: Deep Discrete Flow . Asian Conference on Computer Vision
(ACCV) 2016.
203
UJG code 11.21 %
20.08 %
12.82 %
100.00 %
0.03 s
GPU @ 2.5 Ghz (Python)
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.
204
Self-SuperFlow 11.18 %
24.17 %
13.54 %
100.00 %
0.13 s
GTX 1080 Ti
K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in
Stereo Sequences . International Conference on Image Processing (ICIP) 2022.
205
IntrpNt-df code 11.67 %
22.09 %
13.56 %
100.00 %
3 min
GPU @ 2.5 Ghz (Python)
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.
206
SegFlow(d0=3) 12.41 %
19.39 %
13.68 %
100.00 %
6.6 s
1 core @ >3.5 Ghz (C/C++)
J. Chen, Z. Cai, J. Lai and X. Xie: Efficient Segmentation-based PatchMatch for
Large displacement Optical
Flow Estimation . IEEE TCSVT 2018.
207
Multi-Mono-SF code 11.72 %
22.83 %
13.74 %
100.00 %
0.06 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Multi-Frame
Monocular Scene Flow . CVPR 2021.
208
PCOF-LDOF 9.24 %
34.40 %
13.80 %
100.00 %
50 s
1 core @ 3.0 Ghz (C/C++)
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.
209
CPM-Flow code 12.77 %
18.71 %
13.85 %
100.00 %
4.2 s
1 core @ 3.5 Ghz (C/C++)
Y. Hu, R. Song and Y. Li: Efficient Coarse-to-Fine PatchMatch for
Large
Displacement Optical Flow . CVPR 2016.
210
Back2FutureFlow(UFO) code 12.49 %
20.00 %
13.85 %
100.00 %
0.12 s
GPU @ 2.5 Ghz (LUA/Torch)
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.
211
OmegaNet 11.14 %
26.10 %
13.86 %
100.00 %
0.01 s
GPU @ 1.5 Ghz (Python)
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.
212
IntrpNt-cpm code 12.10 %
22.73 %
14.03 %
100.00 %
5.6 s
GPU @ 2.5 Ghz (Python)
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.
213
HiLM code 13.32 %
17.45 %
14.07 %
100.00 %
8 sec
P6000 (C/C++)
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.
214
SPM-BP 12.86 %
20.33 %
14.22 %
100.00 %
10 s
2 cores @ 2.5 Ghz (C/C++)
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.
215
FullFlow 12.97 %
20.58 %
14.35 %
100.00 %
4 min
4 cores @ >3.5 Ghz (Matlab and C++)
Q. Chen and V. Koltun: Full Flow: Optical Flow Estimation By
Global Optimization over Regular Grids . CVPR 2016.
216
IntrpNt-dm code 12.88 %
22.41 %
14.61 %
100.00 %
15 s
GPU @ 2.5 Ghz (Python)
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.
217
SGM+SF 13.36 %
21.78 %
14.89 %
100.00 %
45 min
16 core @ 3.2 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching
and Mutual Information . PAMI 2008. SphereFlow: 6
DoF Scene Flow from RGB-D Pairs . CVPR 2014.
218
EPC++ (stereo) 13.24 %
22.70 %
14.96 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
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.
219
PPM code 15.09 %
18.91 %
15.78 %
100.00 %
17.3 s
1 core @ 2.5 Ghz (C/Chttps://github.c++)
F. Kuang: PatchMatch algorithms for motion
estimation and stereo reconstruction . 2017.
220
SODA-Flow 13.93 %
25.45 %
16.02 %
100.00 %
96 s
2 cores @ 3.5 Ghz (C/C++)
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.
221
OAR-Flow 14.33 %
24.03 %
16.09 %
100.00 %
100 s
2 cores @ 3.5 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between . SSVM 2017.
222
MotionSLIC code 6.19 %
63.03 %
16.50 %
100.00 %
30 s
4 cores @ 2.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation . CVPR 2013.
223
EpicFlow code 15.00 %
24.34 %
16.69 %
100.00 %
15 s
1 core @ >3.5 Ghz (C/C++)
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.
224
Self-Mono-SF code 15.98 %
20.85 %
16.86 %
100.00 %
0.09 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene
Flow Estimation . CVPR 2020.
225
3DFlow 15.13 %
25.02 %
16.92 %
100.00 %
448s
Matlab with embedded C++ code
J. Chen, Z. Cai, J. Lai and X. Xie: A Filtering Based Framework for Optical
Flow Estimation . IEEE TCSVT 2018.
226
DeepFlow code 16.47 %
26.80 %
18.35 %
100.00 %
17 s
1 core @ >3.5 Ghz (Python + C/C++)
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.
227
PCOF + ACTF 9.77 %
57.63 %
18.45 %
100.00 %
0.08 s
GPU @ 2.0 Ghz (C/C++)
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.
228
SegFlow(d0=11) 19.02 %
18.05 %
18.84 %
100.00 %
4.5 s
1 core @ 3.5 Ghz (C/C++)
J. Chen, Z. Cai, J. Lai and X. Xie: Efficient Segmentation-based PatchMatch for
Large displacement Optical
Flow Estimation . IEEE TCSVT 2018.
229
DMF_ROB code 19.32 %
25.60 %
20.46 %
100.00 %
150 s
1 core @ 2.5 Ghz (C/C++)
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.
230
IIOF-NLDP 19.40 %
28.20 %
20.99 %
100.00 %
350 s
4 cores @ 3.5 Ghz (Matlab + C/C++)
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.
231
CPNFlow 23.41 %
23.39 %
23.40 %
100.00 %
0.1 s
GPU @ 1.5 Ghz (Python)
Y. Yang and S. Soatto: Conditional prior networks for optical flow . Proceedings of the European Conference on Computer
Vision (ECCV) 2018.
232
SGM+C+NL code 23.03 %
38.80 %
25.89 %
99.90 %
4.5 min
1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information . PAMI 2008. A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them . IJCV 2013.
233
SPyNet code 23.64 %
40.58 %
26.71 %
100.00 %
0.16 s
1 core @ 2.5 Ghz (C/C++)
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.
234
3DG-DVO 24.51 %
47.53 %
28.68 %
100.00 %
0.04 s
GPU @ 1.5 Ghz (Python)
235
DWBSF 30.13 %
26.68 %
29.50 %
100.00 %
7 min
4 cores @ 3.5 Ghz (C/C++)
C. Richardt, H. Kim, L. Valgaerts and C. Theobalt: Dense Wide-Baseline Scene Flow
From Two Handheld Video Cameras . 3DV 2016.
236
SGM+LDOF code 30.41 %
27.62 %
29.90 %
99.94 %
86 s
1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information . PAMI 2008. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation . PAMI 2011.
237
HS code 30.49 %
48.25 %
33.71 %
100.00 %
2.6 min
1 core @ 3.0 Ghz (Matlab)
D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current
Practices in Optical Flow Estimation
and The Principles Behind Them . 2014.
238
GCSF code 38.12 %
37.77 %
38.05 %
100.00 %
2.4 s
1 core @ 2.5 Ghz (C/C++)
J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds . CVPR 2011.
239
DB-TV-L1 code 38.67 %
44.94 %
39.81 %
100.00 %
16 s
1 core @ 2.5 Ghz (Matlab)
C. Zach, T. Pock and H. Bischof: A Duality Based Approach for Realtime TV-
L1 Optical Flow . DAGM 2007.
240
VSF code 41.15 %
41.85 %
41.28 %
100.00 %
125 min
1 core @ 2.5 Ghz (C/C++)
F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences . ICCV 2007.
241
HAOF code 41.52 %
47.66 %
42.63 %
100.00 %
16.2 s
1 core @ 2.5 Ghz (C/C++)
T. Brox, A. Bruhn, N. Papenberg and J. Weickert: High accuracy optical flow estimation
based on a theory for warping . ECCV 2004.
242
TVL1_ROB code 42.85 %
47.99 %
43.79 %
100.00 %
3 s
4 cores @ 2.5 Ghz (C/C++)
J. Sánchez Pérez, E. Meinhardt-Llopis and G. Facciolo: TV-L1 Optical Flow Estimation . Image Processing On Line 2013.
243
PolyExpand 43.77 %
55.90 %
45.97 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion . SCIA 2003.
244
H+S_ROB code 62.55 %
74.96 %
64.80 %
100.00 %
8 s
4 cores @ 2.5 Ghz (C/C++)
E. Meinhardt-Llopis, J. Sánchez Pérez and D. Kondermann: Horn-Schunck Optical Flow with a Multi-Scale Strategy . Image Processing On Line 2013.
245
Stereo-RSSF code 65.47 %
71.92 %
66.64 %
10.75 %
2.5 s
8 core @ 2.5 Ghz (Matlab)
E. Salehi, A. Aghagolzadeh and R. Hosseini: Stereo-RSSF: stereo robust sparse scene-flow estimation . The Visual Computer 2023.
246
Pyramid-LK code 66.72 %
75.32 %
68.28 %
100.00 %
1.5 min
1 core @ 2.5 Ghz (Matlab)
J. Bouguet: Pyramidal implementation of the Lucas
Kanade feature tracker . Intel 2000.
247
MEDIAN 85.07 %
92.33 %
86.39 %
99.92 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
248
AVERAGE 86.38 %
91.57 %
87.32 %
99.92 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
Stereo: Method uses left and right (stereo) images
Multiview: Method uses more than 2 temporally adjacent images
Motion stereo: Method uses epipolar geometry for computing optical flow
Additional training data: Use of additional data sources for training (see details)