Method
Setting
Code
Out-Noc
Out-All
Avg-Noc
Avg-All
Density
Runtime
Environment
1
PPAC-HD3
code
2.01 %
5.09 %
0.6 px
1.2 px
100.00 %
0.19 s
NVIDIA GTX 1080 Ti
A. Wannenwetsch and S. Roth: Probabilistic Pixel-Adaptive Refinement Networks . CVPR 2020.
2
HD3F+MSDRNet
2.05 %
5.15 %
0.6 px
1.2 px
100.00 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
3
MaskFlownet
code
2.07 %
4.82 %
0.6 px
1.1 px
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.
4
UnDAF-MaskFlownet
2.11 %
5.25 %
0.8 px
1.4 px
100.00 %
0.06 s
GPU @ 2.5 Ghz (Python)
5
VCN+MSDRNet
2.20 %
5.39 %
0.6 px
1.2 px
100.00 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
6
IOFPL-CVr8-ft
2.25 %
5.17 %
0.7 px
1.2 px
100.00 %
-1
GPU @ 2.5 Ghz (Python)
7
HD^3-Flow
code
2.26 %
5.41 %
0.7 px
1.4 px
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.
8
GPNet
2.28 %
5.37 %
0.6 px
1.2 px
100.00 %
0.03 s
NVIDIA GTX 1080 Ti
9
f407HTJ
2.28 %
5.37 %
0.6 px
1.2 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python) GPU
10
IOFPL-ft
2.29 %
5.21 %
0.7 px
1.2 px
100.00 %
-1 s
GPU @ 2.5 Ghz (Python + C/C++)
11
MaskFlownet-S
code
2.29 %
5.24 %
0.6 px
1.1 px
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.
12
DistillFlow+ft
2.33 %
5.23 %
0.6 px
1.2 px
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
13
GCA-Net-ft+
2.35 %
5.81 %
0.6 px
1.3 px
100.00 %
0.03 s
NVIDIA GTX 1080 Ti
14
SGM
2.35 %
5.59 %
0.6 px
1.2 px
100.00 %
0.08 s
1 core @ 2.5 Ghz (Python)
15
F407HTJ
2.38 %
5.54 %
0.9 px
1.4 px
100.00 %
-1 s
1 core @ 2.5 Ghz (Python)
16
htjvcn
2.40 %
5.50 %
0.9 px
1.4 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
17
F407HTJ-1
2.44 %
5.72 %
0.8 px
1.3 px
100.00 %
-1 s
1 core @ 2.5 Ghz (Python)
18
PRSM
code
2.46 %
4.23 %
0.7 px
1.0 px
100.00 %
300 s
1 core @ 2.5 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a
Piecewise Rigid Scene Model . ijcv 2015.
19
htjvn4d
2.48 %
5.64 %
0.6 px
1.2 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
20
LiteFlowNet3-S
code
2.49 %
5.91 %
0.7 px
1.3 px
100.00 %
0.07s
GTX 1080 (slower than Pascal Titan X)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence
Ambiguity for More Accurate Optical Flow Estimation . European Conference on Computer Vision
(ECCV) 2020.
21
LiteFlowNet3
code
2.51 %
5.90 %
0.7 px
1.3 px
100.00 %
0.07s
GTX 1080 (slower than Pascal Titan X)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence
Ambiguity for More Accurate Optical Flow Estimation . European Conference on Computer Vision
(ECCV) 2020.
22
PRichFlow
2.60 %
5.63 %
0.7 px
1.3 px
100.00 %
0.1 s
GPU 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 . .
23
LiteFlowNet2
code
2.63 %
6.16 %
0.7 px
1.4 px
100.00 %
0.0486 s
GTX 1080 (slower than Pascal Titan X)
T. Hui, X. Tang and C. Loy: A Lightweight Optical Flow CNN -
Revisiting Data
Fidelity and Regularization . TPAMI 2020.
24
SwiftFlow
2.64 %
6.17 %
1.4 px
2.0 px
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 J., M. Liu, I. Pitas and R. Fan: ATG-PVD: Ticketing Parking Violations on
A Drone . Proceedings of the European
Conference on Computer Vision (ECCV) Workshops 2020.
25
ScopeFlow
code
2.68 %
5.66 %
0.7 px
1.3 px
100.00 %
-1 s
1 core @ 2.5 Ghz (Python)
A. Bar-Haim and L. Wolf: ScopeFlow: Dynamic Scene Scoping for
Optical Flow . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) 2020.
26
newmask
2.70 %
6.01 %
1.0 px
1.5 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
27
VC-SF
2.72 %
4.84 %
0.8 px
1.3 px
100.00 %
300 s
1 core @ 2.5 Ghz (Matlab + C/C++)
C. Vogel, S. Roth and K. Schindler: View-Consistent 3D Scene Flow
Estimation over Multiple Frames . Proceedings of European
Conference on Computer Vision. Lecture
Notes in, Computer Science 2014.
28
SPS-StFl
2.82 %
5.61 %
0.8 px
1.3 px
100.00 %
35 s
1 core @ 3.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo
and Flow Estimation . ECCV 2014.
29
OAS-Net
2.88 %
6.41 %
0.7 px
1.4 px
100.00 %
0.03 s
NVIDIA GTX 1080 Ti
L. Kong, X. Yang and J. Yang: OAS-Net: Occlusion Aware Sampling Network
for Accurate Optical Flow . IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP) 2021.
30
newfull
2.88 %
6.28 %
1.2 px
1.7 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
31
htjmask
2.88 %
6.28 %
1.2 px
1.7 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
32
htjfull
2.88 %
6.28 %
1.2 px
1.7 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
33
SMURF
3.13 %
6.19 %
0.8 px
1.4 px
100.00 %
.2 s
1 core @ 2.5 Ghz (C/C++)
34
FDFlowNet
3.19 %
7.17 %
0.8 px
1.5 px
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.
35
IRR-PWC
code
3.21 %
6.70 %
0.9 px
1.6 px
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.
36
LiteFlowNet
code
3.27 %
7.27 %
0.8 px
1.6 px
100.00 %
0.0885 s
GTX 1080 (slower than Pascal Titan X)
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.
37
SelFlow
3.32 %
6.19 %
0.9 px
1.5 px
100.00 %
0.09 s
NVIDIA GPU
P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical
Flow . CVPR 2019.
38
PWC-Net+
code
3.36 %
6.72 %
0.8 px
1.4 px
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.
39
SMURF-NM
3.37 %
6.56 %
0.8 px
1.6 px
100.00 %
.2 s
1 core @ 2.5 Ghz (C/C++)
40
SPS-Fl
3.38 %
10.06 %
0.9 px
2.9 px
100.00 %
11 s
1 core @ 3.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation . ECCV 2014.
41
OSF
code
3.47 %
6.34 %
1.0 px
1.5 px
100.00 %
50 min
1 core @ 3.0 Ghz (Matlab + C/C++)
M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles . Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
42
CoT-AMFlow
3.50 %
8.26 %
0.9 px
1.7 px
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.
43
PR-Sf+E
3.57 %
7.07 %
0.9 px
1.6 px
100.00 %
200 s
4 cores @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow . International Conference on Computer
Vision (ICCV) 2013.
44
PCBP-Flow
3.64 %
8.28 %
0.9 px
2.2 px
100.00 %
3 min
4 cores @ 2.5 Ghz (Matlab + C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation . CVPR 2013.
45
CVPR-1235
3.69 %
7.01 %
0.9 px
1.5 px
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
46
UPFlow
3.72 %
6.81 %
0.9 px
1.4 px
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
47
PR-Sceneflow
3.76 %
7.39 %
1.2 px
2.8 px
100.00 %
150 sec
4 core @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow . International Conference on Computer
Vision (ICCV) 2013.
48
FastFlowNet
3.78 %
8.34 %
0.9 px
1.8 px
100.00 %
0.01 s
NVIDIA GTX 1080 Ti
49
SDF
3.80 %
7.69 %
1.0 px
2.3 px
100.00 %
TBA s
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.
50
FCU-Net
3.84 %
7.82 %
0.9 px
1.5 px
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
51
MotionSLIC
3.91 %
10.56 %
0.9 px
2.7 px
100.00 %
11 s
1 core @ 3.0 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation . CVPR 2013.
52
DistillFlow
3.91 %
7.18 %
0.9 px
1.6 px
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
53
Flow2Stereo
4.02 %
7.63 %
0.9 px
1.7 px
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.
54
SfM-PM
4.02 %
6.15 %
1.0 px
1.5 px
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.
55
PWC-Net
code
4.22 %
8.10 %
0.9 px
1.7 px
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.
56
UFlow
code
4.26 %
7.91 %
0.9 px
1.9 px
100.00 %
0.02 s
GPU @ 3.0 Ghz (Python)
R. Jonschkowski, A. Stone, J. Barron, A. Gordon, K. Konolige and A. Angelova: What Matters in Unsupervised Optical
Flow . ECCV 2020.
57
UnFlow
code
4.28 %
8.42 %
0.9 px
1.7 px
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.
58
SelFlow
4.31 %
7.68 %
1.0 px
2.2 px
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.
59
MirrorFlow
code
4.38 %
8.20 %
1.2 px
2.6 px
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.
60
stflow_r
4.40 %
8.77 %
0.9 px
1.9 px
100.00 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
61
OIFlow
code
4.43 %
7.52 %
1.0 px
1.6 px
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
62
ProFlow
4.49 %
7.88 %
1.1 px
2.1 px
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.
63
DDFlow
4.57 %
8.86 %
1.1 px
3.0 px
100.00 %
0.06 s
GPU @ >3.5 Ghz (Python)
P. Liu, I. King and M. Xu: DDFlow: Learning Optical Flow with Unlabeled
Data Distillation . AAAI 2019.
64
ImpPB+SPCI
code
4.65 %
13.47 %
1.1 px
2.9 px
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.
65
CostUnrolling
4.69 %
8.31 %
1.1 px
1.9 px
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
66
DIP-Flow-CPM
4.69 %
9.63 %
1.0 px
2.4 px
100.00 %
52 s
2 cores @ 3.6 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow . BMVC 2018.
67
CNNF+PMBP
4.70 %
14.87 %
1.1 px
3.3 px
100.00 %
30 min
1 core @ 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.
68
FlowNet2
code
4.82 %
8.80 %
1.0 px
1.8 px
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.
69
FlowFieldCNN
4.89 %
13.01 %
1.2 px
3.0 px
100.00 %
23 s
GPU @ 2.5 Ghz (C/C++)
C. Bailer, K. Varanasi and D. Stricker: CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss . CVPR 2017.
70
IntrpNt-df
code
4.94 %
14.13 %
1.0 px
2.4 px
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.
71
RicFlow
4.96 %
13.04 %
1.3 px
3.2 px
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.
72
DIP-Flow-DF
4.97 %
10.02 %
1.1 px
2.6 px
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.
73
FlowFields+
5.06 %
13.14 %
1.2 px
3.0 px
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 . .
74
DF+OIR
5.17 %
10.43 %
1.1 px
2.9 px
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.
75
IntrpNt-cpm
code
5.28 %
14.57 %
1.0 px
2.5 px
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.
76
PatchBatch
code
5.29 %
14.17 %
1.3 px
3.3 px
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.
77
PCOF-SGBM
5.40 %
8.73 %
1.2 px
2.1 px
100.00 %
0.8 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.
78
SODA-Flow
5.57 %
10.71 %
1.3 px
2.8 px
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.
79
DCVNet
5.57 %
10.09 %
1.1 px
1.7 px
100.00 %
0.03 s
GPU @ 2.5 Ghz (C/C++)
80
IntrpNt-ff
code
5.57 %
14.76 %
1.1 px
2.6 px
100.00 %
25 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.
81
PCOF
5.59 %
9.69 %
1.2 px
1.9 px
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.
82
OAR-Flow
5.69 %
10.72 %
1.4 px
2.8 px
100.00 %
90 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.
83
DDF
code
5.73 %
14.18 %
1.4 px
3.4 px
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.
84
PH-Flow
5.76 %
10.57 %
1.3 px
2.9 px
100.00 %
800 s
1 core @ 3.5 Ghz (Matlab + C/C++)
J. Yang and H. Li: Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model . CVPR 2015.
85
FlowFields
code
5.77 %
14.01 %
1.4 px
3.5 px
100.00 %
23 s
4 cores @ 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 . International Conference on Computer Vision (ICCV) 2015.
86
CPM-Flow
code
5.79 %
13.70 %
1.3 px
3.2 px
100.00 %
4.2s
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.
87
IntrpNt-dm
code
5.85 %
15.03 %
1.1 px
2.7 px
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.
88
NLTGV-SC
5.93 %
11.96 %
1.6 px
3.8 px
100.00 %
16 s
GPU @ 2.5 Ghz (Matlab + C/C++)
R. Ranftl, K. Bredies and T. Pock: Non-Local Total Generalized Variation
for Optical Flow Estimation . Proceedings of the 13th European
Conference on Computer Vision 2014.
89
DDS-DF
6.03 %
13.08 %
1.6 px
4.2 px
100.00 %
1 min
1 core @ 2.5 Ghz (Matlab + C/C++)
D. Wei, C. Liu and W. Freeman: A Data-driven Regularization Model for Stereo and Flow . 3DTV-Conference, 2014 International Conference on 2014.
90
TGV2ADCSIFT
6.20 %
15.15 %
1.5 px
4.5 px
100.00 %
12s
GPU @ 2.4 Ghz (C/C++)
J. Braux-Zin, R. Dupont and A. Bartoli: A General Dense Image Matching
Framework Combining Direct and Feature-based
Costs . International Conference on
Computer Vision (ICCV) 2013.
91
DiscreteFlow
code
6.23 %
16.63 %
1.3 px
3.6 px
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.
92
PPM
code
6.23 %
15.91 %
1.4 px
5.0 px
100.00 %
36 s
1 core @ 2.8 Ghz (C/C++)
F. Kuang: PatchMatch algorithms for
motion estimation and stereo reconstruction . 2017.
93
BTF-ILLUM
6.52 %
11.03 %
1.5 px
2.8 px
100.00 %
80 seconds
1 core @ 3.0 Ghz (C/C++)
O. Demetz, M. Stoll, S. Volz, J. Weickert and A. Bruhn: Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow . Computer Vision -- ECCV 2014 2014.
94
DeepFlow2
code
6.61 %
17.35 %
1.4 px
5.3 px
100.00 %
22 s
1 core @ >3.5 Ghz (C/C++)
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid: DeepMatching: Hierarchical Deformable
Dense Matching . 2015.
95
Data-Flow
code
7.11 %
14.57 %
1.9 px
5.5 px
100.00 %
3 min
2 cores @ 2.5 Ghz (Matlab + C/C++)
C. Vogel, S. Roth and K. Schindler: An Evaluation of Data Costs for
Optical
Flow . German Conference on Pattern
Recognition (GCPR) 2013.
96
DeepFlow
code
7.22 %
17.79 %
1.5 px
5.8 px
100.00 %
17 s
1 core @ 3.6Ghz (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.
97
EpicFlow
code
7.88 %
17.08 %
1.5 px
3.8 px
100.00 %
15 s
1 core @ 3.6 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.
98
TVL1-HOG
7.91 %
18.90 %
2.0 px
6.1 px
100.00 %
180 s
2 cores @ 3.0 Ghz (Matlab)
H. Rashwan, M. Mohamed, M. Garcia, B. Mertsching and D. Puig: Illumination Robust Optical Flow Model
Based on Histogram of Oriented Gradients . German Conference on Pattern
Recognition 2013 .
99
FastFlow
7.93 %
12.95 %
1.4 px
2.4 px
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
100
MLDP-OF
8.67 %
18.78 %
2.4 px
6.7 px
100.00 %
160 s
2 cores @ 2.5 Ghz (Matlab)
M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia and D. Puig: Illumination-Robust Optical Flow Using Local Directional Pattern . IEEE Transactions on Circuits and Systems for Video Technology 2014 .
101
SparseFlow
code
9.09 %
19.32 %
2.6 px
7.6 px
100.00 %
10 s
1 core @ 3.5 Ghz (Matlab + C/C++)
R. Timofte and L. Gool: SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow . WACV 2015 .
102
CRTflow
9.43 %
18.72 %
2.7 px
6.5 px
100.00 %
18 s
GPU @ 1.0 Ghz (C/C++)
O. Demetz, D. Hafner and J. Weickert: The Complete Rank Transform: A Tool for Accurate and Morphologically Invariant Matching of Structure . Proc.~British Machine Vision Conference 2013 (BMVC) 2013.
103
C++
code
10.04 %
20.26 %
2.6 px
7.1 px
100.00 %
8.5 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.
104
TF+OFM
code
10.22 %
18.46 %
2.0 px
5.0 px
100.00 %
350 s
1 cores @ 2.5 Ghz (Matlab + C/C++)
R. Kennedy and C. Taylor: Optical Flow with Geometric Occlusion
Estimation and Fusion of Multiple Frames . EMMCVPR 2015.
105
ROF-NND
10.44 %
21.23 %
2.5 px
6.5 px
100.00 %
50 s
4 cores @ 3.5 Ghz (Matlab + C/C++)
S. Ali, C. Daul, E. Galbrun and W. Blondel: Illumination invariant optical flow using
neighborhood descriptors . Computer Vision and Image Understanding 2015.
106
C+NL
code
10.49 %
20.64 %
2.8 px
7.2 px
100.00 %
14.8 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.
107
DSPyNet
10.64 %
19.10 %
2.4 px
5.5 px
100.00 %
0.02 s
GPU @ 3.0 Ghz (C/C++)
Z. Sun and H. Wang: Deeper Spatial Pyramid Network with Refined
Up-Sampling for Optical Flow Estimation . Proc. Pacific Rim Conference on
Multimedia 2018.
108
fSGM
10.74 %
22.66 %
3.2 px
12.2 px
100.00 %
60 s
1 core @ 2.4 Ghz (C/C++)
S. Hermann and R. Klette: Hierarchical Scan Line Dynamic Programming for Optical
Flow using Semi-Global Matching . ACCV Workshops 2012.
109
TGV2CENSUS
code
11.03 %
18.37 %
2.9 px
6.6 px
100.00 %
4 s
GPU+CPU @ 3.0 Ghz (Matlab + C/C++)
M. Werlberger: Convex Approaches for High Performance
Video Processing . 2012. R. Ranftl, S. Gehrig, T. Pock and H. Bischof: Pushing the Limits of Stereo Using Variational Stereo Estimation . IV 2012.
110
PCA-Layers
code
12.02 %
19.11 %
2.5 px
5.2 px
100.00 %
3.2 s
1 core @ 2.5 Ghz (Python + C/C++)
J. Wulff and M. Black: Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers . IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015 2015.
111
AggregFlow
12.23 %
21.79 %
3.1 px
7.4 px
100.00 %
35 min
1 core @ 2.5 Ghz (C/C++)
D. Fortun, P. Bouthemy and C. Kervrann: Aggregation of local parametric candidates with exemplar-based occlusion
handling for optical flow . Computer Vision and Image Understanding 2016.
112
SPyNet
code
12.31 %
20.97 %
2.0 px
4.1 px
100.00 %
0.16 s
Nvidia TitanX GPU (lua)
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.
113
C+NL-fast
code
12.36 %
22.28 %
3.2 px
7.9 px
100.00 %
2.9 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.
114
EPPM
code
12.75 %
23.55 %
2.5 px
9.2 px
100.00 %
0.25 s
GPU @ 1.0 Ghz (C/C++)
L. Bao, Q. Yang and H. Jin: Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow . IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014.
115
CPNFlow
13.01 %
19.17 %
2.0 px
3.6 px
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.
116
HS
code
14.75 %
24.11 %
4.0 px
9.0 px
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.
117
Grts-Flow-V2
15.63 %
26.41 %
3.2 px
8.4 px
100.00 %
0.3 s
1 core @ 1.5 Ghz (C/C++)
E. Zhu, Y. Li and Y. Shi: Fast Optical Flow Estimation Without Parallel
Architectures . IEEE Transactions on Circuits and Systems
for Video Technology 2016.
118
PCA-Flow
code
15.67 %
24.59 %
2.7 px
6.2 px
100.00 %
0.19 s
1 core @ 2.5 Ghz (Python + C/C++)
J. Wulff and M. Black: Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers . IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015 2015.
119
GC-BM-Bino
18.83 %
29.30 %
5.0 px
12.1 px
83.73 %
1.3 s
2 cores @ 2.5 Ghz (C/C++)
B. Kitt and H. Lategahn: Trinocular Optical Flow Estimation for Intelligent
Vehicle Applications . ITSC 2012.
120
eFolki
19.31 %
28.79 %
5.2 px
10.9 px
100.00 %
0.026 s
GPU @ 700 Mhz (C/C++)
A. Plyer, G. Le Besnerais and F. Champagnat: Massively parallel Lucas Kanade optical flow for real-time video processing applications . Journal of Real-Time Image Processing 2014.
121
GC-BM-Mono
19.38 %
29.80 %
5.0 px
12.1 px
84.33 %
1.3 s
2 cores @ 2.5 Ghz (C/C++)
B. Kitt and H. Lategahn: Trinocular Optical Flow Estimation for Intelligent
Vehicle Applications . ITSC 2012.
122
SVFilterOh
20.38 %
30.38 %
4.3 px
9.1 px
100.00 %
2 s
1 core @ 3 Ghz (C/C++), 1 GTX 780 GPU
M. Helala and F. Qureshi: Fast Estimation of Large Displacement Optical Flow
Using Dominant Motion Patterns & Sub-Volume PatchMatch
Filtering . Proc. 14th Conference on Computer and Robot Vision
(CRV 17) 2017.
123
RSRS-Flow
20.78 %
29.75 %
6.2 px
12.1 px
100.00 %
4 min
1 core @ 2.5 Ghz (Matlab)
P. Ghosh and B. Manjunath: Robust Simultaneous Registration and Segmentation with Sparse Error Reconstruction . PAMI 2012.
124
ALD
21.37 %
30.71 %
10.9 px
16.0 px
100.00 %
110 s
1 core @ 2.5 Ghz (C/C++)
M. Stoll, S. Volz and A. Bruhn: Adaptive Integration of Feature Matches into Variational Optical Flow Methods . ACCV 2012.
125
LDOF
code
21.93 %
31.39 %
5.6 px
12.4 px
100.00 %
1 min
1 core @ 2.5 Ghz (C/C++)
T. Brox and J. Malik: Large Displacement Optical Flow:
Descriptor Matching in Variational Motion
Estimation . PAMI 2011.
126
2Bit-BM-tele
code
24.10 %
33.59 %
7.1 px
15.2 px
100.00 %
6 min
1 core @ 2.4 Ghz (C/C++)
R. Xu and D. Taubman: Robust Dense Block-Based Motion Estimation Using a
Two-Bit Transform on a Laplacian Pyramid . 20th Proc. IEEE Int. Conf. Image Proc. 2013 2013.
127
DB-TV-L1
code
30.87 %
39.25 %
7.9 px
14.6 px
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.
128
PyrLK
code
31.48 %
40.08 %
15.6 px
29.6 px
92.33 %
1.3 s
4 cores @ 3.5 Ghz (C/C++)
J. Bouguet: Pyramidal implementation of the affine lucas
kanade feature tracker description of the algorithm . .
129
GCSF
33.17 %
41.71 %
7.0 px
15.3 px
48.27 %
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.
130
UnsupFlownet
34.85 %
43.15 %
4.6 px
11.3 px
100.00 %
0.03 s
GPU @ 3.0 Ghz (C/C++)
J. Yu, A. Harley and K. Derpanis: Back to Basics: Unsupervised Learning of
Optical Flow via Brightness Constancy and Motion
Smoothness . ECCV 2016.
131
HAOF
code
35.87 %
43.46 %
11.1 px
18.3 px
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.
132
FSDEF
36.85 %
44.65 %
8.8 px
16.4 px
41.81 %
0.26s
4 cores sandy bridge @ 3.5 Ghz (C/C++)
M. Garrigues and A. Manzanera: Fast Semi Dense Epipolar Flow Estimation . 2017 IEEE Winter Conference on
Applications of Computer Vision (WACV) 2017.
133
FlowNetS+ft
code
37.05 %
44.49 %
5.0 px
9.1 px
100.00 %
0.08 s
GPU @ 1.0 Ghz (C/C++)
A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. Smagt, D. Cremers and T. Brox: FlowNet: Learning Optical Flow with Convolutional Networks . ICCV 2015.
134
RLOF(IM-GM)
37.49 %
44.78 %
8.2 px
15.4 px
11.84 %
3.7 s
4 core @ 3.4 Ghz (C/C++)
T. Senst, J. Geistert and T. Sikora: Robust local optical flow: Long-range motions and varying illuminations . 2016 IEEE International Conference on Image Processing (ICIP) 2016.
135
BERLOF
code
37.66 %
45.27 %
8.5 px
16.2 px
15.26 %
0.231 s
GPU @ 700 Mhz (C/C++) GeForce GTX 680
T. Senst, J. Geistert, I. Keller and T. Sikora: Robust Local Optical Flow Estimation using Bilinear Equations for Sparse Motion Estimation . 20th IEEE International Conference on Image Processing 2013.
136
DIS-FAST
code
38.58 %
46.21 %
7.8 px
14.4 px
100.00 %
0.023
1 core @ 4 Ghz (C/C++)
T. Kroeger, R. Timofte, D. Dai and L. Van Gool: Fast Optical Flow using Dense Inverse
Search . ECCV 2016.
137
RLOF
code
38.60 %
46.13 %
8.7 px
16.5 px
14.76 %
0.488 s
GPU @ 700 Mhz (C/C++) GeForce GTX 680
T. Senst, V. Eiselein and T. Sikora: Robust Local Optical Flow for Feature Tracking . TCSVT 2012.
138
Next-Flow
code
39.12 %
46.37 %
5.1 px
9.2 px
100.00 %
0.1 s
GPU @ 1.0 Ghz (C/C++)
N. Sedaghat, M. Zolfaghari and T. Brox: Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild . 2017.
139
PolyExpand
47.59 %
54.00 %
17.3 px
25.3 px
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion . SCIA 2003.
140
OCV-BM
code
63.50 %
68.19 %
24.4 px
33.3 px
100.00 %
1.5 min
1 core @ 2.5 Ghz (C/C++)
G. Bradski: The OpenCV Library . Dr. Dobb's Journal of Software
Tools 2000.
141
Pyramid-LK
code
65.81 %
70.16 %
21.8 px
33.2 px
99.90 %
1.5 min
1 core @ 2.5 Ghz (Matlab)
J. Bouguet: Pyramidal implementation of the Lucas
Kanade feature tracker . Intel 2000.
142
MEDIAN
79.37 %
82.46 %
16.0 px
24.0 px
99.94 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
143
AVERAGE
81.27 %
84.06 %
16.3 px
24.7 px
99.94 %
0.01 s
1 core @ 2.5 Ghz (C/C++)