Method
Setting
Code
Out-Noc
Out-All
Avg-Noc
Avg-All
Density
Runtime
Environment
1
GANet+ADL
0.98 %
1.29 %
0.4 px
0.5 px
100.00 %
1.8 s
GPU @ 1.5 Ghz (Python)
2
DN + GANet
1.01 %
1.36 %
0.4 px
0.4 px
100.00 %
1.8 s
GPU @ 2.5 Ghz (C/C++)
3
CGF-ACV
1.03 %
1.34 %
0.4 px
0.5 px
100.00 %
0.24 s
NVIDIA RTX 3090 (PyTorch)
4
DiffuVolume
1.04 %
1.37 %
0.4 px
0.4 px
100.00 %
0.36 s
GPU @ 2.5 Ghz (Python)
5
RCA-Stereo
1.04 %
1.34 %
0.4 px
0.4 px
100.00 %
0.40 s
1 core @ 2.5 Ghz (Python)
6
PCWNet
code
1.04 %
1.37 %
0.4 px
0.5 px
100.00 %
0.44 s
1 core @ 2.5 Ghz (C/C++)
Z. Shen, Y. Dai, X. Song, Z. Rao, D. Zhou and L. Zhang: PCW-Net: Pyramid Combination and Warping
Cost Volume for Stereo Matching . European Conference on Computer
Vision(ECCV) 2022.
7
LaC+GANet
code
1.05 %
1.42 %
0.4 px
0.5 px
100.00 %
1.8 s
1 core @ 2.5 Ghz (C/C++)
B. Liu, H. Yu and Y. Long: Local Similarity Pattern and Cost Self-
Reassembling for Deep Stereo Matching Networks . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
8
GwcNet+ADL
1.05 %
1.42 %
0.4 px
0.5 px
100.00 %
0.32 s
1 core @ 2.5 Ghz (Python)
9
SCVFormer
1.05 %
1.39 %
0.4 px
0.5 px
100.00 %
0.09 s
NVIDIA RTX 3090 (PyTorch)
10
ICVP
1.06 %
1.39 %
0.4 px
0.5 px
100.00 %
0.17 s
GPU @ 1.5 Ghz (Python)
11
MOSStereo
1.06 %
1.36 %
0.4 px
0.4 px
100.00 %
~1s s
2 cores @ 2.5 Ghz (Python)
12
UDGNet
1.07 %
1.39 %
0.4 px
0.4 px
100.00 %
0.4 s
GPU @ 2.5 Ghz (Python)
13
gwcnet+L_norm
1.09 %
1.56 %
0.4 px
0.5 px
100.00 %
0.4 s
1 core @ 2.5 Ghz (Python)
14
HCRNet
1.09 %
1.42 %
0.4 px
0.4 px
100.00 %
0.19 s
GPU @ 2.5 Ghz (Python)
15
IEG-Net
1.09 %
1.48 %
0.4 px
0.5 px
100.00 %
0.40 s
1 core @ 2.5 Ghz (Python)
16
AAG
1.09 %
1.48 %
0.4 px
0.5 px
100.00 %
1.2 s
1 core @ 2.5 Ghz (C/C++)
17
ERNet
1.11 %
1.43 %
0.4 px
0.5 px
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
18
ASNet
1.11 %
1.47 %
0.4 px
0.5 px
100.00 %
0.17 s
GPU @ >3.5 Ghz (Python)
19
NLCA-Net v2
code
1.11 %
1.46 %
0.4 px
0.5 px
100.00 %
0.67 s
GPU @ >3.5 Ghz (Python)
Z. Rao, D. Yuchao, S. Zhelun and H. Renjie: Rethinking Training Strategy in
Stereo Matching . IEEE TRANSACTIONS ON NEURAL
NETWORKS AND LEARNING SYSTEMS .
20
Astar
1.11 %
1.46 %
0.4 px
0.5 px
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
21
BSDual-CNN
1.12 %
1.42 %
0.4 px
0.5 px
100.00 %
0.45 s
GPU @ 2.5 Ghz (Python)
22
IGEV-Stereo(32)
code
1.12 %
1.43 %
0.4 px
0.4 px
100.00 %
0.32 s
NVIDIA RTX 3090 (PyTorch)
G. Xu, X. Wang, X. Ding and X. Yang: Iterative Geometry Encoding Volume for
Stereo Matching . CVPR 2023.
23
UCFNet
1.12 %
1.49 %
0.4 px
0.5 px
100.00 %
0.21 s
1 core @ 2.5 Ghz (C/C++)
24
GRNet
1.12 %
1.48 %
0.4 px
0.5 px
100.00 %
0.19 s
GPU @ 2.5 Ghz (Python)
25
IGEV-Stereo
1.12 %
1.44 %
0.4 px
0.4 px
100.00 %
0.18 s
NVIDIA RTX 3090 (PyTorch)
G. Xu, X. Wang, X. Ding and X. Yang: Iterative Geometry Encoding Volume for
Stereo Matching . CVPR 2023.
26
LaC+GwcNet
code
1.13 %
1.49 %
0.5 px
0.5 px
100.00 %
0.65 s
GPU @ 2.5 Ghz (Python)
B. Liu, H. Yu and Y. Long: Local Similarity Pattern and Cost Self-
Reassembling for Deep Stereo Matching Networks . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
27
ACVNet
code
1.13 %
1.47 %
0.4 px
0.5 px
100.00 %
0.2 s
NVIDIA RTX 3090 (PyTorch)
G. Xu, J. Cheng, P. Guo and X. Yang: Attention Concatenation Volume for
Accurate and Efficient Stereo Matching . CVPR 2022.
28
LEAStereo
code
1.13 %
1.45 %
0.5 px
0.5 px
100.00 %
0.3 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.
29
PSMNet+ADL
1.14 %
1.50 %
0.4 px
0.5 px
100.00 %
0.41 s
GPU @ 1.5 Ghz (Python)
30
CREStereo
code
1.14 %
1.46 %
0.4 px
0.5 px
100.00 %
0.40 s
GPU @ >3.5 Ghz (C/C++)
J. Li, P. Wang, P. Xiong, T. Cai, Z. Yan, L. Yang, J. Liu, H. Fan and S. Liu: Practical Stereo Matching via
Cascaded Recurrent Network with Adaptive
Correlation . 2022.
31
LSE+CFNet
1.14 %
1.52 %
0.4 px
0.5 px
100.00 %
0.30 s
1 core @ 2.5 Ghz (Python)
32
AFNet
1.17 %
1.50 %
0.4 px
0.5 px
100.00 %
0.25 s
1 core @ 2.5 Ghz (Python)
33
CGF-Gwc
1.17 %
1.52 %
0.4 px
0.5 px
100.00 %
0.26 s
1 core @ 2.5 Ghz (C/C++)
34
AcfNet
code
1.17 %
1.54 %
0.5 px
0.5 px
100.00 %
0.48 s
1 core @ 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.
35
DMLMBCVNet
1.17 %
1.57 %
0.4 px
0.5 px
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
36
Abc-Net
1.18 %
1.59 %
0.4 px
0.5 px
100.00 %
0.72 s
4 cores @ 2.5 Ghz (Python)
X. Li, Y. Fan, G. Lv and H. Ma: Area-based correlation and non-local
attention network for stereo matching . The Visual Computer 2021.
37
CAL-Net
1.19 %
1.53 %
0.4 px
0.5 px
100.00 %
0.44 s
4 cores @ 2.5 Ghz (Python)
S. Chen, B. Li, W. Wang, H. Zhang, H. Li and Z. Wang: Cost Affinity Learning Network for
Stereo Matching . IEEE International Conference on
Acoustics, Speech and Signal Processing,
ICASSP 2021, Toronto, ON, Canada,
June 6-11, 2021 2021.
38
GANet-deep
code
1.19 %
1.60 %
0.4 px
0.5 px
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.
39
OptStereo
1.20 %
1.61 %
0.4 px
0.5 px
100.00 %
0.10 s
GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: PVStereo: Pyramid voting module for
end-to-end self-supervised stereo matching . IEEE Robotics and Automation Letters 2021.
40
GFGANet-v2
1.21 %
1.58 %
0.5 px
0.5 px
100.00 %
0.43 s
4 cores @ >3.5 Ghz (Python)
41
OB_GWC
1.21 %
1.58 %
0.4 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
42
RDNet
1.21 %
1.57 %
0.4 px
0.5 px
100.00 %
0.60 s
1 core @ 2.5 Ghz (C/C++)
43
CGF-PSM
1.21 %
1.57 %
0.5 px
0.5 px
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
44
NLCA-Net-3
code
1.21 %
1.60 %
0.4 px
0.5 px
100.00 %
0.44 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.
45
DSN
code
1.22 %
1.63 %
0.4 px
0.5 px
100.00 %
0.07 s
1 core @ 2.5 Ghz (Python)
46
URDAD
1.22 %
1.58 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
47
DCANet-fast
1.22 %
1.60 %
0.5 px
0.5 px
100.00 %
0.10s
GTX30900TI
48
CFNet
code
1.23 %
1.58 %
0.4 px
0.5 px
100.00 %
0.18 s
1 core @ 2.5 Ghz (Python)
Z. Shen, Y. Dai and Z. Rao: CFNet: Cascade and Fused Cost Volume for
Robust Stereo Matching . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2021.
49
PFSMNet
code
1.23 %
1.58 %
0.5 px
0.5 px
100.00 %
0.31 s
1 core @ 2.5 Ghz (C/C++)
K. Zeng, Y. Wang, Q. Zhu, J. Mao and H. Zhang: Deep Progressive Fusion Stereo Network . IEEE Transactions on Intelligent
Transportation Systems 2021.
50
DMLMBCV
1.24 %
1.61 %
0.4 px
0.5 px
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
51
FGDS-Net
1.24 %
1.62 %
0.4 px
0.5 px
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
52
NLCA-Net
code
1.25 %
1.62 %
0.4 px
0.5 px
100.00 %
0.6 s
GPU @ 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.
53
PASNet
1.25 %
1.69 %
0.4 px
0.5 px
100.00 %
0.38 s
GPU @ 3.5 Ghz (Python)
54
SCV-Stereo
code
1.27 %
1.68 %
0.5 px
0.5 px
100.00 %
0.08 s
GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan and M. Liu: SCV-Stereo: Learning stereo
matching from a sparse cost volume . 2021 IEEE International Conference
on Image Processing (ICIP) 2021.
55
SASNet
1.29 %
1.67 %
0.5 px
0.5 px
100.00 %
0.21 s
GPU @ >3.5 Ghz (Python)
56
SplitNet
1.29 %
1.60 %
0.5 px
0.5 px
100.00 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
57
RAFT-Stereo
code
1.30 %
1.66 %
0.4 px
0.5 px
100.00 %
0.38 s
1 core @ 2.5 Ghz (Python)
58
AGNet
1.30 %
1.68 %
0.4 px
0.5 px
100.00 %
0.40 s
1 core @ 2.5 Ghz (Python)
59
DPCTF-S
1.31 %
1.72 %
0.5 px
0.5 px
100.00 %
0.11 s
GPU @ 2.5 Ghz (Python)
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.
60
PCMAnet
code
1.31 %
1.87 %
0.5 px
0.5 px
100.00 %
0.27 s
1 core @ 2.5 Ghz (C/C++)
61
GFGANet
1.31 %
1.70 %
0.5 px
0.5 px
100.00 %
0.39 s
4 cores @ >3.5 Ghz (Python)
62
yjlgreen
1.31 %
1.70 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
63
RCGSNP
1.31 %
1.68 %
0.5 px
0.5 px
100.00 %
0.12 s
GPU @ 2.5 Ghz (Python)
64
AMNet
1.32 %
1.73 %
0.5 px
0.5 px
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.
65
GwcNet-gc
code
1.32 %
1.70 %
0.5 px
0.5 px
100.00 %
0.32 s
GPU @ 2.0 Ghz (Java + C/C++)
X. Guo, K. Yang, W. Yang, X. Wang and H. Li: Group-wise correlation stereo network . CVPR 2019.
66
OB_GWC
1.32 %
1.70 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
67
PGNet
1.32 %
1.79 %
0.5 px
0.5 px
100.00 %
0.7 s
1 core @ 2.5 Ghz (python)
S. Chen, Z. Xiang, C. Qiao, Y. Chen and T. Bai: PGNet: Panoptic parsing guided deep stereo
matching . Neurocomputing 2021.
68
High_U+A_coex
1.32 %
1.69 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
69
GAMNet
1.34 %
1.80 %
0.5 px
0.5 px
100.00 %
1 s
1 core @ 2.5 Ghz (Python)
70
High_U+A_coex
1.34 %
1.73 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
71
ADStereo
1.35 %
1.72 %
0.5 px
0.5 px
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
72
DFL-CCA-Net
1.35 %
1.73 %
0.5 px
0.5 px
100.00 %
0.46 s
Nvidia Titan Xp
73
CGF-F-B
1.35 %
1.73 %
0.5 px
0.5 px
100.00 %
0.26 s
GPU @ 2.5 Ghz (Python)
74
DSN
1.36 %
1.79 %
0.5 px
0.5 px
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
75
GCGANet-V1
1.36 %
1.75 %
0.5 px
0.5 px
100.00 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
76
GANet-15
code
1.36 %
1.80 %
0.5 px
0.5 px
100.00 %
0.36 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.
77
GDANet
1.36 %
1.78 %
0.5 px
0.5 px
100.00 %
0.04 s
1 core @ 2.5 Ghz (Python)
78
Cs-Net
1.36 %
1.78 %
0.5 px
0.5 px
100.00 %
0.6 s
GPU @ 2.5 Ghz (Python)
79
FAPEEM
1.37 %
1.75 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
80
taugr12
1.38 %
1.75 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
81
SGNet
1.38 %
1.85 %
0.5 px
0.5 px
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.
82
HD^3-Stereo
code
1.40 %
1.80 %
0.5 px
0.5 px
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.
83
taugr1215
1.41 %
1.77 %
0.5 px
0.5 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
84
HITNet
code
1.41 %
1.89 %
0.4 px
0.5 px
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
V. Tankovich, C. Häne, Y. Zhang, A. Kowdle, S. Fanello and S. Bouaziz: HITNet: Hierarchical Iterative Tile
Refinement Network for Real-time Stereo
Matching . CVPR 2021.
85
CGI-Stereo
1.41 %
1.76 %
0.5 px
0.5 px
100.00 %
0.02 s
NVIDIA RTX 3090 (PyTorch)
86
CFP-Net
code
1.41 %
1.83 %
0.5 px
0.5 px
100.00 %
0.95 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.
87
WSMCnet
code
1.42 %
1.90 %
0.6 px
0.6 px
100.00 %
0.39 s
GPU @ 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.
88
Lite-UAStereo
1.43 %
1.82 %
0.5 px
0.5 px
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
89
sCroCo_RVC
code
1.43 %
1.72 %
0.5 px
0.5 px
100.00 %
1.1 s
1 core @ 2.5 Ghz (Python)
90
pcanet
code
1.46 %
2.13 %
0.5 px
0.6 px
100.00 %
0.27 s
1 core @ 2.5 Ghz (C/C++)
91
EdgeStereo-V2
1.46 %
1.83 %
0.4 px
0.5 px
100.00 %
0.32 s
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.
92
MABNet_origin
code
1.47 %
1.89 %
0.5 px
0.5 px
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 . .
93
SSPCVNet
1.47 %
1.90 %
0.5 px
0.6 px
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.
94
W-Stereo-a-r
1.49 %
2.00 %
0.5 px
0.5 px
100.00 %
0.07 s
1 core @ 2.5 Ghz (Python)
95
PSMNet
code
1.49 %
1.89 %
0.5 px
0.6 px
100.00 %
0.41 s
Nvidia Titan Xp
J. Chang and Y. Chen: Pyramid Stereo Matching Network . arXiv preprint arXiv:1803.08669 2018.
96
Ct-Net
1.52 %
1.97 %
0.6 px
0.6 px
100.00 %
0.45 s
GPU @ 2.5 Ghz (Python)
97
HSM
code
1.53 %
1.99 %
0.5 px
0.6 px
100.00 %
0.15 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.
98
EBNet
1.53 %
1.97 %
0.5 px
0.6 px
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
99
CGFNet
code
1.55 %
1.93 %
0.5 px
0.5 px
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
100
AANet+
code
1.55 %
2.04 %
0.4 px
0.5 px
100.00 %
0.06 s
NVIDIA V100 GPU
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network
for Efficient Stereo Matching . CVPR 2020.
101
CoEx
code
1.55 %
1.93 %
0.5 px
0.5 px
100.00 %
0.027 s
RTX 2080Ti (Python)
A. Bangunharcana, J. Cho, S. Lee, I. Kweon, K. Kim and S. Kim: Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation . 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
102
CGNet
code
1.59 %
1.98 %
0.5 px
0.5 px
100.00 %
0.03 s
GPU @ >3.5 Ghz (Python)
103
OA_COEX
1.60 %
2.03 %
0.5 px
0.6 px
100.00 %
0.35 s
1 core @ 2.5 Ghz (C/C++)
104
BGNet+
1.62 %
2.03 %
0.5 px
0.6 px
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
B. Xu, Y. Xu, X. Yang, W. Jia and Y. Guo: Bilateral Grid Learning for Stereo Matching
Network . Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR) 2021.
105
MSDC-Net
1.63 %
2.09 %
0.5 px
0.6 px
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: MSDC-Net: Multi-Scale Dense and
Contextual Networks for Stereo Matching . 2019 Asia-Pacific Signal and
Information Processing Association Annual Summit
and Conference (APSIPA ASC) 2019.
106
WaveletStereo
1.66 %
2.18 %
0.5 px
0.6 px
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.
107
SegStereo
code
1.68 %
2.03 %
0.5 px
0.6 px
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.
108
AutoDispNet-CSS
code
1.70 %
2.05 %
0.5 px
0.5 px
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.
109
iResNet-i2
code
1.71 %
2.16 %
0.5 px
0.6 px
100.00 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
Z. Liang, Y. Feng, Y. Guo, H. Liu, W. Chen, L. Qiao, L. Zhou and J. Zhang: Learning for disparity estimation through feature constancy . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018.
110
GAANet
1.73 %
2.22 %
0.5 px
0.6 px
100.00 %
0.08
2080tiGPU @ 2.5 Ghz (Python)
111
GC-NET
1.77 %
2.30 %
0.6 px
0.7 px
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.
112
ERSCNet
1.80 %
2.30 %
0.5 px
0.6 px
100.00 %
0.28 s
GPU @ 2.5 Ghz (Python)
Anonymous: ERSCNet . Proceedings of the European
Conference on Computer Vision (ECCV) 2020.
113
EASNet
1.89 %
2.41 %
0.6 px
0.6 px
100.00 %
0.1 s
GPU @ 1.5 Ghz (Python)
114
AANet
code
1.91 %
2.42 %
0.5 px
0.6 px
100.00 %
0.06 s
GPU @ 2.5 Ghz (Python)
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network
for Efficient Stereo Matching . CVPR 2020.
115
PDSNet
1.92 %
2.53 %
0.9 px
1.0 px
100.00 %
0.5 s
1 core @ 2.5 Ghz (Python + C/C++)
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.
116
EASNet-M
1.96 %
2.47 %
0.6 px
0.6 px
100.00 %
0.8 s
GPU @ 1.5 Ghz (Python)
117
PVStereo
1.98 %
2.47 %
0.7 px
0.8 px
100.00 %
0.10 s
1 core @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: PVStereo: Pyramid voting module
for end-to-end self-supervised stereo matching . IEEE Robotics and Automation
Letters 2021.
118
ED-Net
1.98 %
2.52 %
0.7 px
0.7 px
100.00 %
0.24 s
1 core @ 2.5 Ghz (C/C++)
119
TSNnet_Teacher
2.00 %
2.50 %
0.6 px
0.7 px
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
120
TSNnet_student
2.01 %
2.63 %
0.6 px
0.8 px
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
121
FADNet
code
2.04 %
2.46 %
0.5 px
0.6 px
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.
122
MMStereo
2.04 %
2.52 %
0.6 px
0.7 px
100.00 %
0.04 s
Nvidia Titan RTX (Python)
K. Shankar, M. Tjersland, J. Ma, K. Stone and M. Bajracharya: A Learned Stereo Depth System for
Robotic Manipulation in Homes . .
123
TSNnet_naive
2.15 %
2.76 %
0.6 px
0.7 px
100.00 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
124
OB_COEX
2.18 %
2.71 %
0.6 px
0.7 px
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
125
RecResNet
code
2.21 %
2.94 %
0.6 px
0.7 px
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.
126
L-ResMatch
code
2.27 %
3.40 %
0.7 px
1.0 px
100.00 %
48 s
Titan X (Torch7, CUDA)
A. Shaked and L. Wolf: Improved Stereo Matching with Constant Highway
Networks and Reflective Loss . arXiv preprint arxiv:1701.00165 2016.
127
CNNF+SGM
2.28 %
3.48 %
0.7 px
0.9 px
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.
128
stroco
code
2.28 %
2.67 %
0.6 px
0.7 px
100.00 %
1.1 s
1 core @ 2.5 Ghz (Python)
129
SGM-Net
2.29 %
3.50 %
0.7 px
0.9 px
100.00 %
67 s
Titan X
A. Seki and M. Pollefeys: SGM-Nets: Semi-Global Matching With Neural
Networks . CVPR 2017.
130
SsSMnet
2.30 %
3.00 %
0.7 px
0.8 px
100.00 %
0.8 s
Titan Xp
Y. Zhong, Y. Dai and H. Li: Self-Supervised Learning for Stereo
Matching with Self-Improving Ability . arXiv:1709.00930 2017.
131
PBCP
2.36 %
3.45 %
0.7 px
0.9 px
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.
132
Displets v2
code
2.37 %
3.09 %
0.7 px
0.8 px
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.
133
RTSnet
code
2.43 %
2.90 %
0.7 px
0.7 px
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
H. Lee and Y. Shin: Real-Time Stereo Matching Network with High
Accuracy . 2019 IEEE International Conference on Image
Processing (ICIP) 2019.
134
MC-CNN-acrt
code
2.43 %
3.63 %
0.7 px
0.9 px
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 .
135
DSN
code
2.46 %
3.19 %
0.8 px
0.9 px
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
136
cfusion
code
2.46 %
2.69 %
0.8 px
0.8 px
99.93 %
70 s
GPU (Matlab + CUDA)
V. Ntouskos and F. Pirri: Confidence driven TGV fusion . arXiv preprint arXiv:1603.09302 2016.
137
Displets
code
2.47 %
3.27 %
0.7 px
0.9 px
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.
138
EASNet-S
2.57 %
3.19 %
0.7 px
0.7 px
100.00 %
0.6 s
GPU @ 1.5 Ghz (Python)
139
MC-CNN
2.61 %
3.84 %
0.8 px
1.0 px
100.00 %
100 s
Nvidia GTX Titan (CUDA, Lua/Torch7)
J. Zbontar and Y. LeCun: Computing the Stereo Matching Cost with a
Convolutional Neural Network . Conference on Computer Vision and
Pattern Recognition (CVPR) 2015.
140
Fast DS-CS
code
2.61 %
3.20 %
0.7 px
0.8 px
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).
141
MABNet_tiny
code
2.71 %
3.31 %
0.7 px
0.8 px
100.00 %
0.11 s
1 core @ 2.5 Ghz (Python)
J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network
Based on Multibranch Adjustable Bottleneck
Module . .
142
PRSM
code
2.78 %
3.00 %
0.7 px
0.7 px
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.
143
SPS-StFl
2.83 %
3.64 %
0.8 px
0.9 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.
144
MC-CNN-WS
code
3.02 %
4.45 %
0.8 px
1.0 px
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.
145
VC-SF
3.05 %
3.31 %
0.8 px
0.8 px
100.00 %
300 s
1 core @ 2.5 Ghz (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.
146
Content-CNN
3.07 %
4.29 %
0.8 px
1.0 px
100.00 %
0.7 s
Nvidia GTX Titan X (Torch)
W. Luo, A. Schwing and R. Urtasun: Efficient Deep Learning for Stereo Matching . CVPR 2016.
147
Deep Embed
3.10 %
4.24 %
0.9 px
1.1 px
100.00 %
3 s
1 core @ 2.5 Ghz (C/C++)
Z. Chen, X. Sun, Y. Yu, L. Wang and C. Huang: A Deep Visual Correspondence
Embedding Model for Stereo Matching Costs . ICCV 2015.
148
JSOSM
3.15 %
3.94 %
0.8 px
0.9 px
100.00 %
105 s
8 cores @ 2.5 Ghz (C/C++)
X. Li and J. Liu: EFFICIENT STEREO MATCHING USING SEGMENT
OPTIMIZATION . ICIP 2016.
149
FD-Fusion
code
3.16 %
3.85 %
0.7 px
0.8 px
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.
150
OSF
code
3.28 %
4.07 %
0.8 px
0.9 px
99.98 %
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.
151
CoR
code
3.30 %
4.10 %
0.8 px
0.9 px
100.00 %
6 s
6 cores @ 3.3 Ghz (Matlab + C/C++)
A. Chakrabarti, Y. Xiong, S. Gortler and T. Zickler: Low-level Vision by Consensus in a Spatial
Hierarchy of Regions . CVPR 2015.
152
TCD-CRF
3.32 %
5.24 %
0.9 px
1.9 px
100.00 %
60 s
4 cores @ 3.5 Ghz (C/C++)
S. Arjomand Bigdeli, G. Budweiser and M. Zwicker: Temporally Coherent Disparity Maps Using CRFs with Fast 4D Filtering . Proc. ACPR 2015.
153
CKDNet_naïve
3.39 %
4.22 %
0.8 px
0.9 px
100.00 %
0.01 s
AGX @ 32TOPs python
154
SPS-St
code
3.39 %
4.41 %
0.9 px
1.0 px
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.
155
PCBP-SS
3.40 %
4.72 %
0.8 px
1.0 px
100.00 %
5 min
4 cores @ 2.5 Ghz (Matlab + C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation . CVPR 2013.
156
CBMV
code
3.56 %
4.73 %
0.9 px
1.1 px
100.00 %
250 s
6 cores@3.0Ghz(Python,C/C++,CUDA TitanX)
K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching
Volume for Disparity Estimation . 2018.
157
SepStereo
3.69 %
4.50 %
0.8 px
1.0 px
100.00 %
0.088 s
GPU @ 2.0 Ghz (Pyhton)
158
DDS-SS
3.83 %
4.59 %
0.9 px
1.0 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.
159
StereoSLIC
3.92 %
5.11 %
0.9 px
1.0 px
99.89 %
2.3 s
1 core @ 3.0 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation . CVPR 2013.
160
SMCM
3.94 %
5.24 %
0.9 px
1.1 px
100.00 %
1800 s
Nvidia GTX 1080 (Caffe)
M. Yang, Y. Liu, Y. Cai and Z. You: Stereo matching based on classification of
materials . Neurocomputing 2016.
161
PR-Sf+E
4.02 %
4.87 %
0.9 px
1.0 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.
162
PCBP
4.04 %
5.37 %
0.9 px
1.1 px
100.00 %
5 min
4 cores @ 2.5 Ghz (Matlab + C/C++)
K. Yamaguchi, T. Hazan, D. McAllester and R. Urtasun: Continuous Markov Random Fields for Robust Stereo
Estimation . ECCV 2012.
163
DispNetC
code
4.11 %
4.65 %
0.9 px
1.0 px
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.
164
CSPMS
4.13 %
5.92 %
1.2 px
1.6 px
100.00 %
6 s
4 cores @ 2.5 Ghz (C/C++)
J. Cho and M. Humenberger: Fast PatchMatch Stereo
Matching Using Multi-Scale Cost Fusion for
Automotive Applications . IV 2015.
165
SGM-post
4.27 %
5.33 %
1.0 px
1.1 px
100.00 %
5 s
4 cores @ 2.5 Ghz (C/C++)
Z. Zhong: Efficient Learning based Semi-Global Stereo
Matching . 2015 submitted.
166
MBM
4.35 %
5.43 %
1.0 px
1.1 px
100.00 %
0.2 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: A Multi-Block-Matching Approach for Stereo . IV 2015.
167
PR-Sceneflow
4.36 %
5.22 %
0.9 px
1.1 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.
168
CRD-Fusion
code
4.38 %
5.40 %
0.9 px
1.1 px
100.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
X. Fan, S. Jeon and B. Fidan: Occlusion-Aware Self-Supervised Stereo
Matching with Confidence Guided Raw Disparity
Fusion . Conference on Robots and Vision 2022.
169
CoR-Conf
code
4.49 %
5.26 %
1.0 px
1.2 px
96.37 %
6 s
6 cores @ 3.3 Ghz (Matlab + C/C++)
A. Chakrabarti, Y. Xiong, S. Gortler and T. Zickler: Low-level Vision by Consensus in a Spatial
Hierarchy of Regions . CVPR 2015.
170
Flow2Stereo
4.58 %
5.11 %
1.0 px
1.1 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.
171
DispSegNet
4.68 %
5.66 %
0.9 px
1.0 px
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.
172
pSGM
4.68 %
6.13 %
1.0 px
1.4 px
100.00 %
7.92 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.
173
AARBM
4.86 %
5.94 %
1.0 px
1.2 px
100.00 %
0.25 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption . IV 2014.
174
wSGM
4.97 %
6.18 %
1.3 px
1.6 px
97.03 %
6s
1 core @ 3.5 Ghz (C/C++)
R. Spangenberg, T. Langner and R. Rojas: Weighted Semi-Global Matching and Center-Symmetric Census Transform for Robust Driver Assistance . CAIP 2013.
175
AABM
4.97 %
6.04 %
1.0 px
1.2 px
100.00 %
0.12 s
1 core @ 3.1 Ghz (C/C++)
N. Einecke and J. Eggert: Stereo Image Warping for Improved Depth Estimation of Road Surfaces . IV 2013.
176
ATGV
5.02 %
6.88 %
1.0 px
1.6 px
100.00 %
6 min
>8 cores @ 3.0 Ghz (Matlab + C/C++)
R. Ranftl, T. Pock and H. Bischof: Minimizing TGV-based Variational Models with Non-Convex Data terms . ICSSVM 2013.
177
rSGM
code
5.03 %
6.60 %
1.1 px
1.5 px
97.22 %
0.2 s
4 cores @ 2.6 Ghz (C/C++)
R. Spangenberg, T. Langner, S. Adfeldt and R. Rojas: Large Scale Semi-Global Matching on the CPU . IV 2014.
178
iSGM
5.11 %
7.15 %
1.2 px
2.1 px
94.70 %
8 s
2 cores @ 2.5 Ghz (C/C++)
S. Hermann and R. Klette: Iterative Semi-Global Matching for Robust Driver
Assistance Systems . ACCV 2012.
179
RBM
5.18 %
6.21 %
1.1 px
1.3 px
100.00 %
0.2 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption . IV 2014.
180
ARW
code
5.20 %
6.87 %
1.2 px
1.5 px
99.33 %
4.6s
1 core @ 3.5 Ghz (MATLAB+C/C++)
S. Lee, J. Lee, J. Lim and I. Suh: Robust Stereo Matching using Adaptive Random
Walk with Restart Algorithm . Image and vision computing (accepted) 2015.
181
DLP
5.28 %
7.21 %
1.2 px
2.0 px
100.00 %
60 s
8 cores @ >3.5 Ghz (C/C++)
V. Nguyen, H. Nguyen and J. Jeon: Robust Stereo Data Cost With a Learning
Strategy . IEEE Transactions on Intelligent
Transportation Systems 2017.
182
Ensemble
5.34 %
6.91 %
1.5 px
2.0 px
100.00 %
135 s
2 cores @ >3.5 Ghz (Matlab)
A. Spyropoulos and P. Mordohai: Ensemble Classifier for Combining Stereo
Matching Algorithms . International Conference on 3D Vision
(3DV) 2015.
183
ALTGV
5.36 %
6.49 %
1.1 px
1.2 px
100.00 %
20 s
GPU @ 2.5 Ghz (C/C++)
G. Kuschk and D. Cremers: Fast and Accurate Large-scale Stereo Reconstruction using Variational Methods . ICCV Workshop on Big Data in 3D Computer Vision 2013.
184
SNCC
5.40 %
6.44 %
1.2 px
1.3 px
100.00 %
0.11 s
1 core @ 3.1 Ghz (C/C++)
N. Einecke and J. Eggert: A Two-Stage Correlation Method for Stereoscopic Depth Estimation . DICTA 2010.
185
CAT
5.45 %
6.54 %
1.1 px
1.2 px
100.00 %
10 s
1 core @ 3.5 Ghz (C/C++)
J. Ha, J. Jeon, G. Bae, S. Jo and H. Jeong: Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table Scheme for Dense Stereo Correspondence . Advances in Visual Computing 2014.
186
SGM
5.76 %
7.00 %
1.2 px
1.3 px
85.80 %
3.7 s
1 core @ 3.0 Ghz (C/C++)
H. Hirschmueller: Stereo Processing by Semi-Global Matching and Mutual Information . PAMI 2008.
187
mSGM-LDE
6.01 %
8.22 %
1.4 px
2.4 px
100.00 %
55 s
2 cores @ 2.5 Ghz (C/C++)
V. Nguyen, D. Nguyen, S. Lee and J. Jeon: Local Density Encoding for Robust Stereo
Matching . TCSVT 2014.
188
AAFS
6.10 %
6.94 %
1.2 px
1.3 px
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.
189
Toast2
6.16 %
7.42 %
1.2 px
1.4 px
95.39 %
0.03 s
4 cores @ 3.5 Ghz (C/C++)
B. Ranft and T. Strau\ss: Modeling Arbitrarily Oriented Slanted
Planes for Efficient Stereo Vision based on Block
Matching . Intelligent Transportation Systems
(ITSC), 2014 IEEE 17th International Conference
on 2014.
190
ITGV
6.20 %
7.30 %
1.3 px
1.5 px
100.00 %
7 s
1 core @ 3.0 Ghz (Matlab + C/C++)
R. Ranftl, S. Gehrig, T. Pock and H. Bischof: Pushing the Limits of Stereo Using Variational Stereo Estimation . IV 2012.
191
OASM-Net
6.39 %
8.60 %
1.3 px
2.0 px
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.
192
Permutation Stereo
7.39 %
8.48 %
1.6 px
1.8 px
99.93 %
30 s
GPU @ 2.5 Ghz (Matlab)
P. Brousseau and S. Roy: A Permutation Model for the Self-
Supervised Stereo Matching Problem . 2022 19th Conference on Robots and
Vision (CRV) 2022.
193
OCV-SGBM
code
7.64 %
9.13 %
1.8 px
2.0 px
86.50 %
1.1 s
1 core @ 2.5 Ghz (C/C++)
H. Hirschmueller: Stereo processing by semiglobal matching
and mutual information . PAMI 2008.
194
SSMW
7.83 %
8.95 %
1.6 px
1.8 px
99.99 %
2.5 min
8 cores @ 2.5 Ghz (C/C++)
X. Li, J. Liu, G. Chen and H. Fu: Efficient Methods Using Slanted
Support Windows for Slanted Surfaces . IET Computer Vision,
http://ietdl.org/t/5QsTxb 2016.
195
MSMW
code
8.01 %
9.24 %
1.6 px
1.7 px
72.39 %
3 min
4 cores @ 2.5 Ghz (C/C++)
A. Buades and G. Facciolo: On the performance of local methods for stereovision . 2013 submitted.
196
HSMA
8.15 %
10.33 %
1.9 px
2.9 px
100.00 %
44s
1 core @ 3.0 Ghz (C/C++)
O. Zeglazi, M. Rziza, A. Amine and C. Demonceaux: A hierarchical stereo matching
algorithm
based on adaptive support region aggregation
method . Pattern Recognition Letters 2018.
197
ELAS
code
8.24 %
9.96 %
1.4 px
1.6 px
94.55 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching . ACCV 2010.
198
linBP
8.56 %
10.70 %
1.7 px
2.7 px
99.89 %
1.6 min
1 core @ 3.0 Ghz (C/C++)
W. Khan, V. Suaste, D. Caudillo and R. Klette: Belief Propagation Stereo Matching
Compared to iSGM on Binocular or Trinocular Video
Data . IV 2013.
199
ADSM
8.71 %
10.05 %
2.1 px
2.7 px
100.00 %
125 s
1 core @ 2.0 Ghz (C/C++)
O. Zeglazi, M. Rziza, A. Amine and C. Demonceaux: Accurate dense stereo matching
for road scenes . 2017 IEEE International
Conference on Image Processing, ICIP 2017,
Beijing, China, September 17-20,
2017 .
200
Deep-Raw
8.93 %
11.07 %
3.9 px
4.9 px
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
Z. Chen, X. Sun, Y. Yu, L. Wang and C. Huang: A Deep Visual Correspondence
Embedding Model for Stereo Matching Costs . ICCV 2015.
201
S+GF (Cen)
code
9.03 %
11.21 %
2.1 px
3.4 px
100.00 %
140 s
1 core @ 3.0 Ghz (C/C++)
K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan and Q. Tian: Cross-Scale Cost Aggregation
for Stereo Matching . CVPR 2014.
202
CrossCensus
9.46 %
10.86 %
2.3 px
2.7 px
100.00 %
30 s
1 core @ 2.5 Ghz (C/C++)
K. Zhang, J. Lu and G. Lafruit: Cross-Based Local Stereo Matching Using
Orthogonal Integral Images . Circuits and Systems for Video
Technology, IEEE Transactions on 2009.
203
SymST-GP
9.79 %
11.66 %
2.5 px
3.3 px
100.00 %
0.254 s
Dual - Nvidia GTX Titan (CUDA)
R. Ralha, G. Falcao, J. Amaro, V. Mota, M. Antunes, J. Barreto and U. Nunes: Parallel refinement of slanted 3D
reconstruction using dense stereo induced from
symmetry . Journal of Real-Time Image
Processing 2016.
204
SM_GPTM
9.79 %
11.38 %
2.1 px
2.6 px
100.00 %
6.5 s
2 cores @ 2.5 Ghz (C/C++)
C. Cigla and A. Alatan: An Improved Stereo Matching Algorithm with Ground Plane
and Temporal Smoothness Constraints . ECCV Workshops 2012.
205
LAMC-DSΜ
9.82 %
11.49 %
2.1 px
2.7 px
99.96 %
10.8 min
2 cores @ 2.5 Ghz (Matlab)
C. Stentoumis, L. Grammatikopoulos, I. Kalisperakis, E. Petsa and G. Karras: A local adaptive approach for dense stereo matching in architectural scene reconstruction . ISPRS 2013.
206
IIW
10.78 %
12.62 %
3.3 px
4.3 px
70.85 %
5.5 s
1 core @ 2.5 Ghz (C/C++)
A. Murarka and N. Einecke: A meta-technique for increasing density of local stereo methods through iterative interpolation and warping . Canadian Conference on Computer and Robot Vision 2014.
207
SDM
code
10.95 %
12.14 %
2.0 px
2.3 px
63.58 %
1 min
1 core @ 2.5 Ghz (C/C++)
J. Kostkova: Stratified dense matching for stereopsis
in complex scenes . BMVC 2003.
208
HLSC_mesh
11.22 %
12.82 %
2.3 px
2.9 px
100.00 %
800 s
1 core @ 2.5 Ghz (Matlab + C/C++)
S. Hadfield, K. Lebeda and R. Bowden: Stereo reconstruction using top-down
cues . Computer Vision and Image
Understanding 2016.
209
GF (Census)
code
11.65 %
13.76 %
4.5 px
5.6 px
100.00 %
120 s
1 core @ 3.0 Ghz (C/C++)
A. Hosni, C. Rhemann, M. Bleyer, C. Rother and M. Gelautz: Fast Cost-Volume Filtering
for Visual Correspondence and Beyond . TPAMI 2013. K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan and Q. Tian: Cross-Scale Cost Aggregation
for Stereo Matching . CVPR 2014.
210
BSM
code
11.74 %
13.44 %
2.2 px
2.8 px
97.02 %
2.5 min
1 core @ 3.0 Ghz (C/C++)
K. Zhang, J. Li, Y. Li, W. Hu, L. Sun and S. Yang: Binary stereo matching . Pattern Recognition (ICPR), 2012 21st
International Conference on 2012.
211
GCSF
code
12.05 %
13.24 %
1.9 px
2.1 px
60.77 %
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.
212
OCV-BM-post
code
12.28 %
13.76 %
2.1 px
2.3 px
47.11 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
G. Bradski: The OpenCV Library . Dr. Dobb's Journal of Software Tools 2000.
213
GCS
code
13.38 %
14.54 %
2.1 px
2.3 px
51.06 %
2.2 s
1 core @ 2.5 Ghz (C/C++)
J. Cech and R. Sara: Efficient Sampling of Disparity Space
for Fast And Accurate Matching . BenCOS 2007.
214
GLDS
code
17.22 %
18.63 %
2.8 px
3.2 px
100.00 %
26 s
GPU @ 1.5 Ghz (C/C++)
K. Oguri and Y. Shibata: A new stereo formulation not using pixel and disparity
models . 2018.
215
CostFilter
code
19.99 %
21.08 %
5.0 px
5.4 px
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.
216
GC+occ
code
33.49 %
34.73 %
8.6 px
9.2 px
87.57 %
6 min
1 core @ 2.5 Ghz (C/C++)
V. Kolmogorov and R. Zabih: Computing Visual Correspondence with
Occlusions using Graph Cuts . ICCV 2001.
217
VariableCros
34.84 %
36.11 %
12.4 px
12.9 px
95.66 %
30 s
1 core @ 2.5 Ghz (Matlab)
K. Zhang, J. Lu and G. Lafruit: Cross-Based Local Stereo Matching Using
Orthogonal Integral Images . Circuits and Systems for Video
Technology,
IEEE Transactions on 2009.
218
ALE-Stereo
code
50.48 %
51.19 %
13.0 px
13.5 px
100.00 %
50 min
1 core @ 3.0 Ghz (C/C++)
L. Ladicky, P. Sturgess, C. Russell, S. Sengupta, Y. Bastanlar, W. Clocksin and P. Torr: Joint Optimisation for Object Class
Segmentation and Dense Stereo Reconstruction . BMVC 2010.
219
MEDIAN
52.61 %
53.67 %
7.7 px
8.2 px
99.95 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
220
AVERAGE
61.62 %
62.49 %
8.0 px
8.6 px
99.95 %
0.01 s
1 core @ 2.5 Ghz (C/C++)