Stereo Evaluation 2012


The stereo / flow benchmark consists of 194 training image pairs and 195 test image pairs, saved in loss less png format. Our evaluation server computes the average number of bad pixels for all non-occluded or occluded (=all groundtruth) pixels. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing disparity maps and flow fields.

Our evaluation table ranks all methods according to the number of non-occluded erroneous pixels at the specified disparity / end-point error threshold. All methods providing less than 100 % density have been interpolated using simple background interpolation as explained in the corresponding header file in the development kit. For each method we show:

  • Out-Noc: Percentage of erroneous pixels in non-occluded areas
  • Out-All: Percentage of erroneous pixels in total
  • Avg-Noc: Average disparity / end-point error in non-occluded areas
  • Avg-All: Average disparity / end-point error in total
  • Density: Percentage of pixels for which ground truth has been provided by the method

Note: On 04.11.2013 we have improved the ground truth disparity maps and flow fields leading to slightly improvements for all methods. Please download the stereo/flow dataset with the improved ground truth for training again, if you have downloaded the dataset prior to 04.11.2013. Please consider reporting these new number for all future submissions. Links to last leaderboards before the updates: stereo and flow!

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • 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)

Table        Error threshold        Evaluation area

Method Setting Code Out-Noc Out-All Avg-Noc Avg-All Density Runtime Environment
1 MonSter++ code 0.79 % 1.07 % 0.3 px 0.4 px 100.00 % 0.45 s GPU @ 2.5 Ghz (Python)
J. Cheng, W. Liao, Z. Cai, L. Liu, G. Xu, X. Wang, Y. Wang, Z. Yuan, Y. Deng, J. Zang, Y. Shi, J. Tang and X. Yang: MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors. 2025.
2 MAF++ 0.80 % 1.07 % 0.3 px 0.4 px 100.00 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
3 LACA1 code 0.80 % 1.00 % 0.3 px 0.4 px 100.00 % 0.24 s 1 core @ 2.5 Ghz (C/C++)
4 MT 0.80 % 1.11 % 0.3 px 0.4 px 100.00 % 0.45 s GPU @ 2.5 Ghz (Python)
5 test1 0.81 % 1.09 % 0.3 px 0.4 px 100.00 % 0.67 s GPU @ 2.5 Ghz (Python)
6 DispViT+ 0.82 % 1.02 % 0.3 px 0.4 px 100.00 % 0.15 s 1 core @ 2.5 Ghz (Python)
7 Wavelet-MonSter 0.83 % 1.07 % 0.4 px 0.4 px 100.00 % 0.58 s NVIDIA A6000 (PyTorch)
8 BridgeDepth code 0.83 % 1.03 % 0.4 px 0.4 px 100.00 % 0.13 s Pytorch@NVIDIA RTX 3090
T. Guan, J. Guo, C. Wang and Y. Liu: BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment. ICCV 2025 Highlight.
9 MonSter code 0.84 % 1.09 % 0.4 px 0.4 px 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.
10 GREAT-IGEV-DepthAny code 0.85 % 1.13 % 0.4 px 0.4 px 100.00 % 0.43 s GPU @ 2.5 Ghz (Python)
J. Li, X. Chen, Z. Jiang, Q. Zhou, Y. Li and J. Wang: Global regulation and excitation via attention tuning for stereo matching. Proceedings of the IEEE/CVF International Conference on Computer Vision 2025.
11 MGS-Selective 0.85 % 1.13 % 0.4 px 0.4 px 100.00 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
12 LACA code 0.86 % 1.04 % 0.3 px 0.4 px 100.00 % 0.24 s GPU @ 2.5 Ghz (Python)
13 Unrectified stereo 0.88 % 1.17 % 0.4 px 0.4 px 100.00 % 0.9 s 1 core @ 2.5 Ghz (Python)
14 IGEV++ (DepthAny.) code 0.89 % 1.13 % 0.4 px 0.4 px 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.
15 SGD-Stereo 0.91 % 1.13 % 0.4 px 0.4 px 100.00 % 0.45 s 1 core @ 2.5 Ghz (C/C++)
16 depth dila volume 0.92 % 1.27 % 0.4 px 0.4 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
17 HCCV-Stereo 0.93 % 1.17 % 0.4 px 0.4 px 100.00 % 0.60 s 1 core @ 2.5 Ghz (C/C++)
18 ViTAStereo code 0.93 % 1.16 % 0.4 px 0.4 px 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.
19 DEFOM-Stereo code 0.94 % 1.18 % 0.3 px 0.4 px 100.00 % 0.30 s 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. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2025.
20 mlt 0.94 % 1.28 % 0.4 px 0.4 px 100.00 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
21 GREAT-IGEV (Solid) code 0.95 % 1.32 % 0.4 px 0.4 px 100.00 % 0.33 s GPU @ 2.5 Ghz (Python)
J. Li, X. Chen, Z. Jiang, Q. Zhou, Y. Li and J. Wang: Global regulation and excitation via attention tuning for stereo matching. Proceedings of the IEEE/CVF International Conference on Computer Vision 2025.
22 S-IGEV-ICAE 0.95 % 1.21 % 0.4 px 0.4 px 100.00 % 0.251 s 1 core @ 2.5 Ghz (C/C++)
23 frequence-stereo 0.96 % 1.31 % 0.4 px 0.4 px 100.00 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
24 Depthstereo 0.98 % 1.21 % 0.4 px 0.4 px 100.00 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
25 GANet+ADL code 0.98 % 1.29 % 0.4 px 0.5 px 100.00 % 0.67 s 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.
26 DS-Stereo 0.98 % 1.34 % 0.4 px 0.4 px 100.00 % 0.35 s 1 core @ 2.5 Ghz (Python)
J. Lin, J. Du and H. Wang: DS-Stereo: Deep-Shallow Information Interaction for Stereo Matching. IEEE Robotics and Automation Letters 2025.
27 MoCha-V2 code 0.98 % 1.24 % 0.4 px 0.4 px 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.
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 2024.
28 dpt_FFLO-Net 0.99 % 1.24 % 0.4 px 0.4 px 100.00 % 0.21 s GPU @ 2.5 Ghz (Python)
29 RiskMin code 1.00 % 1.44 % 0.4 px 0.5 px 100.00 % 0.20 s GPU @ 2.5 Ghz (Python)
C. Liu, S. Kumar, S. Gu, R. Timofte, Y. Yao and L. Gool: Stereo Risk: A Continuous Modeling Approach to Stereo Matching. Forty-first International Conference on Machine Learning (ICML 2024) 2024.
30 StereoBase code 1.00 % 1.26 % 0.4 px 0.4 px 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.
31 GREAT-Selective 1.00 % 1.31 % 0.4 px 0.4 px 100.00 % 0.43 s NVIDIA RTX 3090 (PyTorch)
32 VIP-Stereo 1.01 % 1.31 % 0.4 px 0.4 px 100.00 % 0.40 s 1 core @ 2.5 Ghz (C/C++)
33 NMRF-Stereo code 1.01 % 1.35 % 0.4 px 0.4 px 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.
34 GREAT-IGEV code 1.02 % 1.37 % 0.4 px 0.4 px 100.00 % 0.33 s NVIDIA RTX 3090 (PyTorch)
J. Li, X. Chen, Z. Jiang, Q. Zhou, Y. Li and J. Wang: Global regulation and excitation via attention tuning for stereo matching. Proceedings of the IEEE/CVF International Conference on Computer Vision 2025.
35 NLCSM 1.02 % 1.32 % 0.4 px 0.4 px 100.00 % 0.9 s 1 core @ 2.5 Ghz (C/C++)
36 ACVNet-ICAE 1.02 % 1.32 % 0.4 px 0.4 px 100.00 % 0.218 1 core @ 2.5 Ghz (C/C++)
37 DN+ACVNet 1.02 % 1.41 % 0.4 px 0.5 px 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.
38 PGA-Stereo 1.02 % 1.31 % 0.4 px 0.4 px 100.00 % 0.9 s 1 core @ 2.5 Ghz (Python)
39 IGEV++ code 1.04 % 1.36 % 0.4 px 0.4 px 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.
40 FFLO-Net 1.04 % 1.31 % 0.4 px 0.4 px 100.00 % 0.37 s NVIDIA RTX 3090 (PyTorch)
41 Reg-Stereo 1.04 % 1.35 % 0.4 px 0.4 px 100.00 % 0.37 s 1 core @ 2.5 Ghz (C/C++)
42 MC-Stereo code 1.04 % 1.34 % 0.4 px 0.4 px 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.
43 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.
44 WCG-NET 1.04 % 1.38 % 0.4 px 0.4 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
45 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.
46 StereoSA code 1.05 % 1.40 % 0.4 px 0.4 px 100.00 % 0.064 s RTX 4070S (Python)
47 AIO-Stereo 1.05 % 1.29 % 0.4 px 0.4 px 100.00 % 0.20 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.
48 UGIA-Selective 1.05 % 1.36 % 0.4 px 0.4 px 100.00 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao and W. Zhao: SR-Stereo \& DAPE: Stepwise Regression and Pre-Trained Edges for Practical Stereo Matching. IEEE Transactions on Intelligent Transportation Systems 2025.
W. Xiao and W. Zhao: Rectified Iterative Disparity for Stereo Matching. arXiv preprint arXiv:2406.10943 2024.
49 ICVP code 1.06 % 1.39 % 0.4 px 0.5 px 100.00 % 0.17 s GPU @ 1.5 Ghz (Python)
O. Kwon and E. Zell: Image-Coupled Volume Propagation for Stereo Matching. 2023 IEEE International Conference on Image Processing (ICIP) 2023.
50 VMStereo-Base 1.06 % 1.35 % 0.4 px 0.4 px 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
51 NMRF-light 1.06 % 1.38 % 0.4 px 0.4 px 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
52 4D-IteraStereo 1.06 % 1.36 % 0.4 px 0.4 px 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.
53 try 1.06 % 1.36 % 0.4 px 0.4 px 100.00 % 0.31 s GPU @ 2.5 Ghz (Python)
54 MoCha-Stereo code 1.06 % 1.36 % 0.4 px 0.4 px 100.00 % 0.33 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.
55 DMIO 1.07 % 1.38 % 0.4 px 0.4 px 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.
56 Selective-IGEV code 1.07 % 1.38 % 0.4 px 0.4 px 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.
57 dilated volume 1.07 % 1.41 % 0.4 px 0.5 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
58 RT-MonSter++ 1.07 % 1.41 % 0.4 px 0.4 px 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
59 SR Stereo 1.09 % 1.36 % 0.4 px 0.4 px 100.00 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao and W. Zhao: SR-Stereo \& DAPE: Stepwise Regression and Pre-Trained Edges for Practical Stereo Matching. IEEE Transactions on Intelligent Transportation Systems 2025.
60 Lite Any Stereo 1.09 % 1.49 % 0.4 px 0.5 px 100.00 % 0.02 s GPU @ 1.0 Ghz (Python)
61 HCR 1.09 % 1.42 % 0.4 px 0.4 px 100.00 % 0.19 s GPU @ 2.5 Ghz (Python)
Y. Tuming Yuan: Hourglass cascaded recurrent stereo matching network. Image and Vision computing 2024.
62 UCFNet code 1.09 % 1.45 % 0.4 px 0.5 px 100.00 % 0.21 s 1 core @ 2.5 Ghz (C/C++)
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.
63 MatchStereo code 1.09 % 1.39 % 0.4 px 0.4 px 100.00 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
T. Yan, T. Liu, X. Yang, Q. Zhao and Z. Xia: MatchAttention: Matching the Relative Positions for High-Resolution Cross-View Matching. arXiv preprint arXiv:2510.14260 2025.
64 MSCA-IGEV 1.09 % 1.39 % 0.4 px 0.4 px 100.00 % 0.2 s GPU @ 3.0 Ghz (Python)
65 GAStereo 1.10 % 1.47 % 0.4 px 0.4 px 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
66 CMSF-stereo 1.10 % 1.42 % 0.4 px 0.4 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
67 LoS 1.10 % 1.38 % 0.4 px 0.4 px 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.
68 xcit-stereo 1.10 % 1.43 % 0.4 px 0.4 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
69 GGEV 1.10 % 1.44 % 0.4 px 0.4 px 100.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
70 Selective-RAFT code 1.10 % 1.43 % 0.4 px 0.5 px 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.
71 middle stereo 1.10 % 1.45 % 0.4 px 0.4 px 100.00 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
72 HART code 1.11 % 1.38 % 0.4 px 0.4 px 100.00 % 0.34 s NVIDIA Tesla A100 (Python)
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.
73 MSCA-stereo 1.11 % 1.44 % 0.4 px 0.4 px 100.00 % 0.4 s GPU @ 2.5 Ghz (Python)
74 volume rese 1.11 % 1.46 % 0.4 px 0.4 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
75 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 .
76 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.
77 FSU-Stereo 1.12 % 1.49 % 0.4 px 0.5 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
78 SG-IGEV 1.12 % 1.39 % 0.4 px 0.4 px 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.
79 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.
80 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.
81 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.
82 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.
83 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.
84 MMBStereo 1.15 % 1.55 % 0.4 px 0.5 px 100.00 % 51ms GPU @ 2.0 Ghz (Python)
85 ForeEdge-Stereo 1.15 % 1.47 % 0.4 px 0.5 px 100.00 % 0.38 s GPU @ 2.5 Ghz (Python)
86 ESMStereo-L-gwc code 1.15 % 1.52 % 0.4 px 0.5 px 100.00 % 0.026 s RTX 4070S (Python)
87 HSGC-Stereo 1.17 % 1.52 % 0.4 px 0.5 px 100.00 % 1.36 s 1 core @ 2.5 Ghz (Python)
88 [ICCV 2025] DKT-SMoE code 1.17 % 1.56 % 0.4 px 0.5 px 100.00 % 0.20 s 1 core @ 2.5 Ghz (C/C++)
J. Yun Wang: learning robust stereo matching in the wild with selective mixture-of-experts. arXiv preprint arXiv:2507.04631 2025.
89 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.
90 DSIGA 1.17 % 1.51 % 0.4 px 0.5 px 100.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
91 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.
92 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.
93 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.
94 volume rese 1.19 % 1.56 % 0.4 px 0.5 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
95 GMCR 1.19 % 1.53 % 0.4 px 0.5 px 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
96 HSGC-Stereo(wo SCE) 1.19 % 1.46 % 0.4 px 0.5 px 100.00 % 1.71 s GPU @ 2.5 Ghz (Python)
97 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.
98 lms-stereo 1.20 % 1.60 % 0.4 px 0.5 px 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
99 GHUStereo code 1.21 % 1.61 % 0.4 px 0.5 px 100.00 % 0.036 s RTX 4070 (Python)
M. Tahmasebi, S. Huq, K. Meehan and M. McAfee: GHUStereo: A Lightweight Real-Time Stereo Matching Network with Guided Hourglass Up-Sampling. SSRN Electronic Journal 2025.
100 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.
101 G2L-Stereo 1.22 % 1.57 % 0.4 px 0.5 px 100.00 % 0.05 s GPU @ 1.5 Ghz (Python)
J. Tang, G. Peng, J. Liu and B. Yu: G2L-Stereo: Global to Local Two-Stage Real- Time Stereo Matching Network. IEEE Transactions on Computational Imaging 2025.
102 LLKStereo 1.22 % 1.61 % 0.4 px 0.5 px 100.00 % 0.06 s 1 core @ 2.5 Ghz (Python)
103 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.
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.
104 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.
105 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.
106 Context-Stereo-I 1.26 % 1.66 % 0.4 px 0.5 px 100.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 BANet-3D 1.27 % 1.72 % 0.5 px 0.5 px 100.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
109 NLSDR-Net 1.28 % 1.62 % 0.5 px 0.5 px 100.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
110 RT-IGEV code 1.29 % 1.68 % 0.4 px 0.5 px 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.
111 DCVSMNet code 1.30 % 1.67 % 0.5 px 0.5 px 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.
112 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.
113 CCAStereo 1.31 % 1.64 % 0.5 px 0.5 px 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.
114 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.
115 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.
116 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.
117 LightStereo-H code 1.34 % 1.62 % 0.5 px 0.5 px 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, Y. Zhang, W. Zheng, D. Nie, M. Poggi and L. Chen: Lightstereo: Channel boost is all you need for efficient 2d cost aggregation. ICRA 2025.
118 SG-MSNet3D 1.34 % 1.74 % 0.5 px 0.5 px 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.
119 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.
120 FIA-Net 1.37 % 1.83 % 0.5 px 0.5 px 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
121 CAR-Stereo 1.38 % 1.80 % 0.4 px 0.5 px 100.00 % 0.01 s GPU @ 2.5 Ghz (Python)
C. Park, J. Kim, M. Kweon and J. Park: CAR-Stereo: Confidence-Aware Adaptive Disparity Refinement for Real-Time Stereo Matching. IEEE Robotics and Automation Letters 2026.
122 [TIP25]ADStereo_fast code 1.38 % 1.72 % 0.5 px 0.5 px 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
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.
123 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.
124 BANet-2D 1.38 % 1.79 % 0.5 px 0.5 px 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
125 SG-PSMnet 1.38 % 1.80 % 0.5 px 0.5 px 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.
126 DPDNet_3D 1.38 % 1.75 % 0.4 px 0.5 px 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
J. Liu and Y. Zhang: DPDNet: The lightweight stereo matching network based on disparity probability distribution consistency. Image and Vision Computing 2025.
127 LCA-Stereo 1.39 % 1.78 % 0.5 px 0.5 px 100.00 % 0.03 s NVIDIA RTX 3090 (PyTorch)
128 Context-Stereo code 1.39 % 1.75 % 0.5 px 0.5 px 100.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
129 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.
130 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.
131 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.
132 LXF-Stereo 1.42 % 1.77 % 0.5 px 0.5 px 100.00 % 50 ms GPU @ 2.0 Ghz (Python)
133 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.
134 Fast-ACVNet+ code 1.45 % 1.85 % 0.5 px 0.5 px 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.
135 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.
136 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. .
137 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.
138 G2L-ROB 1.49 % 1.92 % 0.5 px 0.5 px 100.00 % 0.05 s GPU @ 1.0 Ghz (Python)
139 NeXtStereo-M 1.49 % 1.84 % 0.5 px 0.5 px 100.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
140 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.
141 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.
142 GhostStereoNet 1.54 % 1.98 % 0.5 px 0.6 px 100.00 % 0.04 s GPU @ 3.0 Ghz (Python)
143 LightStereo-L code 1.55 % 1.87 % 0.5 px 0.5 px 100.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, Y. Zhang, W. Zheng, D. Nie, M. Poggi and L. Chen: Lightstereo: Channel boost is all you need for efficient 2d cost aggregation. ICRA 2025.
144 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.
145 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.
146 LightStereo-M code 1.56 % 1.91 % 0.5 px 0.5 px 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, Y. Zhang, W. Zheng, D. Nie, M. Poggi and L. Chen: Lightstereo: Channel boost is all you need for efficient 2d cost aggregation. ICRA 2025.
147 NeXtStereo-S 1.57 % 1.93 % 0.5 px 0.6 px 100.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
148 JBFNet2 1.60 % 2.02 % 0.5 px 0.6 px 100.00 % 0.29 s GPU @ 3.0 Ghz (Python)
149 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.
150 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.
151 SG-MSNet2D 1.63 % 2.09 % 0.5 px 0.6 px 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.
152 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.
153 DBCANet code 1.68 % 2.04 % 0.5 px 0.6 px 100.00 % 0.1 s GPU @ 2.5 Ghz (Python)
154 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.
155 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.
156 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.
157 EFSNet 1.76 % 2.17 % 0.5 px 0.6 px 100.00 % 0.01 s GPU @ 2.5 Ghz (Python)
158 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.
159 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.
160 IINet 1.81 % 2.21 % 0.5 px 0.5 px 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
161 LightStereo-S code 1.88 % 2.34 % 0.6 px 0.6 px 100.00 % 0.01 s 1 core @ 2.5 Ghz (Python)
X. Guo, C. Zhang, Y. Zhang, W. Zheng, D. Nie, M. Poggi and L. Chen: Lightstereo: Channel boost is all you need for efficient 2d cost aggregation. ICRA 2025.
162 DPDNet_2D 1.89 % 2.42 % 0.5 px 0.6 px 100.00 % 0.09 s GPU @ 2.5 Ghz (Python)
J. Liu and Y. Zhang: DPDNet: The lightweight stereo matching network based on disparity probability distribution consistency. Image and Vision Computing 2025.
163 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.
164 S³-IGEV-ft 1.92 % 2.31 % 0.5 px 0.6 px 100.00 % 0.18 s 1 core @ 2.5 Ghz (Python)
165 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.
166 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.
167 EFSNet-lite 2.01 % 2.56 % 0.6 px 0.7 px 100.00 % 0.01 s GPU @ 2.5 Ghz (Python)
168 EfficientStereo code 2.03 % 2.52 % 0.6 px 0.7 px 100.00 % 0.015 s NVIDIA RTX 3090 (PyTorch)
J. Tang, J. Liu, S. Ding and others: EfficientStereo: A Real-Time Stereo Matching Approach Using Lightweight Feature Extraction and Disparity-Dimensional Convolution. 2025.
169 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.
170 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. .
171 S³-IGEV 2.17 % 2.60 % 0.6 px 0.6 px 100.00 % 0.18 s 1 core @ 2.5 Ghz (Python)
172 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.
173 GMCR-Stereo 2.21 % 2.78 % 0.6 px 0.7 px 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
174 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.
175 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.
176 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.
177 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.
178 test model 2.34 % 2.70 % 0.7 px 0.7 px 100.00 % .2 s >8 cores @ 3.0 Ghz (C/C++)
179 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.
180 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.
181 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.
182 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 .
183 cfusion
This method makes use of multiple (>2) views.
 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.
184 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.
185 RoSe 2.55 % 3.17 % 0.7 px 0.8 px 100.00 % 0.17 s 1 core @ 2.5 Ghz (C/C++)
186 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.
187 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).
188 Syn2Real Stereo 2.64 % 3.14 % 0.7 px 0.7 px 100.00 % 0.28 s 1 core @ 2.5 Ghz (C/C++)
189 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. .
190 S³-IGEV-zeroshot 2.72 % 3.27 % 0.7 px 0.8 px 100.00 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
191 PRSM
This method uses optical flow information.
This method makes use of multiple (>2) views.
 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.
192 DualNet (step 1) code 2.82 % 3.45 % 0.7 px 0.8 px 100.00 % 0.17 s 1 core @ 2.5 Ghz (C/C++)
Y. Wang, J. Zheng, C. Zhang, Z. Zhang, K. Li, Y. Zhang and J. Hu: DualNet: Robust Self-Supervised Stereo Matching with Pseudo-Label Supervision. Proceedings of the AAAI Conference on Artificial Intelligence 2025.
193 SPS-StFl
This method uses optical flow information.
This method makes use of the epipolar geometry.
 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.
194 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.
195 VC-SF
This method uses optical flow information.
This method makes use of multiple (>2) views.
 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.
196 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.
197 Unister code 3.08 % 3.62 % 0.7 px 0.8 px 100.00 % 0.56 s 1 core @ 2.5 Ghz (C/C++)
198 Pseudo-Stereo 3.08 % 3.62 % 0.7 px 0.8 px 100.00 % 0.45 s 1 core @ 2.5 Ghz (Python)
199 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.
200 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.
201 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.
202 OSF
This method uses optical flow information.
 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.
203 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.
204 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.
205 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.
206 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.
207 P3SNet+ code 3.55 % 4.42 % 0.8 px 0.9 px 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.
208 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.
209 ReaSMNet 3.63 % 4.22 % 0.8 px 0.9 px 100.00 % 0.01 s GPU @ 2.5 Ghz (Python)
210 P3SNet code 3.65 % 4.46 % 0.9 px 1.0 px 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.
211 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.
212 Un-ViTAStereo 3.84 % 4.55 % 0.8 px 0.9 px 100.00 % 0.22 s GPU @ 2.5 Ghz (Python)
213 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.
214 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.
215 PR-Sf+E
This method uses optical flow information.
 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.
216 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.
217 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.
218 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.
219 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.
220 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.
221 PR-Sceneflow
This method uses optical flow information.
 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.
222 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.
223 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.
224 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.
225 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.
226 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.
227 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.
228 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.
229 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.
230 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.
231 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.
232 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.
233 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.
234 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.
235 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.
236 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.
237 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.
238 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.
239 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.
240 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.
241 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.
242 UHP 6.05 % 7.09 % 1.2 px 1.3 px 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
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.
243 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.
244 Toast2
This method uses stereo information.
 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.
245 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.
246 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.
247 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.
248 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.
249 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.
250 MSMW
This method uses stereo information.
 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.
251 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.
252 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.
253 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.
254 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 .
255 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.
256 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.
257 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.
258 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.
259 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.
260 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.
261 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.
262 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.
263 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.
264 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.
265 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.
266 GCSF
This method uses optical flow information.
 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.
267 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.
268 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.
269 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.
270 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.
271 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.
272 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.
273 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.
274 MEDIAN 52.61 % 53.67 % 7.7 px 8.2 px 99.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
275 AVERAGE 61.62 % 62.49 % 8.0 px 8.6 px 99.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
276 EN 97.97 % 97.94 % 35.6 px 35.7 px 99.99 % 1.71 s GPU @ 2.5 Ghz (Python)
This table as LaTeX


Related Datasets

  • HCI/Bosch Robust Vision Challenge: Optical flow and stereo vision challenge on high resolution imagery recorded at a high frame rate under diverse weather conditions (e.g., sunny, cloudy, rainy). The Robert Bosch AG provides a prize for the best performing method.
  • Image Sequence Analysis Test Site (EISATS): Synthetic image sequences with ground truth information provided by UoA and Daimler AG. Some of the images come with 3D range sensor information.
  • Middlebury Stereo Evaluation: The classic stereo evaluation benchmark, featuring four test images in version 2 of the benchmark, with very accurate ground truth from a structured light system. 38 image pairs are provided in total.
  • Daimler Stereo Dataset: Stereo bad weather highway scenes with partial ground truth for freespace
  • Make3D Range Image Data: Images with small-resolution ground truth used to learn and evaluate depth from single monocular images.
  • Lubor Ladicky's Stereo Dataset: Stereo Images with manually labeled ground truth based on polygonal areas.

Citation

When using this dataset in your research, we will be happy if you cite us:
@inproceedings{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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