Method
Setting
Code
D1-bg
D1-fg
D1-all
Density
Runtime
Environment
1
RAS-Net(SAIT China)
1.42 %
2.73 %
1.64 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
2
ADLAB-RFDisp
1.41 %
2.77 %
1.64 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
3
LEAStereo
code
1.40 %
2.91 %
1.65 %
100.00 %
0.30 s
GPU @ 2.5 Ghz (Python)
X. Cheng, Y. Zhong, M. Harandi, Y. Dai, X. Chang, H. Li, T. Drummond and Z. Ge: Hierarchical Neural Architecture Search
for Deep Stereo Matching . Advances in Neural Information
Processing Systems 2020.
4
GA-fw
1.52 %
2.49 %
1.68 %
100.00 %
1.8 s
1 core @ 2.5 Ghz (Python)
5
OptStereo
1.44 %
2.95 %
1.69 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
6
MSMD-Net(only MS)
1.41 %
3.13 %
1.69 %
100.00 %
0.32 s
1 core @ 2.5 Ghz (C/C++)
7
Dahua_SF
1.48 %
2.83 %
1.71 %
100.00 %
0.5 s
1 core @ 2.5 Ghz (Python)
8
Dahua_Stereo
1.48 %
2.83 %
1.71 %
100.00 %
1.52 s
GPU @ 2.5 Ghz (Python)
9
SCV-Stereo
1.44 %
3.05 %
1.71 %
100.00 %
0.08 s
GPU @ 2.5 Ghz (Python)
10
CANet
1.45 %
3.11 %
1.72 %
100.00 %
0.70 s
1 core @ 2.5 Ghz (C/C++)
11
CSPN
1.51 %
2.88 %
1.74 %
100.00 %
1.0 s
GPU @ 2.5 Ghz (Python)
X. Cheng, P. Wang and R. Yang: Learning Depth with Convolutional Spatial
Propagation Network . IEEE Transactions on Pattern Analysis
and Machine Intelligence(T-PAMI) 2019.
12
GA_CSA
1.49 %
3.01 %
1.74 %
100.00 %
1.8 s
1 core @ 2.5 Ghz (Python)
13
DJIStereo
1.46 %
3.20 %
1.75 %
100.00 %
1.5 s
1 core @ 2.5 Ghz (Python)
14
DHSM
1.54 %
2.92 %
1.77 %
100.00 %
2 s
1 core @ 2.5 Ghz (Python)
15
NLCA-Net_V2
1.41 %
3.56 %
1.77 %
100.00 %
0.67 s
1 core @ 2.5 Ghz (C/C++)
16
HPA-Net
1.50 %
3.31 %
1.80 %
100.00 %
0.42 s
GPU @ 2.5 Ghz (Python)
17
SUW-Stereo
1.47 %
3.45 %
1.80 %
100.00 %
1.8 s
1 core @ 2.5 Ghz (C/C++)
H. Ren, A. Raj, M. El-Khamy and J. Lee: SUW-Learn: Joint Supervised,
Unsupervised, Weakly Supervised Deep Learning for
Monocular Depth Estimation . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition Workshops 2020.
18
nsg
1.47 %
3.46 %
1.80 %
100.00 %
1.82 s
GPU @ 1.5 Ghz (Python)
19
RME
1.48 %
3.46 %
1.81 %
100.00 %
2 s
GPU @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
20
GANet-deep
code
1.48 %
3.46 %
1.81 %
100.00 %
1.8 s
GPU @ 2.5 Ghz (Python)
F. Zhang, V. Prisacariu, R. Yang and P. Torr: GA-Net: Guided Aggregation Net for
End-to-end Stereo Matching . Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition
(CVPR) 2019.
21
MaskRCNN+ISF
1.48 %
3.46 %
1.81 %
100.00 %
3.3 s
GPU @ 2.5 Ghz (Python)
22
Stereo expansion
code
1.48 %
3.46 %
1.81 %
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.
23
PSMNet-NL
1.58 %
3.01 %
1.82 %
100.00 %
0.41 s
GPU @ 2.5 Ghz (Python)
24
GA-Net+G
1.49 %
3.47 %
1.82 %
100.00 %
0.5 s
GPU (Python)
25
HDU-FCC
code
1.50 %
3.45 %
1.82 %
100.00 %
0.7 s
1 core @ 2.5 Ghz (C/C++)
26
PVStereo
1.50 %
3.43 %
1.82 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
27
gwcnet+DCA
1.49 %
3.51 %
1.83 %
100.00 %
0.27 s
GPU @ 2.5 Ghz (Python)
28
CMF
1.44 %
3.76 %
1.83 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
29
NLCA-Net-3
code
1.45 %
3.78 %
1.83 %
100.00 %
0.44 s
>8 cores @ 3.5 Ghz (C/C++)
Z. Rao, M. He, Y. Dai, Z. Zhu, B. Li and R. He: NLCA-Net: a non-local context attention
network for stereo matching . APSIPA Transactions on Signal and
Information Processing 2020.
30
HD3+_Flow
1.63 %
2.89 %
1.84 %
100.00 %
0.04 s
GPU @ 2.5 Ghz (Python)
31
AMNet
1.53 %
3.43 %
1.84 %
100.00 %
0.9 s
GPU @ 2.5 Ghz (Python)
X. Du, M. El-Khamy and J. Lee: AMNet: Deep Atrous Multiscale Stereo
Disparity Estimation Networks . 2019.
32
Gwc-RSSM
1.54 %
3.42 %
1.85 %
100.00 %
0.20 s
1 core @ 2.5 Ghz (Python)
33
UnDAF-GANet
1.53 %
3.49 %
1.86 %
100.00 %
1.8 s
GPU @ 2.5 Ghz (Python)
34
MFM-Net
1.51 %
3.67 %
1.87 %
100.00 %
0.47 s
GPU @ 1.5 Ghz (Python)
35
RAS-Net
1.61 %
3.16 %
1.87 %
100.00 %
0.23 s
1 core @ 2.5 Ghz (C/C++)
36
pcr_psm
1.53 %
3.62 %
1.88 %
100.00 %
0.46 s
GPU @ 2.5 Ghz (Python)
37
CFNet
1.54 %
3.56 %
1.88 %
100.00 %
0.18 s
1 core @ 2.5 Ghz (Python)
38
RigidMask+ISF
1.53 %
3.65 %
1.89 %
100.00 %
3.3 s
GPU @ 2.5 Ghz (Python)
39
AcfNet
code
1.51 %
3.80 %
1.89 %
100.00 %
0.48 s
GPU @ 2.5 Ghz (Python)
Y. Zhang, Y. Chen, X. Bai, S. Yu, K. Yu, Z. Li and K. Yang: Adaptive Unimodal Cost Volume Filtering for Deep
Stereo Matching . AAAI 2020.
40
LISAStereo
1.62 %
3.24 %
1.89 %
100.00 %
0.09 s
4 cores @ 2.5 Ghz (Python)
41
CAIS+PSMNet
1.57 %
3.62 %
1.91 %
100.00 %
0.38 s
GPU @ 2.5 Ghz (Python)
42
NLCA_NET_v2_RVC
1.51 %
3.97 %
1.92 %
100.00 %
0.67 s
GPU @ 2.5 Ghz (Python)
Z. Rao, M. He, Y. Dai, Z. Zhu, B. Li and R. He: NLCA-Net: a non-local context attention
network for stereo matching . APSIPA Transactions on Signal and
Information Processing 2020.
43
CDN
code
1.66 %
3.20 %
1.92 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity
Estimation . Advances in Neural Information
Processing Systems 2020.
44
Abc-Net
1.47 %
4.20 %
1.92 %
100.00 %
0.83 s
4 core @ 2.5 Ghz (Python)
45
HCGANet
1.64 %
3.38 %
1.93 %
100.00 %
0.064 s
GPU @ 2.5 Ghz (Python)
46
GANet-15
code
1.55 %
3.82 %
1.93 %
100.00 %
0.36 s
GPU (Pytorch)
F. Zhang, V. Prisacariu, R. Yang and P. Torr: GA-Net: Guided Aggregation Net for
End-to-end Stereo Matching . Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition
(CVPR) 2019.
47
CAL-Net
1.59 %
3.76 %
1.95 %
100.00 %
0.44 s
2 cores @ 2.5 Ghz (Python)
48
GWC_pcr
1.57 %
3.91 %
1.96 %
100.00 %
0.32 s
1 core @ 2.5 Ghz (Python)
49
NLCA-Net
code
1.53 %
4.09 %
1.96 %
100.00 %
0.6 s
1 core @ 2.5 Ghz (C/C++)
Z. Rao, M. He, Y. Dai, Z. Zhu, B. Li and R. He: NLCA-Net: a non-local context attention
network for stereo matching . APSIPA Transactions on Signal and
Information Processing 2020.
50
CFNet_RVC
1.65 %
3.53 %
1.96 %
100.00 %
0.22 s
GPU @ 2.5 Ghz (Python)
51
MSRFNet
1.68 %
3.38 %
1.96 %
100.00 %
0.056 s
GPU @ 2.5 Ghz (Python)
52
FWSM
1.69 %
3.37 %
1.97 %
100.00 %
0.42 s
1 core @ 2.5 Ghz (Python)
53
MonoStereo
1.63 %
3.73 %
1.98 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
54
UGwc
1.64 %
3.70 %
1.98 %
100.00 %
0.8 s
1 core @ 2.5 Ghz (Python)
55
WTHNet
1.63 %
3.75 %
1.98 %
100.00 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
56
HITNet
1.74 %
3.20 %
1.98 %
100.00 %
0.015 s
Titan V,
57
TS_FAD
1.85 %
2.69 %
1.99 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
58
SGNet
1.63 %
3.76 %
1.99 %
100.00 %
0.6 s
1 core @ 2.5 Ghz (Python + C/C++)
S. Chen, Z. Xiang, C. Qiao, Y. Chen and T. Bai: SGNet: Semantics Guided Deep Stereo
Matching . Proceedings of the Asian Conference
on Computer Vision (ACCV) 2020.
59
gwc_dcr_300
1.63 %
3.82 %
1.99 %
100.00 %
0.32 s
GPU @ 2.5 Ghz (Python)
60
CSN
code
1.59 %
4.03 %
2.00 %
100.00 %
0.6 s
1 core @ 2.5 Ghz (Python)
X. Gu, Z. Fan, S. Zhu, Z. Dai, F. Tan and P. Tan: Cascade cost volume for high-resolution
multi-view stereo and stereo matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition 2020.
61
GwcNet-cmd
1.57 %
4.14 %
2.00 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
62
MANet-Selected
1.58 %
4.13 %
2.00 %
100.00 %
0.88 s
GPU @ 2.5 Ghz (Python)
63
HD^3-Stereo
code
1.70 %
3.63 %
2.02 %
100.00 %
0.14 s
NVIDIA Pascal Titan XP
Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition
for Match Density Estimation . CVPR 2019.
64
CoT-Stereo
1.67 %
3.78 %
2.02 %
100.00 %
0.3 s
GPU @ 2.5 Ghz (Python)
65
CVL
1.71 %
3.59 %
2.02 %
100.00 %
0.36 s
1 core @ 2.5 Ghz (Python)
66
PSMNet-fw
1.74 %
3.50 %
2.03 %
100.00 %
0.41 s
1 core @ 2.5 Ghz (C/C++)
67
nsa
1.65 %
3.95 %
2.03 %
100.00 %
0.08 s
GPU @ 1.5 Ghz (Python)
68
AANet+
code
1.65 %
3.96 %
2.03 %
100.00 %
0.06 s
NVIDIA V100 GPU
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network
for Efficient Stereo Matching . CVPR 2020.
69
GwcNet_CSA
1.73 %
3.57 %
2.03 %
100.00 %
0.37 s
1 core @ 2.5 Ghz (C/C++)
70
MANet-Medium
1.63 %
4.15 %
2.05 %
100.00 %
0.88 s
GPU @ 2.5 Ghz (Python)
71
SM^3Net
code
1.65 %
4.03 %
2.05 %
100.00 %
0.54 s
1 core @ 2.5 Ghz (Python)
72
DDP_out_HD3
1.75 %
3.55 %
2.05 %
100.00 %
10 min
1 GPU (Python)
73
CAEF-Net
1.68 %
3.92 %
2.05 %
100.00 %
0.44 s
1 core @ 2.5 Ghz (Python)
74
MANet-Selected
1.61 %
4.29 %
2.06 %
100.00 %
0.88 s
GPU @ 2.5 Ghz (Python)
75
DSA-Net
1.68 %
3.95 %
2.06 %
100.00 %
0.46 s
GPU @ 2.5 Ghz (Python)
76
LR-PSMNet
code
1.65 %
4.13 %
2.06 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (Python)
W. Chuah, R. Tennakoon, R. Hoseinnezhad, A. Bab-Hadiashar and D. Suter: Adjusting Bias in Long Range Stereo
Matching: A semantics guided approach . 2020.
77
PSMNet++
1.63 %
4.27 %
2.07 %
100.00 %
0.36 s
GPU @ >3.5 Ghz (Python)
78
DeepStereo
1.71 %
3.87 %
2.07 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
79
PSMNet_pcr
1.71 %
3.89 %
2.07 %
100.00 %
0.32 s
GPU @ 2.5 Ghz (Python)
80
UnDAF-SENSE
1.75 %
3.70 %
2.07 %
100.00 %
0.32 s
GPU @ 2.5 Ghz (Python)
81
MANet-Large
1.61 %
4.41 %
2.08 %
100.00 %
0.88 s
GPU @ 2.5 Ghz (Python)
82
MGSNet
1.68 %
4.06 %
2.08 %
100.00 %
0.65 s
GPU @ 2.5 Ghz (Python)
83
PSM + SMD-Nets
1.69 %
4.01 %
2.08 %
100.00 %
0.41 s
1 core @ 2.5 Ghz (Python + C/C++)
84
EdgeStereo-V2
1.84 %
3.30 %
2.08 %
100.00 %
0.32s
Nvidia GTX Titan Xp
X. Song, X. Zhao, L. Fang, H. Hu and Y. Yu: Edgestereo: An effective multi-task
learning network for stereo matching and edge
detection . International Journal of Computer
Vision (IJCV) 2019.
85
DDP_in_HD3
1.78 %
3.64 %
2.09 %
100.00 %
1 s
1 core @ 2.5 Ghz (Python + C/C++)
86
GwcNet-gc ++
1.67 %
4.19 %
2.09 %
100.00 %
0.32 s
GPU @ 2.5 Ghz (Python)
87
PSM-BCD
1.67 %
4.20 %
2.09 %
100.00 %
0.32 s
NVIDIA Titan Xp, 8 core 1.7 Ghz, Pytorch
88
MDA-Net(New)
1.76 %
3.77 %
2.10 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
89
GwcNet-g
code
1.74 %
3.93 %
2.11 %
100.00 %
0.32 s
GPU @ 2.0 Ghz (Python + C/C++)
X. Guo, K. Yang, W. Yang, X. Wang and H. Li: Group-wise correlation stereo network . CVPR 2019.
90
False
1.75 %
3.93 %
2.11 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (Python)
91
SSPCVNet
1.75 %
3.89 %
2.11 %
100.00 %
0.9 s
1 core @ 2.5 Ghz (Python)
Z. Wu, X. Wu, X. Zhang, S. Wang and L. Ju: Semantic Stereo Matching With Pyramid Cost
Volumes . The IEEE International Conference on
Computer Vision (ICCV) 2019.
92
E-GWCNet
1.67 %
4.34 %
2.11 %
100.00 %
0.49 s
1 core @ 2.5 Ghz (Python)
93
WSMCnet
code
1.72 %
4.19 %
2.13 %
100.00 %
0.39s
Nvidia GTX 1070 (Pytorch)
Y. Wang, H. Wang, G. Yu, M. Yang, Y. Yuan and J. Quan: Stereo Matching Algorithm Based on Three-Dimensional Convolutional Neural Network . Acta Optica Sinica 2019.
94
DEINet+ft
1.72 %
4.26 %
2.14 %
100.00 %
0.23 s
GPU @ 2.5 Ghz (Python + C/C++)
95
HSM-1.8x
code
1.80 %
3.85 %
2.14 %
100.00 %
0.14 s
Titan X Pascal
G. Yang, J. Manela, M. Happold and D. Ramanan: Hierarchical Deep Stereo Matching on High-
Resolution Images . The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR) 2019.
96
DSMNet
1.78 %
3.97 %
2.14 %
100.00 %
0.67 s
1 core @ 2.5 Ghz (Python)
97
DeepPruner (best)
code
1.87 %
3.56 %
2.15 %
100.00 %
0.18 s
1 core @ 2.5 Ghz (C/C++)
S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching
via Differentiable PatchMatch . ICCV 2019.
98
DC3DC
1.84 %
3.75 %
2.16 %
100.00 %
1.5 s
1 core @ 2.5 Ghz (C/C++)
99
GANet++
1.88 %
3.53 %
2.16 %
100.00 %
0.04 s
GPU @ 2.5 Ghz (Python)
100
PSM+CRF
1.86 %
3.66 %
2.16 %
100.00 %
0.32 s
GPU @ 2.0 Ghz (C/C++)
101
Stereo-fusion-SJTU
1.87 %
3.61 %
2.16 %
100.00 %
0.7 s
Nvidia GTX Titan Xp
X. Song, X. Zhao, H. Hu and L. Fang: EdgeStereo: A Context Integrated Residual
Pyramid Network for Stereo Matching . Asian Conference on Computer Vision 2018.
102
AutoDispNet-CSS
code
1.94 %
3.37 %
2.18 %
100.00 %
0.9 s
1 core @ 2.5 Ghz (C/C++)
T. Saikia, Y. Marrakchi, A. Zela, F. Hutter and T. Brox: AutoDispNet: Improving Disparity
Estimation with AutoML . The IEEE International Conference
on Computer Vision (ICCV) 2019.
103
BGNet+
1.81 %
4.09 %
2.19 %
100.00 %
0.03 s
GPU @ 2.5 Ghz (Python)
104
Bi3D
code
1.95 %
3.48 %
2.21 %
100.00 %
0.48 s
GPU @ 1.5 Ghz (Python)
A. Badki, A. Troccoli, K. Kim, J. Kautz, P. Sen and O. Gallo: Bi3D: Stereo Depth Estimation via Binary Classifications . The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
105
MFRANet
1.78 %
4.35 %
2.21 %
100.00 %
0.32 s
GPU @ 2.5 Ghz (Python)
106
ICANet
1.81 %
4.23 %
2.21 %
100.00 %
0.47 s
GPU @ 2.5 Ghz (Python)
107
dh
1.86 %
4.01 %
2.22 %
100.00 %
1.9 s
1 core @ 2.5 Ghz (C/C++)
F. Zhang, V. Prisacariu, R. Yang and P. Torr: GA-Net: Guided Aggregation Net for
End-to-end Stereo Matching . Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition
(CVPR) 2019.
108
JS^3M(only SM)
code
1.79 %
4.37 %
2.22 %
100.00 %
0.45 s
GPU @ 2.5 Ghz (Python)
109
SENSE
code
2.07 %
3.01 %
2.22 %
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.
110
PSMNet+D
1.85 %
4.15 %
2.23 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
111
GA-TeNet
1.89 %
3.94 %
2.23 %
100.00 %
0.49 s
1 core @ 2.5 Ghz (C/C++)
112
MA-P
1.75 %
4.65 %
2.23 %
100.00 %
0.33 s
GPU @ 2.5 Ghz (Python)
113
CTFNet-v2
1.80 %
4.46 %
2.24 %
100.00 %
0.3 s
8 cores @ 2.5 Ghz (Python)
114
SegStereo
code
1.88 %
4.07 %
2.25 %
100.00 %
0.6 s
Nvidia GTX Titan Xp
G. Yang, H. Zhao, J. Shi, Z. Deng and J. Jia: SegStereo: Exploiting Semantic
Information for Disparity Estimation . ECCV 2018.
115
PSMNet+GLR
1.85 %
4.25 %
2.25 %
100.00 %
0.3 s
GPU (Python)
116
PDR_Net
1.85 %
4.24 %
2.25 %
100.00 %
0.19 s
1 core @ 2.5 Ghz (Python)
117
DRNet
1.82 %
4.42 %
2.25 %
100.00 %
0.45 s
8 cores @ 2.5 Ghz (Python)
118
DTF_SENSE
2.08 %
3.13 %
2.25 %
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.
119
SuperB
1.99 %
3.63 %
2.26 %
100.00 %
0.1 s
NVIDIA Tesla V100 + PyTorch 1.2.0
120
E-PSMNet
1.89 %
4.17 %
2.27 %
100.00 %
0.68 s
1 core @ 2.5 Ghz (Python)
121
CTFNet
1.81 %
4.56 %
2.27 %
100.00 %
0.7 s
8 cores @ 2.5 Ghz (Python)
122
MCV-MFC
1.95 %
3.84 %
2.27 %
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
Z. Liang, Y. Guo, Y. Feng, W. Chen, L. Qiao, L. Zhou, J. Zhang and H. Liu: Stereo Matching Using Multi-level Cost Volume and Multi-scale Feature Constancy . IEEE transactions on pattern analysis and machine intelligence 2019.
123
CTFNet-v1
1.85 %
4.35 %
2.27 %
100.00 %
0.6 s
8 cores @ 2.5 Ghz (Python)
124
HSM-1.5x
code
1.95 %
3.93 %
2.28 %
100.00 %
0.085 s
Titan X Pascal
G. Yang, J. Manela, M. Happold and D. Ramanan: Hierarchical Deep Stereo Matching on
High-
Resolution Images . The IEEE Conference on Computer
Vision
and Pattern Recognition (CVPR) 2019.
125
CVANet_RVC
1.74 %
4.98 %
2.28 %
100.00 %
0.8 s
1 core @ 2.5 Ghz (C/C++)
126
CCNet
1.74 %
4.98 %
2.28 %
100.00 %
0.8 s
1 core @ 2.5 Ghz (C/C++)
127
MSFGNet
1.79 %
4.73 %
2.28 %
100.00 %
0.14 s
GPU @ >3.5 Ghz (Python)
128
PSMNet-Naifan
1.82 %
4.64 %
2.29 %
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
129
SGNet
1.82 %
4.69 %
2.30 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
130
DWA
code
1.99 %
3.92 %
2.31 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
131
CFP-Net
code
1.90 %
4.39 %
2.31 %
100.00 %
0.9 s
8 cores @ 2.5 Ghz (Python)
Z. Zhu, M. He, Y. Dai, Z. Rao and B. Li: Multi-scale Cross-form Pyramid Network for Stereo Matching . arXiv preprint 2019.
132
PSMNet
code
1.86 %
4.62 %
2.32 %
100.00 %
0.41 s
Nvidia GTX Titan Xp
J. Chang and Y. Chen: Pyramid Stereo Matching Network . arXiv preprint arXiv:1803.08669 2018.
133
GANetREF_RVC
code
1.88 %
4.58 %
2.33 %
100.00 %
1.62 s
GPU @ >3.5 Ghz (Python + C/C++)
F. Zhang, V. Prisacariu, R. Yang and P. Torr: GA-Net: Guided Aggregation Net for End-
to-end Stereo Matching . Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 2019.
134
DEINet+
1.82 %
4.88 %
2.33 %
100.00 %
0.21 s
GPU @ 2.5 Ghz (Python + C/C++)
135
JDCNet
1.91 %
4.47 %
2.33 %
100.00 %
0.079s
NVIDIA V100
136
JPSMNet
1.93 %
4.40 %
2.34 %
100.00 %
0.47 s
GPU @ 2.5 Ghz (Python)
137
PSM+LGF55
1.89 %
4.76 %
2.37 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
138
VH-Net
2.11 %
3.71 %
2.38 %
100.00 %
0.4 s
GPU @ >3.5 Ghz (Python)
139
PSM+LGF551
1.88 %
4.91 %
2.39 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (Python)
140
MTLnet
2.07 %
4.01 %
2.39 %
100.00 %
0.09 s
RTX 2070(pytorch)
141
ASM
1.97 %
4.60 %
2.41 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
142
MABNet_origin
code
1.89 %
5.02 %
2.41 %
100.00 %
0.38 s
Nvidia rtx2080ti (Python)
J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network
Based on Multibranch Adjustable Bottleneck
Module . .
143
MFN_U_SF_DS_K
code
2.15 %
3.74 %
2.42 %
100.00 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
144
HybridNet
code
1.93 %
4.90 %
2.42 %
100.00 %
0.12 s
GPU @ 2.5 Ghz (Python)
145
FBNet
1.96 %
4.86 %
2.45 %
100.00 %
0.6 s
8 cores @ 2.5 Ghz (Python)
146
ANM3
1.95 %
5.19 %
2.49 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (Python)
147
ANM1
1.99 %
5.05 %
2.50 %
100.00 %
0.41 s
1 core @ 2.5 Ghz (Python)
148
ERSCNet
2.11 %
4.46 %
2.50 %
100.00 %
0.28 s
GPU @ 2.5 Ghz (Python)
Anonymous: ERSCNet . Proceedings of the European
Conference on Computer Vision (ECCV) 2020.
149
BGNet
2.07 %
4.74 %
2.51 %
100.00 %
0.02 s
GPU @ >3.5 Ghz (Python)
150
LFENet
2.26 %
3.88 %
2.53 %
100.00 %
0.09 s
GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
151
MDA-Net
2.12 %
4.63 %
2.54 %
100.00 %
0.7 s
1 core @ 2.5 Ghz (Python)
152
UberATG-DRISF
2.16 %
4.49 %
2.55 %
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.
153
AANet
code
1.99 %
5.39 %
2.55 %
100.00 %
0.062 s
NVIDIA V100 GPU
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network
for Efficient Stereo Matching . CVPR 2020.
154
PDSNet
2.29 %
4.05 %
2.58 %
100.00 %
0.5 s
1 core @ 2.5 Ghz (Python)
S. Tulyakov, A. Ivanov and F. Fleuret: Practical Deep Stereo (PDS): Toward
applications-friendly deep stereo matching . Proceedings of the international conference
on Neural Information Processing Systems (NIPS) 2018.
155
SPP
2.10 %
5.02 %
2.59 %
100.00 %
0.41 s
4 cores @ 2.0 Ghz (Python)
156
DeepPruner (fast)
code
2.32 %
3.91 %
2.59 %
100.00 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching
via Differentiable PatchMatch . ICCV 2019.
157
FADNet
code
2.50 %
3.10 %
2.60 %
100.00 %
0.05 s
Tesla V100 (Python)
Q. Wang, S. Shi, S. Zheng, K. Zhao and X. Chu: FADNet: A Fast and Accurate Network
for Disparity Estimation . arXiv preprint arXiv:2003.10758 2020.
158
MSC_U_SF_DS_K
code
2.29 %
4.17 %
2.60 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
159
SCV
code
2.22 %
4.53 %
2.61 %
100.00 %
0.36 s
Nvidia GTX 1080 Ti
C. Lu, H. Uchiyama, D. Thomas, A. Shimada and R. Taniguchi: Sparse Cost Volume for Efficient
Stereo Matching . Remote Sensing 2018.
160
2DFuseNet
2.08 %
5.42 %
2.63 %
100.00 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
161
WaveletStereo:
2.24 %
4.62 %
2.63 %
100.00 %
0.27 s
1 core @ 2.5 Ghz (C/C++)
Anonymous: WaveletStereo: Learning wavelet coefficients
for stereo matching . arXiv: Computer Vision and Pattern
Recognition 2019.
162
MCDRNet
2.09 %
5.42 %
2.65 %
100.00 %
0.032 s
1 core @ 2.5 Ghz (C/C++)
163
AANet_RVC
2.23 %
4.89 %
2.67 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for
Efficient Stereo Matching . CVPR 2020.
164
CRL
code
2.48 %
3.59 %
2.67 %
100.00 %
0.47 s
Nvidia GTX 1080
J. Pang, W. Sun, J. Ren, C. Yang and Q. Yan: Cascade residual learning: A two-stage
convolutional neural network for stereo
matching . ICCV Workshop on Geometry Meets
Deep Learning 2017.
165
GC+CRF
2.11 %
5.71 %
2.71 %
100.00 %
0.27 s
GPU @ 2.0 Ghz (C/C++)
166
MSCVNet
2.31 %
5.41 %
2.82 %
100.00 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
167
PCStereo
2.39 %
4.98 %
2.82 %
100.00 %
0.2 s
GPU @ 2.5 Ghz (Python)
168
NVstereo2D
2.57 %
4.20 %
2.84 %
100.00 %
0.01 s
GPU @ 2.5 Ghz (Python)
169
GC-NET
2.21 %
6.16 %
2.87 %
100.00 %
0.9 s
Nvidia GTX Titan X
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach and A. Bry: End-to-End Learning of Geometry and Context for
Deep Stereo Regression . Proceedings of the International Conference on
Computer Vision (ICCV) 2017.
170
BSDCNet
2.49 %
4.98 %
2.90 %
100.00 %
0.025s
1 core @ 2.5 Ghz (C/C++)
171
LRCR
2.55 %
5.42 %
3.03 %
100.00 %
49.2 s
Nvidia GTX Titan X
Z. Jie, P. Wang, Y. Ling, B. Zhao, Y. Wei, J. Feng and W. Liu: Left-Right Comparative Recurrent Model for
Stereo Matching . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2018.
172
DCNet
2.70 %
4.70 %
3.04 %
100.00 %
0.025s
GPU @ Nvidia GTX 1080 (Tensorflow)
173
SLNet
2.61 %
5.31 %
3.06 %
100.00 %
0.6 s
1 core @ 2.5 Ghz (Python)
174
Fast DS-CS
code
2.83 %
4.31 %
3.08 %
100.00 %
0.02 s
GPU @ 2.0 Ghz (Python + C/C++)
K. Yee and A. Chakrabarti: Fast Deep Stereo with 2D Convolutional
Processing of Cost Signatures . WACV 2020 (to appear).
175
FDNet
2.83 %
4.31 %
3.08 %
100.00 %
0.7 s
1 core @ 2.5 Ghz (C/C++)
176
AdaStereo
2.59 %
5.55 %
3.08 %
100.00 %
0.41 s
GPU @ 2.5 Ghz (Python)
X. Song, G. Yang, X. Zhu, H. Zhou, Z. Wang and J. Shi: AdaStereo: A Simple and Efficient Approach
for Adaptive Stereo Matching . arXiv preprint arXiv:2004.04627 2020.
177
RecResNet
code
2.46 %
6.30 %
3.10 %
100.00 %
0.3 s
GPU @ NVIDIA TITAN X (Tensorflow)
K. Batsos and P. Mordohai: RecResNet: A Recurrent Residual CNN
Architecture for Disparity Map Enhancement . In International Conference on 3D
Vision (3DV) 2018.
178
NVStereoNet
code
2.62 %
5.69 %
3.13 %
100.00 %
0.6 s
NVIDIA Titan Xp
N. Smolyanskiy, A. Kamenev and S. Birchfield: On the Importance of Stereo for Accurate
Depth Estimation: An Efficient Semi-Supervised
Deep Neural Network Approach . arXiv preprint arXiv:1803.09719 2018.
179
NineNet2
2.83 %
4.64 %
3.13 %
100.00 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
180
PMY-net
2.63 %
5.72 %
3.15 %
100.00 %
1 s
1 core @ 2.5 Ghz (Python)
181
Net3_2015
2.69 %
5.44 %
3.15 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
182
DRR
2.58 %
6.04 %
3.16 %
100.00 %
0.4 s
Nvidia GTX Titan X
S. Gidaris and N. Komodakis: Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling . arXiv preprint arXiv:1612.04770 2016.
183
NineNet3
2.69 %
5.57 %
3.17 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
184
SimpleStereo
2.69 %
5.60 %
3.17 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
185
Net3_2015
2.71 %
5.55 %
3.18 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python + C/C++)
186
NineNet
2.70 %
5.94 %
3.24 %
100.00 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
187
DMSNet
2.81 %
5.39 %
3.24 %
100.00 %
0.015625 s
1 core @ 2.5 Ghz (Python)
188
FPN
2.85 %
5.37 %
3.27 %
100.00 %
1 s
1 core @ 2.5 Ghz (Python)
189
SFFNet
2.69 %
6.23 %
3.28 %
100.00 %
0.07 s
GPU @ 2.5 Ghz (Python)
190
DMSNetv2
2.80 %
5.85 %
3.31 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
191
DWARF
3.20 %
3.94 %
3.33 %
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.
192
AbNet1
3.14 %
4.43 %
3.35 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
193
CaTeNet2
2.71 %
6.67 %
3.37 %
100.00 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
194
SsSMnet
2.70 %
6.92 %
3.40 %
100.00 %
0.8 s
P100
Y. Zhong, Y. Dai and H. Li: Self-Supervised Learning for Stereo
Matching with Self-Improving Ability . arXiv:1709.00930 2017.
195
L-ResMatch
code
2.72 %
6.95 %
3.42 %
100.00 %
48 s
1 core @ 2.5 Ghz (C/C++)
A. Shaked and L. Wolf: Improved Stereo Matching with Constant Highway
Networks and Reflective Loss . arXiv preprint arxiv:1701.00165 2016.
196
Displets v2
code
3.00 %
5.56 %
3.43 %
100.00 %
265 s
>8 cores @ 3.0 Ghz (Matlab + C/C++)
F. Guney and A. Geiger: Displets: Resolving Stereo Ambiguities
using Object Knowledge . Conference on Computer Vision and
Pattern Recognition (CVPR) 2015.
197
LBPS
code
2.85 %
6.35 %
3.44 %
100.00 %
0.39 s
GPU @ 2.5 Ghz (C/C++)
P. Knöbelreiter, C. Sormann, A. Shekhovtsov, F. Fraundorfer and T. Pock: Belief Propagation Reloaded: Learning
BP-Layers for Labeling Problems . IEEE Conference on Computer Vision
and Pattern Recognition (CVPR) 2020.
198
CaTeNet
2.65 %
8.21 %
3.58 %
100.00 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
199
ACOSF
2.79 %
7.56 %
3.58 %
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.
200
CNNF+SGM
2.78 %
7.69 %
3.60 %
100.00 %
71 s
TESLA K40C
F. Zhang and B. Wah: Fundamental Principles on Learning New
Features for Effective Dense Matching . IEEE Transactions on Image
Processing 2018.
201
PBCP
2.58 %
8.74 %
3.61 %
100.00 %
68 s
Nvidia GTX Titan X
A. Seki and M. Pollefeys: Patch Based Confidence Prediction for
Dense Disparity Map . British Machine Vision Conference
(BMVC) 2016.
202
ASMNet
3.18 %
5.98 %
3.64 %
100.00 %
0.04 s
4 cores @ 2.5 Ghz (Python)
203
SGM-Net
2.66 %
8.64 %
3.66 %
100.00 %
67 s
Titan X
A. Seki and M. Pollefeys: SGM-Nets: Semi-Global Matching With Neural
Networks . CVPR 2017.
204
STTR
3.23 %
6.06 %
3.70 %
99.98 %
0.6 s
1 core @ 2.5 Ghz (C/C++)
205
Three3
2.99 %
7.33 %
3.71 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
206
HSM-Net_RVC
code
2.74 %
8.73 %
3.74 %
100.00 %
0.97 s
GPU @ 2.5 Ghz (Python)
G. Yang, J. Manela, M. Happold and D. Ramanan: Hierarchical deep stereo matching on
high-resolution images . Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition 2019.
207
CasNet
2.94 %
7.77 %
3.75 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
208
ECCV_RVC
3.28 %
6.40 %
3.80 %
100.00 %
0.6 s
GPU @ 1.0 Ghz (Python)
209
MABNet_tiny
code
3.04 %
8.07 %
3.88 %
100.00 %
0.11 s
Nvidia rtx2080ti (Python)
J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network
Based on Multibranch Adjustable Bottleneck
Module . .
210
MC-CNN-acrt
code
2.89 %
8.88 %
3.89 %
100.00 %
67 s
Nvidia GTX Titan X (CUDA, Lua/Torch7)
J. Zbontar and Y. LeCun: Stereo Matching by Training a Convolutional
Neural Network to Compare Image Patches . Submitted to JMLR .
211
FD-Fusion
code
3.22 %
7.44 %
3.92 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
M. Ferrera, A. Boulch and J. Moras: Fast Stereo Disparity Maps Refinement By Fusion of Data-Based And Model-Based Estimations . International Conference on 3D Vision (3DV) 2019.
212
Net3_3
3.52 %
6.22 %
3.97 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
213
Reversing-PSMNet
code
3.13 %
8.70 %
4.06 %
100.00 %
0.41 s
1 core @ 1.5 Ghz (Python)
F. Aleotti, F. Tosi, L. Zhang, M. Poggi and S. Mattoccia: Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation . European Conference on Computer Vision (ECCV) 2020.
214
Simpnet
3.26 %
8.09 %
4.06 %
100.00 %
0.6 s
1 core @ 2.5 Ghz (C/C++)
215
DAStereo
3.67 %
6.83 %
4.20 %
100.00 %
0.32 s
1 core @ 2.5 Ghz (Python)
216
F407NJJ-FLOW
3.25 %
9.11 %
4.22 %
100.00 %
-1 s
1 core @ 2.5 Ghz (C/C++)
217
PRSM
code
3.02 %
10.52 %
4.27 %
99.99 %
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.
218
DispNetC
code
4.32 %
4.41 %
4.34 %
100.00 %
0.06 s
Nvidia GTX Titan X (Caffe)
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy and T. Brox: A Large Dataset to Train
Convolutional Networks for
Disparity, Optical Flow, and Scene Flow
Estimation . CVPR 2016.
219
SGM-Forest
3.11 %
10.74 %
4.38 %
99.92 %
6 seconds
1 core @ 3.0 Ghz (Python/C/C++)
J. Schönberger, S. Sinha and M. Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching . European Conference on Computer Vision (ECCV) 2018.
220
SSF
3.55 %
8.75 %
4.42 %
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.
221
SMV
3.45 %
9.32 %
4.43 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (C/C++)
222
ISF
4.12 %
6.17 %
4.46 %
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.
223
Content-CNN
3.73 %
8.58 %
4.54 %
100.00 %
1 s
Nvidia GTX Titan X (Torch)
W. Luo, A. Schwing and R. Urtasun: Efficient Deep Learning for Stereo Matching . CVPR 2016.
224
MADnet
code
3.75 %
9.20 %
4.66 %
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
A. Tonioni, F. Tosi, M. Poggi, S. Mattoccia and L. Di Stefano: Real-Time self-adaptive deep stereo . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) 2019.
225
DTF_PWOC
3.91 %
8.57 %
4.68 %
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.
226
VN
4.29 %
7.65 %
4.85 %
100.00 %
0.5 s
GPU @ 3.5 Ghz (Python + C/C++)
P. Knöbelreiter and T. Pock: Learned Collaborative Stereo Refinement . German Conference on Pattern Recognition (GCPR) 2019.
227
LWANet
4.28 %
8.22 %
4.94 %
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
228
naive-stereo
4.43 %
7.65 %
4.96 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
229
MC-CNN-WS
code
3.78 %
10.93 %
4.97 %
100.00 %
1.35 s
1 core 2.5 Ghz + K40 NVIDIA, Lua-Torch
S. Tulyakov, A. Ivanov and F. Fleuret: Weakly supervised learning of deep
metrics for stereo reconstruction . ICCV 2017.
230
3DMST
3.36 %
13.03 %
4.97 %
100.00 %
93 s
1 core @ >3.5 Ghz (C/C++)
X. Lincheng Li and L. Zhang: 3D Cost Aggregation with Multiple Minimum
Spanning Trees for Stereo Matching . submitted to Applied Optics .
231
CBMV_ROB
code
3.55 %
12.09 %
4.97 %
100.00 %
250 s
6 core @ 3.0 Ghz (Python + C/C++)
K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching
Volume for Disparity Estimation . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2018.
232
OSF+TC
4.11 %
9.64 %
5.03 %
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.
233
CBMV
code
4.17 %
9.53 %
5.06 %
100.00 %
250 s
6 cores @ 3.0 Ghz (Python,C/C++,CUDA Nvidia TitanX)
K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching
Volume for Disparity Estimation . 2018.
234
PWOC-3D
code
4.19 %
9.82 %
5.13 %
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.
235
naive-stereo-v1
4.99 %
6.56 %
5.25 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
236
OSF 2018
code
4.11 %
11.12 %
5.28 %
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.
237
SPS-St
code
3.84 %
12.67 %
5.31 %
100.00 %
2 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.
238
MDP
4.19 %
11.25 %
5.36 %
100.00 %
11.4 s
4 cores @ 3.5 Ghz (Matlab + C/C++)
A. Li, D. Chen, Y. Liu and Z. Yuan: Coordinating Multiple Disparity Proposals for Stereo Computation . IEEE Conference on Computer Vision and Pattern Recognition 2016.
239
SFF++
4.27 %
12.38 %
5.62 %
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.
240
OSF
code
4.54 %
12.03 %
5.79 %
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.
241
pSGM
4.84 %
11.64 %
5.97 %
100.00 %
7.77 s
4 cores @ 3.5 Ghz (C/C++)
Y. Lee, M. Park, Y. Hwang, Y. Shin and C. Kyung: Memory-Efficient Parametric Semiglobal
Matching . IEEE Signal Processing Letters 2018.
242
CSF
4.57 %
13.04 %
5.98 %
99.99 %
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.
243
MBM
4.69 %
13.05 %
6.08 %
100.00 %
0.13 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: A Multi-Block-Matching Approach for Stereo . IV 2015.
244
PR-Sceneflow
code
4.74 %
13.74 %
6.24 %
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.
245
DispSegNet
4.20 %
16.97 %
6.33 %
100.00 %
0.9 s
GPU @ 2.5 Ghz (Python)
J. Zhang, K. Skinner, R. Vasudevan and M. Johnson-Roberson: DispSegNet: Leveraging Semantics for End-
to-End Learning of Disparity Estimation From
Stereo Imagery . IEEE Robotics and Automation Letters 2019.
246
DeepCostAggr
code
5.34 %
11.35 %
6.34 %
99.98 %
0.03 s
GPU @ 2.5 Ghz (C/C++)
A. Kuzmin, D. Mikushin and V. Lempitsky: End-to-end Learning of Cost-Volume Aggregation
for
Real-time Dense Stereo . 2017 IEEE 27th International Workshop on
Machine Learning for Signal Processing (MLSP) 2017.
247
SGM_RVC
5.06 %
13.00 %
6.38 %
100.00 %
0.11 s
Nvidia GTX 980
H. Hirschm\"uller: Stereo Processing by Semi-Global
Matching and Mutual Information . IEEE Transactions on Pattern
Analysis and Machine Intelligence 2008.
248
SceneFFields
5.12 %
13.83 %
6.57 %
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.
249
SPS+FF++
code
5.47 %
12.19 %
6.59 %
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.
250
Flow2Stereo
5.01 %
14.62 %
6.61 %
99.97 %
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.
251
FSF+MS
5.72 %
11.84 %
6.74 %
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.
252
AABM
4.88 %
16.07 %
6.74 %
100.00 %
0.08 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Stereo Image Warping for Improved Depth Estimation of Road Surfaces . IV 2013.
253
DistillFlow
5.00 %
15.88 %
6.81 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
254
SGM+C+NL
code
5.15 %
15.29 %
6.84 %
100.00 %
4.5 min
1 core @ 2.5 Ghz (C/C++)
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.
255
SGM+LDOF
code
5.15 %
15.29 %
6.84 %
100.00 %
86 s
1 core @ 2.5 Ghz (C/C++)
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.
256
SGM+SF
5.15 %
15.29 %
6.84 %
100.00 %
45 min
16 core @ 3.2 Ghz (C/C++)
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.
257
FC-DCNN
code
5.21 %
15.16 %
6.87 %
100.00 %
5 s
GPU @ >3.5 Ghz (Python)
258
SNCC
5.36 %
16.05 %
7.14 %
100.00 %
0.08 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: A Two-Stage Correlation Method for Stereoscopic Depth Estimation . DICTA 2010.
259
MBSF
5.63 %
15.04 %
7.20 %
100.00 %
1 min
GPU @ 2.5 Ghz (C/C++)
260
PASMnet
code
5.41 %
16.36 %
7.23 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (Python)
L. Wang, Y. Guo, Y. Wang, Z. Liang, Z. Lin, J. Yang and W. An: Parallax Attention for
Unsupervised
Stereo Correspondence Learning . IEEE Transactions on Pattern
Analysis and Machine Intelligence(T-PAMI) 2020.
261
AAFS
6.27 %
13.95 %
7.54 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
J. Chang, P. Chang and Y. Chen: Attention-Aware Feature Aggregation for
Real-time Stereo Matching on Edge Devices . Proceedings of the Asian Conference
on Computer Vision 2020.
262
FCU-Net
6.04 %
17.48 %
7.95 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
263
UUF-Net
6.35 %
16.60 %
8.06 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
264
GOUSM
6.42 %
17.24 %
8.22 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
265
CSCT+SGM+MF
6.91 %
14.87 %
8.24 %
100.00 %
0.0064 s
Nvidia GTX Titan X @ 1.0 Ghz (CUDA)
D. Hernandez-Juarez, A. Chacon, A. Espinosa, D. Vazquez, J. Moure and A. Lopez: Embedded real-time stereo estimation via Semi-Global Matching on the GPU . Procedia Computer Science 2016.
266
MBMGPU
6.61 %
16.70 %
8.29 %
100.00 %
0.0019 s
GPU @ 1.0 Ghz (CUDA)
Q. Chang and T. Maruyama: Real-Time Stereo Vision System:
A Multi-Block Matching on GPU . IEEE Access 2018.
267
MeshStereo
code
5.82 %
21.21 %
8.38 %
100.00 %
87 s
1 core @ 2.5 Ghz (C/C++)
C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao and Y. Rui: MeshStereo: A Global Stereo Model With
Mesh Alignment Regularization for View
Interpolation . The IEEE International Conference on
Computer Vision (ICCV) 2015.
268
PCOF + ACTF
6.31 %
19.24 %
8.46 %
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.
269
PCOF-LDOF
6.31 %
19.24 %
8.46 %
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.
270
OASM-Net
6.89 %
19.42 %
8.98 %
100.00 %
0.73 s
GPU @ 2.5 Ghz (Python)
A. Li and Z. Yuan: Occlusion Aware Stereo Matching via Cooperative Unsupervised Learning . Proceedings of the Asian Conference on Computer Vision, ACCV 2018.
271
ELAS_RVC
code
7.38 %
21.15 %
9.67 %
100.00 %
0.19 s
4 cores @ >3.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching . ACCV 2010.
272
DDP_out_ELAS_params1
7.45 %
21.11 %
9.72 %
100.00 %
30 min
GPU @ 2.5 Ghz (Python)
273
ELAS
code
7.86 %
19.04 %
9.72 %
92.35 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching . ACCV 2010.
274
DDP_in_ELAS_params1
7.48 %
21.14 %
9.76 %
99.97 %
1 s
1 core @ 2.5 Ghz (Matlab + C/C++)
275
ACMC
8.50 %
17.87 %
10.06 %
98.53 %
0.05 s
GPU @ 1.5 Ghz (C/C++)
276
REAF
code
8.43 %
18.51 %
10.11 %
100.00 %
1.1 s
1 core @ 2.5 Ghz (C/C++)
C. Cigla: Recursive Edge-Aware Filters for Stereo Matching . The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2015.
277
iGF
8.64 %
21.85 %
10.84 %
100.00 %
220 s
1 core @ 3.0 Ghz (C/C++)
R. Hamzah, H. Ibrahim and A. Hassan: Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation . Journal of Visual Communication and Image Representation 2016.
278
OCV-SGBM
code
8.92 %
20.59 %
10.86 %
90.41 %
1.1 s
1 core @ 2.5 Ghz (C/C++)
H. Hirschmueller: Stereo processing by semiglobal matching
and mutual information . PAMI 2008.
279
TW-SMNet
11.92 %
12.16 %
11.96 %
100.00 %
0.7 s
GPU @ 2.5 Ghz (Python)
M. El-Khamy, H. Ren, X. Du and J. Lee: TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching . arXiv:1906.04463 2019.
280
SDM
9.41 %
24.75 %
11.96 %
62.56 %
1 min
1 core @ 2.5 Ghz (C/C++)
J. Kostkova: Stratified dense matching for stereopsis
in complex scenes . BMVC 2003.
281
SGM&FlowFie+
11.93 %
20.57 %
13.37 %
81.24 %
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.
282
DDP
12.32 %
20.20 %
13.63 %
100.00 %
10 min
1 core @ 2.5 Ghz (Python)
283
DDP_out_ELAS_params2
12.32 %
20.20 %
13.63 %
100.00 %
10 min
1 core @ 2.5 Ghz (Python)
284
GCSF
code
11.64 %
27.11 %
14.21 %
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.
285
MT-TW-SMNet
15.47 %
16.25 %
15.60 %
100.00 %
0.4s
GPU @ 2.5 Ghz (Python)
M. El-Khamy, X. Du, H. Ren and J. Lee: Multi-Task Learning of Depth from Tele and Wide Stereo Image Pairs . Proceedings of the IEEE Conference on Image Processing 2019.
286
Mono-SF
14.21 %
26.94 %
16.32 %
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.
287
AbNet2
14.71 %
29.88 %
17.23 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
288
CostFilter
code
17.53 %
22.88 %
18.42 %
100.00 %
4 min
1 core @ 2.5 Ghz (Matlab)
C. Rhemann, A. Hosni, M. Bleyer, C. Rother and M. Gelautz: Fast Cost-Volume Filtering for Visual
Correspondence and Beyond . CVPR 2011.
289
MonoComb
17.89 %
21.16 %
18.44 %
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.
290
DWBSF
19.61 %
22.69 %
20.12 %
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.
291
monoResMatch
code
22.10 %
19.81 %
21.72 %
100.00 %
0.16 s
Titan X GPU
F. Tosi, F. Aleotti, M. Poggi and S. Mattoccia: Learning monocular depth estimation
infusing traditional stereo knowledge . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) 2019.
292
DDP_in_ELAS_params2
20.63 %
28.68 %
21.97 %
85.92 %
1 s
1 core @ 2.5 Ghz (Matlab + C/C++)
293
Self-Mono-SF-ft
code
20.72 %
29.41 %
22.16 %
100.00 %
0.09 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene
Flow Estimation . CVPR 2020.
294
Multi-Mono-SF-ft
21.60 %
28.22 %
22.71 %
100.00 %
0.06 s
NVIDIA GTX 1080 Ti
295
OCV-BM
code
24.29 %
30.13 %
25.27 %
58.54 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
G. Bradski: The OpenCV Library . Dr. Dobb's Journal of Software Tools 2000.
296
VSF
code
27.31 %
21.72 %
26.38 %
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.
297
YunYang-Monodepth
26.06 %
28.92 %
26.53 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (Python)
298
SED
code
25.01 %
40.43 %
27.58 %
4.02 %
0.68 s
1 core @ 2.0 Ghz (C/C++)
D. Pe\~{n}a and A. Sutherland: Disparity Estimation by Simultaneous Edge Drawing . Computer Vision -- ACCV 2016 Workshops: ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part II 2017.
299
Multi-Mono-SF
27.48 %
47.30 %
30.78 %
100.00 %
0.06 s
NVIDIA GTX 1080 Ti
300
mts1
code
28.03 %
46.55 %
31.11 %
2.52 %
0.18 s
4 cores @ 3.5 Ghz (C/C++)
R. Brandt, N. Strisciuglio, N. Petkov and M. Wilkinson: Efficient binocular stereo
correspondence matching with 1-D Max-Trees . Pattern Recognition Letters 2020.
301
Self-Mono-SF
code
31.22 %
48.04 %
34.02 %
100.00 %
0.09 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene
Flow Estimation . CVPR 2020.
302
MST
code
45.83 %
38.22 %
44.57 %
100.00 %
7 s
1 core @ 2.5 Ghz (Matlab + C/C++)
Q. Yang: A Non-Local Cost Aggregation Method
for Stereo Matching . CVPR 2012.