Method
Setting
Code
D1-bg
D1-fg
D1-all Density
Runtime
Environment
1
Wavelet-MonSter 1.14 %
2.60 %
1.38 % 100.00 %
0.58 s
1 core @ 2.5 Ghz (Python)
2
OmniDepth 1.13 % 2.73 %
1.40 %
100.00 %
0.17 s
Pytorch@NVIDIA RTX 3090
3
MonSter code 1.13 %
2.81 %
1.41 %
100.00 %
0.45 s
1 core @ 2.5 Ghz (Python)
J. Cheng, L. Liu, G. Xu, Z. Cai and X. Yang: MonSter: Marry Monodepth to Stereo
Unleashes Power . CVPR 2025 Highlight.
4
DEFOM-Stereo code 1.25 %
2.23 % 1.41 %
100.00 %
0.30s
1 core @ 2.5 Ghz (Python)
H. Jiang, Z. Lou, L. Ding, R. Xu, M. Tan, W. Jiang and R. Huang: DEFOM-Stereo: Depth Foundation Model Based
Stereo Matching . arXiv:2501.09466 2025.
5
Argus 1.22 %
2.38 %
1.42 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
6
MGS-Selective 1.13 %
2.88 %
1.42 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
7
SLiDC-Stereo 1.16 %
2.74 %
1.42 %
100.00 %
0.29 s
GPU @ 2.5 Ghz (Python)
8
IGEV++ (DepthAny.) code 1.15 %
2.80 %
1.43 %
100.00 %
0.48 s
NVIDIA RTX 3090 (PyTorch)
G. Xu, X. Wang, Z. Zhang, J. Cheng, C. Liao and X. Yang: IGEV++: Iterative Multi-range Geometry
Encoding Volumes for Stereo Matching . IEEE TPAMI 2025.
9
StereoBase code 1.28 %
2.26 %
1.44 %
100.00 %
0.29 s
GPU @ 1.5 Ghz (Python)
X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for
Stereo Matching and Strong Baseline . arXiv preprint arXiv:2312.00343 2023.
10
NMRF-Stereo-SwinT code 1.20 %
2.67 %
1.45 %
100.00 %
0.11 s
NVIDIA RTX 3090 (PyTorch)
11
SGD-Stereo 1.23 %
2.55 %
1.45 %
100.00 %
0.45 s
1 core @ 2.5 Ghz (C/C++)
12
TC-Stereo code 1.29 %
2.33 %
1.46 %
100.00 %
0.09 s
NVIDIA RTX 3090 (Pytorch)
J. Zeng, C. Yao, Y. Wu and Y. Jia: Temporally Consistent Stereo Matching . European conference on computer
vision 2024.
13
PointerStereo 1.31 %
2.25 %
1.46 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
14
DS-Stereo 1.23 %
2.64 %
1.47 %
100.00 %
0.35 s
1 core @ 2.5 Ghz (Python)
15
UniTT-Stereo 1.27 %
2.45 %
1.47 %
100.00 %
0.46 s
1 core @ 2.5 Ghz (Python)
16
IMC-Stereo 1.33 %
2.26 %
1.48 %
100.00 %
0.48 s
1 core @ 2.5 Ghz (C/C++)
17
Depthstereo 1.22 %
2.79 %
1.48 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
18
FFLO-Net 1.29 %
2.52 %
1.49 %
100.00 %
0.37 s
NVIDIA RTX 3090 (PyTorch)
19
MoCha-V2 Beta code 1.27 %
2.62 %
1.49 %
100.00 %
0.28 s
NVIDIA Tesla A30 (PyTorch)
Z. Chen, Y. Zhang, W. Li, B. Wang, Y. Zhao and C. Chen: Motif Channel Opened in a White-Box:
Stereo Matching via Motif Correlation Graph . arXiv preprint arXiv:2411.12426 2024. MoCha-Stereo: Motif Channel Attention
Network for Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition 2024.
20
GREAT-Selective 1.27 %
2.62 %
1.49 %
100.00 %
0.43 s
NVIDIA RTX 3090 (PyTorch)
21
GREAT-IGEV 1.28 %
2.59 %
1.50 %
100.00 %
0.33 s
NVIDIA RTX 3090 (PyTorch)
22
ST Selective-IGEV 1.30 %
2.48 %
1.50 %
100.00 %
0.24 s
GPU @ 2.5 Ghz (Python)
23
Reg-Stereo 1.30 %
2.54 %
1.50 %
100.00 %
0.37 s
1 core @ 2.5 Ghz (C/C++)
24
ViTAStereo code 1.21 %
2.99 %
1.50 %
100.00 %
0.22 s
NVIDIA RTX 4090 (PyTorch)
C. Liu, Q. Chen and R. Fan: Playing to Vision Foundation Model's
Strengths in Stereo Matching . IEEE Transactions on Intelligent
Vehicles 2024.
25
UGIA-Selective 1.30 %
2.57 %
1.51 %
100.00 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
26
GIP-Stereo 1.27 %
2.73 %
1.51 %
100.00 %
0.39 s
1 core @ 2.5 Ghz (C/C++)
27
IGEV++ code 1.31 %
2.54 %
1.51 %
100.00 %
0.28 s
NVIDIA RTX 3090 (PyTorch)
G. Xu, X. Wang, Z. Zhang, J. Cheng, C. Liao and X. Yang: IGEV++: Iterative Multi-range Geometry
Encoding Volumes for Stereo Matching . IEEE TPAMI 2025.
28
WCG-NET 1.35 %
2.37 %
1.52 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (Python)
29
MoCha-V2 Alpha code 1.35 %
2.40 %
1.52 %
100.00 %
0.33 s
NVIDIA Tesla A100 (Pytorch)
Z. Chen, Y. Zhang, W. Li, B. Wang, Y. Zhao and C. Chen: Motif Channel Opened in a White-Box:
Stereo Matching via Motif Correlation Graph . arXiv preprint arXiv:2411.12426 2024. MoCha-Stereo: Motif Channel Attention
Network for Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition 2024.
30
PANet 1.32 %
2.58 %
1.53 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
31
MoCha-Stereo code 1.36 %
2.43 %
1.53 %
100.00 %
0.34 s
NVIDIA Tesla A6000 (PyTorch)
Z. Chen, W. Long, H. Yao, Y. Zhang, B. Wang, Y. Qin and J. Wu: MoCha-Stereo: Motif Channel Attention
Network for Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2024.
32
MR_Igev 1.35 %
2.49 %
1.54 %
100.00 %
0.5 s
A800
33
IAFR-Stereo 1.34 %
2.57 %
1.54 %
100.00 %
0.20 s
1 core @ 2.5 Ghz (C/C++)
34
AIO-Stereo 1.34 %
2.57 %
1.54 %
100.00 %
0.23 s
GPU @ 2.5 Ghz (Python)
J. Zhou, H. Zhang, J. Yuan, P. Ye, T. Chen, H. Jiang, M. Chen and Y. Zhang: All-in-One: Transferring Vision
Foundation Models into Stereo Matching . arXiv preprint arXiv:2412.09912 2024.
35
DiffuVolume 1.35 %
2.51 %
1.54 %
100.00 %
0.36 s
GPU @ 2.5 Ghz (Python)
D. Zheng, X. Wu, Z. Liu, J. Meng and W. Zheng: DiffuVolume: Diffusion Model for Volume
based Stereo Matching . arXiv preprint arXiv:2308.15989 2023.
36
GANet+ADL code 1.38 %
2.38 %
1.55 %
100.00 %
0.67s
NVIDIA RTX 3090 (PyTorch)
P. Xu, Z. Xiang, C. Qiao, J. Fu and T. Pu: Adaptive Multi-Modal Cross-Entropy Loss for
Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2024.
37
Selective-IGEV code 1.33 %
2.61 %
1.55 %
100.00 %
0.24 s
1 core @ 2.5 Ghz (Python)
X. Wang, G. Xu, H. Jia and X. Yang: Selective-Stereo: Adaptive Frequency
Information Selection for Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2024.
38
MIF-Stereo 1.36 %
2.51 %
1.55 %
100.00 %
0.46 s
NVIDIA Tesla A100 (PyTorch)
39
MC-Stereo code 1.36 %
2.51 %
1.55 %
100.00 %
0.40 s
GPU @ 2.5 Ghz (Python)
M. Feng, J. Cheng, H. Jia, L. Liu, G. Xu and X. Yang: MC-Stereo: Multi-peak Lookup and Cascade
Search Range for Stereo Matching . International Conference on 3D Vision
(3DV) 2024.
40
SR Stereo_32_update 1.37 %
2.49 %
1.56 %
100.00 %
0.18 s
1 core @ 2.5 Ghz (C/C++)
W. Xiao and W. Zhao: Stepwise Regression and Pre-trained Edge for
Robust Stereo Matching . arXiv preprint arXiv:2406.06953 2024.
41
UGIA-IGEV 1.38 %
2.47 %
1.56 %
100.00 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
42
TEST 1.36 %
2.57 %
1.56 %
100.00 %
0.48
1 core @ 2.5 Ghz (C/C++)
43
MTStereo 1.36 %
2.57 %
1.56 %
100.00 %
0.48 s
GPU @ 2.5 Ghz (Python)
44
ForeEdge-Stereo 1.38 %
2.47 %
1.56 %
100.00 %
0.37 s
GPU @ 2.5 Ghz (Python)
45
AdaDepth 1.28 %
3.01 %
1.57 %
100.00 %
0.9 s
1 core @ 2.5 Ghz (C/C++)
46
HART code 1.39 %
2.49 %
1.57 %
100.00 %
0.25 s
NVIDIA Tesla A100 (PyTorch)
Z. Chen, Y. Zhang, W. Li, B. Wang, Y. Wu, Y. Zhao and C. Chen: Hadamard Attention Recurrent Transformer:
A Strong Baseline for Stereo Matching Transformer . arXiv preprint arXiv:2501.01023 2025.
47
DMIO 1.34 %
2.74 %
1.57 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (Python)
Y. Shi: Rethinking Iterative Stereo Matching from
Diffusion Bridge Model Perspective . arXiv preprint arXiv:2404.09051 2024.
48
middle stereo 1.34 %
2.76 %
1.57 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
49
MTEV 1.26 %
3.17 %
1.57 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
50
NMRF-Stereo code 1.28 %
3.07 %
1.57 %
100.00 %
0.09 s
NVIDIA RTX 3090 (PyTorch)
T. Guan, C. Wang and Y. Liu: Neural Markov Random Field for Stereo Matching . Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition 2024.
51
Mono2Stereo (IGEV) 1.36 %
2.67 %
1.58 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (Python)
52
fds 1.37 %
2.66 %
1.58 %
100.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
53
SG-IGEV code 1.40 %
2.50 %
1.58 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
B. Pan, j. jiao, B. Yao, J. Pang and J. Cheng: The Sampling-Gaussian for stereo
matching . 2024.
54
OpenStereo-IGEV code 1.44 %
2.31 %
1.59 %
100.00 %
0.18 s
NVIDIA-3090
X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for
Stereo Matching and Strong Baseline . arXiv preprint arXiv:2312.00343 2023.
55
RAFT-3D (CroCo) 1.38 %
2.65 %
1.59 %
100.00 %
1.5 s
GPU @ 2.5 Ghz (Python)
56
CroCo-Stereo code 1.38 %
2.65 %
1.59 %
100.00 %
0.93s
NVIDIA A100
P. Weinzaepfel, T. Lucas, V. Leroy, Y. Cabon, V. Arora, R. Br\'egier, G. Csurka, L. Antsfeld, B. Chidlovskii and J. Revaud: CroCo v2: Improved Cross-view Completion
Pre-training for Stereo Matching and Optical Flow . ICCV 2023.
57
MS-RAFT-3D 1.38 %
2.65 %
1.59 %
100.00 %
3 s
GPU @ 2.5 Ghz (Python)
58
IGEV-Stereo code 1.38 %
2.67 %
1.59 %
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.
59
DN+ACVNet 1.32 %
2.95 %
1.60 %
100.00 %
0.24 s
1 core @ 2.5 Ghz (C/C++)
J. Zhang, L. Huang, X. Bai, J. Zheng, L. Gu and E. Hancock: Exploring the Usage of Pre-trained
Features for Stereo Matching . International Journal of Computer
Vision 2024.
60
AMSCF-Net 1.32 %
2.98 %
1.60 %
100.00 %
0.3 s
GPU @ 2.5 Ghz (Python)
61
dilated volume 1.34 %
2.87 %
1.60 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
62
depth dila volume 1.25 %
3.35 %
1.60 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
63
RetinaStereo 1.44 %
2.43 %
1.61 %
100.00 %
0.25 s
1 core @ 2.5 Ghz (Python)
64
TEEV_ 1.44 %
2.46 %
1.61 %
100.00 %
0.44 s
1 core @ 2.5 Ghz (C/C++)
65
volume rese 1.39 %
2.72 %
1.61 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
66
UPFNet 1.38 %
2.85 %
1.62 %
100.00 %
0.25 s
1 core @ 2.5 Ghz (C/C++)
Q. Chen, B. Ge and J. Quan: Unambiguous Pyramid Cost Volumes Fusion
for Stereo Matching . IEEE Transactions on Circuits and
Systems for Video Technology 2023.
67
RAFT-SFFRU 1.42 %
2.65 %
1.63 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
68
Selective-RAFT code 1.41 %
2.71 %
1.63 %
100.00 %
0.45 s
1 core @ 2.5 Ghz (Python)
X. Wang, G. Xu, H. Jia and X. Yang: Selective-Stereo: Adaptive Frequency
Information Selection for Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2024.
69
xcit-stereo 1.36 %
2.97 %
1.63 %
100.00 %
0.44 s
1 core @ 2.5 Ghz (C/C++)
70
gaussi 1.51 %
2.24 %
1.63 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
71
DEFOM-Stereo_RVC code 1.42 %
2.68 %
1.63 %
100.00 %
0.24 s
1 core @ 2.5 Ghz (C/C++)
H. Jiang, Z. Lou, L. Ding, R. Xu, M. Tan, W. Jiang and R. Huang: DEFOM-Stereo: Depth Foundation Model
Based Stereo Matching . arXiv:2501.09466 2025.
72
DKT-SMoE 1.47 %
2.44 %
1.63 %
100.00 %
0.19 s
1 core @ 2.5 Ghz (C/C++)
73
NMRF-light 1.30 %
3.29 %
1.63 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
74
RobuSTereo 1.43 %
2.67 %
1.64 %
100.00 %
0.20 s
1 core @ 2.5 Ghz (Python)
75
GAStereo 1.43 %
2.68 %
1.64 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
76
IGEVStereo-DU 1.43 %
2.68 %
1.64 %
100.00 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
77
mlt 1.25 %
3.59 %
1.64 %
100.00 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
78
StereoSA code 1.36 %
3.09 %
1.65 %
100.00 %
0.06 s
RTX 4070S (Python)
79
M-FUSE code 1.40 %
2.91 %
1.65 %
100.00 %
1.3 s
GPU
L. Mehl, A. Jahedi, J. Schmalfuss and A. Bruhn: M-FUSE: Multi-frame Fusion for Scene Flow Estimation . Proc. Winter Conference on Applications of Computer Vision (WACV) 2023.
80
SF2SE3 code 1.40 %
2.91 %
1.65 %
100.00 %
2.7 s
GPU @ >3.5 Ghz (Python)
L. Sommer, P. Schröppel and T. Brox: SF2SE3: Clustering Scene Flow into SE (3)-Motions via Proposal and Selection . DAGM German Conference on Pattern Recognition 2022.
81
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.
82
SplatFlow3D code 1.40 %
2.91 %
1.65 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
B. Wang, Y. Zhang, J. Li, Y. Yu, Z. Sun, L. Liu and D. Hu: SplatFlow: Learning Multi-frame Optical Flow via Splatting . International Journal of Computer Vision 2024.
83
LoS 1.42 %
2.81 %
1.65 %
100.00 %
0.19 s
1 core @ 2.5 Ghz (Python)
K. Li, L. Wang, Y. Zhang, K. Xue, S. Zhou and Y. Guo: LoS: Local Structure Guided Stereo
Matching . Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition (CVPR) 2024.
84
ACVNet code 1.37 %
3.07 %
1.65 %
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.
85
croco 1.40 %
2.97 %
1.66 %
100.00 %
0.93 s
1 core @ 2.5 Ghz (Python)
86
sam 1.48 %
2.57 %
1.66 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
87
PCWNet code 1.37 %
3.16 %
1.67 %
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.
88
LaC+GANet code 1.44 %
2.83 %
1.67 %
100.00 %
1.8 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.
89
MSF-Stereo 1.50 %
2.59 %
1.68 %
100.00 %
0.29 s
1 core @ 2.5 Ghz (C/C++)
90
CREStereo code 1.45 %
2.86 %
1.69 %
100.00 %
0.41 s
GPU @ >3.5 Ghz (Python)
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.
91
ESM_Net 1.37 %
3.30 %
1.69 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
92
LLKStereo 1.45 %
2.90 %
1.69 %
100.00 %
0.06 s
1 core @ 2.5 Ghz (Python)
93
RTSN-P 1.42 %
3.06 %
1.70 %
100.00 %
0.04 s
GPU @ 2.5 Ghz (C/C++)
94
DuMa-Net 1.40 %
3.18 %
1.70 %
100.00 %
0.38 s
PyTorch GPU
S. Sun, R. liu and S. Sun: Range-free disparity estimation with self-
adaptive dual-matching . IET Computer Vision .
95
samstereo 1.52 %
2.59 %
1.70 %
100.00 %
0.44 s
1 core @ 2.5 Ghz (C/C++)
96
cs-Stereo 1.46 %
2.92 %
1.70 %
100.00 %
0.37 s
4 cores @ 2.5 Ghz (Python)
97
Wpa 1.52 %
2.68 %
1.71 %
100.00 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
98
DKT-IGEV 1.46 %
3.05 %
1.72 %
100.00 %
0.18 s
1 core @ 2.5 Ghz (C/C++)
J. Zhang, J. Li, L. Huang, X. Yu, L. Gu, J. Zheng and X. Bai: Robust Synthetic-to-Real Transfer for
Stereo Matching . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2024.
99
ls 1.53 %
2.69 %
1.72 %
100.00 %
0.32 s
1 core @ 2.5 Ghz (C/C++)
100
Context-Stereo-I 1.47 %
3.05 %
1.73 %
100.00 %
0.04 s
NVIDIA RTX 3080 (PyTorch)
101
SMoEStereo_RVC 1.50 %
2.88 %
1.73 %
100.00 %
0.19 s
GPU @ 2.5 Ghz (Python)
102
Patchmatch Stereo++ code 1.55 %
2.71 %
1.74 %
100.00 %
0.2 s
W. Ren, Q. Liao, Z. Shao, X. Lin, X. Yue, Y. Zhang and Z. Lu: Patchmatch Stereo++: Patchmatch Binocular
Stereo with Continuous Disparity Optimization . Proceedings of the 31st ACM
International Conference on Multimedia 2023.
103
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.
104
4D-IteraStereo 1.60 %
2.48 %
1.75 %
100.00 %
0.3 s
GPU @ 1.5 Ghz (Python)
G. Han, S. Shan, Y. Xu, K. Zhang and H. Wei: 4D-IteraStereo: Stereo Matching via
4D Cost Volume Aggregation and Iterative
Optimization . Measurement Science and
Technology 2025.
105
LaC+GwcNet code 1.43 %
3.44 %
1.77 %
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.
106
GMStereo code 1.49 %
3.14 %
1.77 %
100.00 %
0.17 s
GPU (Python)
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao and A. Geiger: Unifying Flow, Stereo and Depth
Estimation . arXiv preprint arXiv:2211.05783 2022.
107
BANet-3D 1.52 %
3.02 %
1.77 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
108
NLCA-Net v2 code 1.41 %
3.56 %
1.77 %
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 .
109
LightStereo 1.66 %
2.32 %
1.77 %
100.00 %
0.3 s
GPU @ 1.5 Ghz (Python)
110
Context-Stereo-I 1.48 %
3.22 %
1.77 %
100.00 %
0.04 s
NVIDIA RTX 3080 (PyTorch)
111
GHUStereo-4-nce 1.48 %
3.21 %
1.77 %
100.00 %
0.028 s
RTX 4070S (PyTorch)
112
GANet+DSMNet 1.48 %
3.23 %
1.77 %
100.00 %
2.0 s
GPU @ 2.5 Ghz (C/C++)
F. Zhang, X. Qi, R. Yang, V. Prisacariu, B. Wah and P. Torr: Domain-invariant Stereo Matching
Networks . Europe Conference on Computer Vision
(ECCV) 2020.
113
IVF-Astereo 1.62 %
2.56 %
1.78 %
100.00 %
0.15 s
GPU @ 3.0 Ghz (Python)
114
NLSDR-Net 1.55 %
2.97 %
1.78 %
100.00 %
0.06 s
1 core @ 2.5 Ghz (Python)
115
PFSMNet code 1.54 %
3.02 %
1.79 %
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.
116
Go-Stereo 1.51 %
3.16 %
1.79 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
117
RT-IGEV code 1.48 %
3.37 %
1.79 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
G. Xu, X. Wang, Z. Zhang, J. Cheng, C. Liao and X. Yang: IGEV++: Iterative Multi-range Geometry
Encoding Volumes for Stereo Matching . IEEE TPAMI 2025.
118
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.
119
TemporalStereo code 1.61 %
2.78 %
1.81 %
100.00 %
0.04 s
1 core @ 2.5 Ghz (Python)
Y. Zhang, M. Poggi and S. Mattoccia: TemporalStereo: Efficient
Spatial-Temporal Stereo Matching Network . IROS 2023.
120
Binary TTC 1.48 %
3.46 %
1.81 %
100.00 %
2 s
GPU @ 1.0 Ghz (Python)
A. Badki, O. Gallo, J. Kautz and P. Sen: Binary TTC: A Temporal Geofence for Autonomous
Navigation . The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2021.
121
RFlow3D+gannet+cambg 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
122
S3DGF 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
123
FP-TTC 1.48 %
3.46 %
1.81 %
100.00 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
124
ScaleFlow++RBO 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
125
ScaleFlow++_SAG 1.48 %
3.46 %
1.81 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python + C/C++)
126
CamLiRAFT code 1.48 %
3.46 %
1.81 %
100.00 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow
with Bidirectional Camera-LiDAR Fusion . TPAMI 2023.
127
GS58_ScaleRES 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
128
Scale-flow code 1.48 %
3.46 %
1.81 %
100.00 %
0.8 s
GPU @ 2.5 Ghz (Python)
H. Ling, Q. Sun, Z. Ren, Y. Liu, H. Wang and Z. Wang: Scale-flow: Estimating 3D Motion from
Video . Proceedings of the 30th ACM
International Conference on Multimedia 2022.
129
ScaleFlow++ 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
130
GS_ScaleFlow 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
131
PAFlow 1.48 %
3.46 %
1.81 %
100.00 %
0.53 s
1 core @ 2.5 Ghz (C/C++)
132
CamLiRAFT-NR code 1.48 %
3.46 %
1.81 %
100.00 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow
with Bidirectional Camera-LiDAR Fusion . arXiv preprint arXiv:2303.12017 2023.
133
RAFT-3D 1.48 %
3.46 %
1.81 %
100.00 %
2 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Teed and J. Deng: RAFT-3D: Scene Flow using Rigid-Motion
Embeddings . arXiv preprint arXiv:2012.00726 2020.
134
OAMaskFlow 1.48 %
3.46 %
1.81 %
100.00 %
0.5 s
1 core @ 2.5 Ghz (Python)
135
ADFactory code 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Ling, Q. Sun, Y. Sun, X. Xu and X. Li: ADFactory: An Effective Framework for
Generalizing Optical Flow with NeRF . Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition 2024.
136
GS58_Scale 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
137
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.
138
CamLiFlow code 1.48 %
3.46 %
1.81 %
100.00 %
1.2 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Liu, T. Lu, Y. Xu, J. Liu, W. Li and L. Chen: CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint
Optical Flow and Scene Flow Estimation . CVPR 2022.
139
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.
140
ISDAFlow+ SAGFt 1.48 %
3.46 %
1.81 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
141
G2L-Stereo 1.54 %
3.20 %
1.82 %
100.00 %
0.05 s
GPU @ 1.5 Ghz (Python)
142
ADStereo code 1.59 %
2.94 %
1.82 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
Y. Wang, K. Li, L. Wang, J. Hu, D. Wu and Y. Guo: ADStereo: Efficient Stereo Matching with
Adaptive Downsampling and Disparity Alignment . IEEE Transactions on Image Processing 2025.
143
LightStereo-L* code 1.60 %
2.92 %
1.82 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need
for Efficient 2D Cost Aggregation . arXiv preprint arXiv:2406.19833 2024.
144
ESMStereo-L-gwc code 1.43 %
3.80 %
1.82 %
100.00 %
0.026 s
RTX 4070S (Python)
145
OptStereo 1.50 %
3.43 %
1.82 %
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.
146
tt-Stereo 1.64 %
2.75 %
1.82 %
100.00 %
0.23 s
1 core @ 2.5 Ghz (C/C++)
147
BANet-2D 1.59 %
3.03 %
1.83 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
148
LoS_RVC 1.58 %
3.08 %
1.83 %
100.00 %
0.19 s
1 core @ 2.5 Ghz (C/C++)
K. Li, L. Wang, Y. Zhang, K. Xue, S. Zhou and Y. Guo: LoS: Local Structure Guided Stereo
Matching . Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition (CVPR) 2024.
149
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.
150
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.
151
HCR 1.51 %
3.51 %
1.85 %
100.00 %
0.19 s
GPU @ 2.5 Ghz (Python)
Y. Tuming Yuan: Hourglass cascaded recurrent stereo
matching network . Image and Vision computing 2024.
152
GHUStereo-4-gwce 1.50 %
3.64 %
1.85 %
100.00 %
0.030 s
RTX 4070S (PyTorch)
153
ADStereo_fast code 1.57 %
3.25 %
1.85 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
Y. Wang, K. Li, L. Wang, J. Hu, D. Wu and Y. Guo: ADStereo: Efficient Stereo Matching with
Adaptive Downsampling and Disparity Alignment . IEEE Transactions on Image Processing 2025.
154
UCFNet_RVC code 1.57 %
3.33 %
1.86 %
100.00 %
0.21 s
GPU @ 2.5 Ghz (Python)
Z. Shen, X. Song, Y. Dai, D. Zhou, Z. Rao and L. Zhang: Digging Into Uncertainty-Based Pseudo-
Label for Robust Stereo Matching . IEEE Transactions on Pattern Analysis
and Machine Intelligence 2023.
155
LCA-Stereo 1.57 %
3.37 %
1.87 %
100.00 %
0.03 s
NVIDIA RTX 3090 (PyTorch)
156
CFNet code 1.54 %
3.56 %
1.88 %
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. Digging Into Uncertainty-Based Pseudo-
Label for Robust Stereo Matching . IEEE Transactions on Pattern Analysis
and Machine Intelligence 2023.
157
RTSN 1.62 %
3.21 %
1.88 %
100.00 %
0.029 s
1 core @ 2.5 Ghz (Python)
158
RigidMask+ISF code 1.53 %
3.65 %
1.89 %
100.00 %
3.3 s
GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Learning to Segment Rigid Motions from Two
Frames . CVPR 2021.
159
DCVSMNet code 1.60 %
3.33 %
1.89 %
100.00 %
0.053 s
RTX 4070S (PyTorch)
M. Tahmasebi, S. Huq, K. Meehan and M. McAfee: DCVSMNet: Double Cost Volume Stereo Matching Network . Neurocomputing 2025.
160
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.
161
Context-Stereo code 1.66 %
3.07 %
1.89 %
100.00 %
0.03 s
NVIDIA RTX 3080 (PyTorch)
162
RSAstereo 1.58 %
3.50 %
1.90 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
163
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.
164
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.
165
Abc-Net 1.47 %
4.20 %
1.92 %
100.00 %
0.83 s
4 core @ 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.
166
LightStereo-L code 1.78 %
2.64 %
1.93 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need
for Efficient 2D Cost Aggregation . arXiv preprint arXiv:2406.19833 2024.
167
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.
168
PCVNet 1.68 %
3.19 %
1.93 %
100.00 %
0.05 s
GPU @ 2.5 Ghz (Python)
J. Zeng, C. Yao, L. Yu, Y. Wu and Y. Jia: Parameterized Cost Volume for Stereo
Matching . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2023.
169
CAL-Net 1.59 %
3.76 %
1.95 %
100.00 %
0.44 s
2 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.
170
CCAStereo 1.58 %
3.81 %
1.95 %
100.00 %
0.05 s
GPU @ 1.5 Ghz (Python)
H. Hashemi, Y. Baleghi and M. Hassanzadeh: Real-time stereo matching with enhanced
geometric comprehension through cross-attention
integration . Neurocomputing 2025.
171
TCMNet 1.68 %
3.33 %
1.95 %
100.00 %
0.02 s
RTX 3090 GPU PyTorch
172
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.
173
CFNet_RVC code 1.65 %
3.53 %
1.96 %
100.00 %
0.22 s
GPU @ 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. Digging Into Uncertainty-Based Pseudo-
Label for Robust Stereo Matching . IEEE Transactions on Pattern Analysis
and Machine Intelligence 2023.
174
PGNet 1.64 %
3.60 %
1.96 %
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.
175
SG-MSNet3D 1.61 %
3.81 %
1.98 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
B. Pan, j. jiao, B. Yao, J. Pang and J. Cheng: The Sampling-Gaussian for stereo matching . 2024.
176
HITNet code 1.74 %
3.20 %
1.98 %
100.00 %
0.02 s
GPU @ 2.5 Ghz (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.
177
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.
178
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.
179
SG-PSMnet 1.77 %
3.13 %
2.00 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
B. Pan, j. jiao, B. Yao, J. Pang and J. Cheng: The Sampling-Gaussian for stereo
matching . 2024.
180
Fast-ACVNet+ code 1.70 %
3.53 %
2.01 %
100.00 %
0.05 s
NVIDIA RTX 3090 (PyTorch)
G. Xu, Y. Wang, J. Cheng, J. Tang and X. Yang: Accurate and efficient stereo matching
via attention concatenation volume . IEEE Transactions on Pattern Analysis
and Machine Intelligence 2023.
181
CoEx code 1.74 %
3.41 %
2.02 %
100.00 %
0.027 s
GPU 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.
182
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.
183
SCV-Stereo code 1.67 %
3.78 %
2.02 %
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.
184
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.
185
G2L-ROB 1.76 %
3.39 %
2.03 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
186
LightStereo-M code 1.81 %
3.22 %
2.04 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need
for Efficient 2D Cost Aggregation . arXiv preprint arXiv:2406.19833 2024.
187
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.
188
iRaftStereo_RVC 1.88 %
3.03 %
2.07 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (Python)
H. Jiang, R. Xu and W. Jiang: An Improved RaftStereo Trained with A
Mixed Dataset for the Robust Vision Challenge
2022 . arXiv preprint arXiv:2210.12785 2022.
189
PSM + SMD-Nets code 1.69 %
4.01 %
2.08 %
100.00 %
0.41 s
1 core @ 2.5 Ghz (Python + C/C++)
F. Tosi, Y. Liao, C. Schmitt and A. Geiger: SMD-Nets: Stereo Mixture Density Networks . Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
190
MDCNet 1.76 %
3.68 %
2.08 %
100.00 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
W. Chen, X. Jia, M. Wu and Z. Liang: Multi-Dimensional Cooperative Network for
Stereo Matching . IEEE Robotics and Automation Letters 2022.
191
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.
192
SG-GwcNet-g 1.73 %
3.88 %
2.09 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
B. Pan, j. jiao, B. Yao, J. Pang and J. Cheng: The Sampling-Gaussian for stereo matching . 2024.
193
3D-MSNet / MSNet3D code 1.75 %
3.87 %
2.10 %
100.00 %
1.5s
Python,1080Ti
F. Shamsafar, S. Woerz, R. Rahim and A. Zell: MobileStereoNet: Towards Lightweight Deep
Networks for Stereo Matching . Proceedings of the IEEE/CVF Winter
Conference on Applications of Computer Vision 2022.
194
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.
195
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.
196
GHUStereo-8-gwce 1.88 %
3.34 %
2.12 %
100.00 %
0.015 s
RTX 4070 (PyTorch)
197
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.
198
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.
199
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.
200
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.
201
ASM 1.86 %
3.72 %
2.17 %
100.00 %
0.01 s
GPU @ 2.5 Ghz (Python)
202
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.
203
BGNet+ 1.81 %
4.09 %
2.19 %
100.00 %
0.03 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.
204
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.
205
Q code 1.84 %
4.05 %
2.21 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
206
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.
207
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.
208
GHUStereo-8-nce 1.92 %
3.79 %
2.23 %
100.00 %
0.016 s
RTX 4070S (PyTorch)
209
IINet 2.02 %
3.39 %
2.25 %
100.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
210
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.
211
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.
212
DBCANet code 2.02 %
3.44 %
2.26 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
213
OpenStereo-PSMNet code 1.80 %
4.58 %
2.26 %
100.00 %
0.21 s
GPU RTX3090
X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for
Stereo Matching and Strong Baseline . arXiv preprint arXiv:2312.00343 2023.
214
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.
215
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.
216
SG-MSNet2D 1.94 %
4.07 %
2.29 %
100.00 %
1 s
1 core @ 2.5 Ghz (C/C++)
B. Pan, j. jiao, B. Yao, J. Pang and J. Cheng: The Sampling-Gaussian for stereo matching . 2024.
217
LightStereo-S code 2.00 %
3.80 %
2.30 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need
for Efficient 2D Cost Aggregation . arXiv preprint arXiv:2406.19833 2024.
218
Separable Convs code 1.90 %
4.36 %
2.31 %
100.00 %
2 s
1 core @ 2.5 Ghz (Python)
R. Rahim, F. Shamsafar and A. Zell: Separable Convolutions for Optimizing 3D Stereo Networks . 2021 IEEE International Conference on Image Processing (ICIP) 2021.
219
Separable Convs code 1.90 %
4.36 %
2.31 %
100.00 %
2 s
1 core @ 2.5 Ghz (Python)
R. Rahim, F. Shamsafar and A. Zell: Separable Convolutions for Optimizing 3D Stereo Networks . 2021 IEEE International Conference on Image Processing (ICIP) 2021.
220
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.
221
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.
222
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.
223
TriStereoNet code 1.86 %
4.77 %
2.35 %
100.00 %
0.5 s
Python,1080Ti
F. Shamsafar and A. Zell: TriStereoNet: A Trinocular Framework for
Multi-baseline Disparity Estimation . arXiv preprint arXiv:2111.12502 2021.
224
GhostStereoNet 2.07 %
4.00 %
2.39 %
100.00 %
0.04 s
GPU @ 3.0 Ghz (Python)
225
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 . .
226
JBFNet2 2.01 %
4.90 %
2.49 %
100.00 %
0.29 s
GPU @ 3.0 Ghz (Python)
227
JBFNet 2.14 %
4.25 %
2.49 %
100.00 %
0.29 s
GPU @ 3.0 Ghz (Python)
228
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.
229
BGNet 2.07 %
4.74 %
2.51 %
100.00 %
0.02 s
GPU @ >3.5 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.
230
EfficientStereo code 2.16 %
4.40 %
2.54 %
100.00 %
0.015 s
NVIDIA RTX 3090 (PyTorch)
231
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.
232
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.
233
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.
234
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.
235
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.
236
MMStereo 2.25 %
4.38 %
2.61 %
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 . .
237
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.
238
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.
239
RLStereo code 2.09 %
5.38 %
2.64 %
100.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
Anonymous: RLStereo: Real-time Stereo Matching
based on Reinforcement Learning . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2021.
240
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.
241
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.
242
EfficientStereo_FP16 code 2.37 %
4.77 %
2.77 %
100.00 %
0.003 s
NVIDIA RTX 4090 (TensorRT)
243
2D-MSNet / MSNet2D code 2.49 %
4.53 %
2.83 %
100.00 %
0.4s
Python,1080Ti
F. Shamsafar, S. Woerz, R. Rahim and A. Zell: MobileStereoNet: Towards Lightweight Deep
Networks for Stereo Matching . Proceedings of the IEEE/CVF Winter
Conference on Applications of Computer Vision 2022.
244
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.
245
DualNet 2.46 %
5.25 %
2.92 %
100.00 %
0.17 s
1 core @ 2.5 Ghz (C/C++)
246
RoSe 2.65 %
4.36 %
2.94 %
100.00 %
0.17 s
1 core @ 2.5 Ghz (C/C++)
247
PVStereo 2.29 %
6.50 %
2.99 %
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.
248
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.
249
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).
250
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 . CVPR 2021. AdaStereo: An Efficient Domain-Adaptive
Stereo Matching Approach . IJCV 2021.
251
RFlow3D+monster 2.51 %
6.04 %
3.09 %
100.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
252
RFlow3D 2.51 %
6.04 %
3.09 %
100.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
253
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.
254
Syn2Real Stereo 2.69 %
5.20 %
3.11 %
100.00 %
0.28 s
1 core @ 2.5 Ghz (C/C++)
255
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.
256
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.
257
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.
258
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.
259
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.
260
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.
261
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.
262
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.
263
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.
264
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.
265
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.
266
FSMNet 2.95 %
7.39 %
3.69 %
100.00 %
1 s
1 core @ 2.5 Ghz (Python + C/C++)
267
DSMNet-synthetic 3.11 %
6.72 %
3.71 %
100.00 %
1.6 s
4 cores @ 2.5 Ghz (C/C++)
F. Zhang, X. Qi, R. Yang, V. Prisacariu, B. Wah and P. Torr: Domain-invariant Stereo Matching
Networks . Europe Conference on Computer Vision
(ECCV) 2020.
268
CAS++ 3.10 %
6.89 %
3.73 %
99.98 %
.1 s
GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
269
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.
270
DualNet-one stage 2.89 %
8.73 %
3.86 %
100.00 %
0.17 s
1 core @ 2.5 Ghz (C/C++)
271
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 . .
272
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 .
273
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.
274
ADCPNet 3.27 %
7.58 %
3.98 %
100.00 %
0.007 s
GPU @ 2.5 Ghz (Python)
H. Dai, X. Zhang, Y. Zhao, H. Sun and N. Zheng: Adaptive Disparity Candidates Prediction
Network for Efficient Real-Time Stereo Matching . IEEE Transactions on Circuits and
Systems for Video Technology 2022.
275
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.
276
ReaSMNet 3.47 %
7.20 %
4.09 %
100.00 %
0.01 s
GPU @ 2.5 Ghz (Python)
277
DGS 3.21 %
8.62 %
4.11 %
100.00 %
0.32 s
GPU @ 2.5 Ghz (Python + C/C++)
W. Chuah, R. Tennakoon, A. Bab-Hadiashar and D. Suter: Achieving Domain Robustness in Stereo
Matching Networks by Removing Shortcut Learning . arXiv preprint arXiv:2106.08486 2021.
278
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.
279
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.
280
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.
281
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.
282
SMV 3.45 %
9.32 %
4.43 %
100.00 %
0.5 s
GPU @ 2.5 Ghz (C/C++)
W. Yuan, Y. Zhang, B. Wu, S. Zhu, P. Tan, M. Wang and Q. Chen: Stereo Matching by Self-
supervision of Multiscopic Vision . IEEE/RSJ International
Conference on Intelligent Robots and
Systems (IROS) 2021.
283
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.
284
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.
285
BaCon-IGEV 2.89 %
13.19 %
4.60 %
100.00 %
0.18 s
1 core @ 2.5 Ghz (Python)
286
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.
287
Self-SuperFlow-ft 3.81 %
8.92 %
4.66 %
100.00 %
0.13 s
GTX 1080 Ti
K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in
Stereo Sequences . International Conference on Image Processing (ICIP) 2022.
288
Pseudo-Stereo 3.11 %
12.52 %
4.68 %
100.00 %
0.15 s
GPU @ 2.5 Ghz (Python)
289
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.
290
P3SNet+ code 4.15 %
7.59 %
4.72 %
100.00 %
0.01 s
1 core @ 2.5 Ghz (Python)
A. Emlek and M. Peker: P3SNet: Parallel Pyramid Pooling Stereo
Network . IEEE Transactions on Intelligent
Transportation Systems 2023.
291
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.
292
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.
293
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 .
294
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.
295
OAUSM 3.51 %
12.56 %
5.02 %
100.00 %
0.33 s
1 core @ 2.5 Ghz (Python)
296
Un-ViTAStereo 3.58 %
12.30 %
5.03 %
100.00 %
0.22 s
GPU @ 1.0 Ghz (Python)
297
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.
298
P3SNet code 4.40 %
8.28 %
5.05 %
100.00 %
0.01 s
GPU @ 2.5 Ghz (Python)
A. Emlek and M. Peker: P3SNet: Parallel Pyramid Pooling Stereo
Network . IEEE Transactions on Intelligent
Transportation Systems 2023.
299
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.
300
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.
301
EfficientStereo_INT8 code 4.91 %
6.49 %
5.17 %
100.00 %
0.005 s
NVIDIA RTX 4090 (TensorRT)
302
stereoVAE code 4.25 %
10.18 %
5.23 %
100.00 %
0.03 s
Jetson AGX Xavier GPU
Q. Chang, X. Li, X. Xu, X. Liu, Y. Li and J. Miyazaki: StereoVAE: A lightweight stereo-matching
system using embedded GPUs . 2023 IEEE International Conference on
Robotics and Automation (ICRA) 2023.
303
IFUSM-Stereo 3.93 %
11.78 %
5.24 %
100.00 %
0.33 s
1 core @ 2.5 Ghz (C/C++)
304
PUI-Stereo2 3.60 %
13.48 %
5.24 %
100.00 %
0.33 s
1 core @ 2.5 Ghz (Python)
305
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.
306
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.
307
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.
308
PUI-Stereo 3.62 %
14.24 %
5.39 %
99.99 %
0.33 s
GPU @ 2.5 Ghz (Python)
309
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.
310
TinyStereo 4.99 %
9.33 %
5.71 %
100.00 %
0.02 s
Jetson AGX Xavier GPU
Q. Chang, X. Xu, A. Zha, M. Er, Y. Sun and Y. Li: TinyStereo: A Tiny Coarse-to-Fine
Framework for Vision-Based Depth Estimation on
Embedded GPUs . IEEE Transactions on Systems, Man, and
Cybernetics: Systems 2024.
311
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.
312
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.
313
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.
314
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.
315
CRD-Fusion code 4.59 %
13.68 %
6.11 %
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.
316
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.
317
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.
318
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.
319
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.
320
UHP 5.00 %
13.70 %
6.45 %
100.00 %
0.02 s
GPU @ 2.5 Ghz (C/C++)
R. Yang, X. Li, R. Cong and J. Du: Unsupervised Hierarchical Iterative Tile
Refinement Network with 3D Planar Segmentation
Loss . IEEE Robotics and Automation Letters 2024.
321
SSpsm-GAN 5.00 %
13.90 %
6.48 %
100.00 %
0.8 s
GPU @ 2.5 Ghz (Python)
322
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.
323
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.
324
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.
325
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.
326
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.
327
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. A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them . IJCV 2013.
328
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. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation . PAMI 2011.
329
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. SphereFlow: 6
DoF Scene Flow from RGB-D Pairs . CVPR 2014.
330
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.
331
Permutation Stereo 5.53 %
15.47 %
7.18 %
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.
332
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.
333
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.
334
Z2ZNCC code 6.55 %
13.19 %
7.65 %
99.93 %
0.035s
Jetson TX2 GPU @ 1.0 Ghz (CUDA)
Q. Chang, A. Zha, W. Wang, X. Liu, M. Onishi, L. Lei, M. Er and T. Maruyama: Efficient stereo matching on embedded
GPUs with zero-means cross correlation . Journal of Systems Architecture 2022.
335
ReS2tAC 6.27 %
16.07 %
7.90 %
86.03 %
0.06 s
Jetson AGX GPU @ 1.5 Ghz (C/C++)
B. Ruf, J. Mohrs, M. Weinmann, S. Hinz and J. Beyerer: ReS2tAC - UAV-Borne Real-Time
SGM Stereo Optimized for Embedded ARM and
CUDA Devices . Sensors 2021.
336
WT-kan code 6.53 %
15.34 %
8.00 %
100.00 %
0.12 s
gpu @ 2.5 Ghz (Python)
337
Self-SuperFlow 5.78 %
19.76 %
8.11 %
100.00 %
0.13 s
GTX 1080 Ti
K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in
Stereo Sequences . International Conference on Image Processing (ICIP) 2022.
338
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.
339
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.
340
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.
341
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.
342
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.
343
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.
344
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.
345
EMR-MSF 8.61 %
15.15 %
9.70 %
100.00 %
0.25 s
GPU @ 2.5 Ghz (Python)
Z. Jiang and M. Okutomi: EMR-MSF: Self-Supervised Recurrent
Monocular Scene Flow Exploiting Ego-Motion
Rigidity . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2023.
346
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.
347
PGC-WCNet code 7.98 %
18.63 %
9.75 %
99.86 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
348
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.
349
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.
350
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.
351
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.
352
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.
353
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.
354
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.
355
3DG-DVO 14.12 %
18.68 %
14.88 %
100.00 %
0.04 s
GPU @ 1.5 Ghz (Python)
356
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.
357
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.
358
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.
359
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.
360
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.
361
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.
362
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.
363
Multi-Mono-SF-ft code 21.60 %
28.22 %
22.71 %
100.00 %
0.06 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Multi-Frame
Monocular Scene Flow . CVPR 2021.
364
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.
365
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.
366
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.
367
Multi-Mono-SF code 27.48 %
47.30 %
30.78 %
100.00 %
0.06 s
NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Multi-Frame
Monocular Scene Flow . CVPR 2021.
368
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.
369
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.
370
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.
371
Stereo-RSSF code 56.60 %
73.05 %
59.34 %
9.26 %
2.5 s
8 core @ 2.5 Ghz (Matlab)
E. Salehi, A. Aghagolzadeh and R. Hosseini: Stereo-RSSF: stereo robust sparse scene-flow estimation . The Visual Computer 2023.
Flow: Method uses optical flow (2 temporally adjacent images)
Multiview: Method uses more than 2 temporally adjacent images
Motion stereo: Method uses epipolar geometry for computing optical flow
Additional training data: Use of additional data sources for training (see details)