Stereo Evaluation 2015


The stereo 2015 / flow 2015 / scene flow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks, it comprises dynamic scenes for which the ground truth has been established in a semi-automatic process. Our evaluation server computes the percentage of bad pixels averaged over all ground truth pixels of all 200 test images. For this benchmark, we consider a pixel to be correctly estimated if the disparity or flow end-point error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity maps and the flow map). 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. More details can be found in Object Scene Flow for Autonomous Vehicles (CVPR 2015).

Our evaluation table ranks all methods according to the number of erroneous pixels. 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. Legend:

  • D1: Percentage of stereo disparity outliers in first frame
  • D2: Percentage of stereo disparity outliers in second frame
  • Fl: Percentage of optical flow outliers
  • SF: Percentage of scene flow outliers (=outliers in either D0, D1 or Fl)
  • bg: Percentage of outliers averaged only over background regions
  • fg: Percentage of outliers averaged only over foreground regions
  • all: Percentage of outliers averaged over all ground truth pixels


Note: On 13.03.2017 we have fixed several small errors in the flow (noc+occ) ground truth of the dynamic foreground objects and manually verified all images for correctness by warping them according to the ground truth. As a consequence, all error numbers have decreased slightly. Please download the devkit and the annotations with the improved ground truth for the training set again if you have downloaded the files prior to 13.03.2017 and consider reporting these new number in all future publications. The last leaderboards before these corrections can be found here (optical flow 2015) and here (scene flow 2015). The leaderboards for the KITTI 2015 stereo benchmarks did not change.

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)

Evaluation ground truth        Evaluation area

Method Setting Code D1-bg D1-fg D1-all Density Runtime Environment
1 UPFNet 1.38 % 2.85 % 1.62 % 100.00 % 0.25 s 1 core @ 2.5 Ghz (Python)
2 Astar code 1.36 % 3.09 % 1.65 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
3 M-FUSE
This method uses optical flow information.
This method makes use of multiple (>2) views.
1.40 % 2.91 % 1.65 % 100.00 % 1.3 s GPU
4 SF2SE3
This method uses optical flow information.
1.40 % 2.91 % 1.65 % 100.00 % 2.7 s GPU @ >3.5 Ghz (Python)
5 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.
6 ACVNet code 1.37 % 3.07 % 1.65 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
G. Xu, J. Cheng, P. Guo and X. Yang: Attention Concatenation Volume for Accurate and Efficient Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
7 ICG-ACV code 1.31 % 3.37 % 1.65 % 100.00 % 0.25 s 1 core @ 2.5 Ghz (Python)
8 PCWNet 1.37 % 3.16 % 1.67 % 100.00 % 0.44 s 1 core @ 2.5 Ghz (C/C++)
9 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.
10 GwcNet-DCA 1.43 % 2.91 % 1.68 % 100.00 % 0.24 s GPU @ 2.5 Ghz (Python)
11 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.
12 DuMa-Net 1.40 % 3.18 % 1.70 % 100.00 % 0.38 s PyTorch GPU
13 Gwc_CoAtRS 1.39 % 3.25 % 1.70 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
14 ICG-Gwc 1.38 % 3.34 % 1.71 % 100.00 % 0.26 s 1 core @ 2.5 Ghz (Python)
15 ACVNet_small 1.41 % 3.20 % 1.71 % 100.00 % 0.24 s 1 core @ 2.5 Ghz (C/C++)
16 DMCA 1.45 % 2.99 % 1.71 % 100.00 % 0.28 s 1 core @ 2.5 Ghz (Python)
17 CFNet-RSSM 1.46 % 3.00 % 1.72 % 100.00 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
18 UCFNet code 1.45 % 3.07 % 1.72 % 100.00 % 0.24 s 1 core @ 2.5 Ghz (C/C++)
19 UFnet 1.42 % 3.24 % 1.72 % 100.00 % 0.23 s 1 core @ 2.5 Ghz (Python)
20 CF_CoAtRS 1.41 % 3.31 % 1.73 % 100.00 % 0.24 s 1 core @ 2.5 Ghz (Python)
21 ADLNet 1.44 % 3.25 % 1.74 % 100.00 % 0.43 s GPU @ 1.5 Ghz (Python)
22 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.
23 CVCNet 1.45 % 3.22 % 1.74 % 100.00 % 0.074 s pytorch GPU
24 DSNet 1.44 % 3.31 % 1.75 % 100.00 % 0.36 s 1 core @ 2.5 Ghz (C/C++)
25 DSFCA 1.52 % 2.90 % 1.75 % 100.00 % 0.31 s 1 core @ 2.5 Ghz (Python)
26 RDNet 1.44 % 3.31 % 1.75 % 100.00 % 0.65 s 1 core @ 2.5 Ghz (Python)
27 DMLMBCVNet 1.48 % 3.15 % 1.76 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
28 MDANet 1.47 % 3.18 % 1.76 % 100.00 % 0.43 s 1 core @ 2.5 Ghz (C/C++)
29 GwcNet+ 1.41 % 3.50 % 1.76 % 100.00 % 0.32 s GPU @ 2.5 Ghz (Python)
30 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.
31 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 .
32 PSM_CoAtRS 1.44 % 3.44 % 1.77 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
33 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.
34 ACVNet 1.46 % 3.36 % 1.77 % 100.00 % 0.43 s 1 core @ 2.5 Ghz (Python)
35 AFCNet 1.44 % 3.47 % 1.78 % 100.00 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
36 HFMANet 1.47 % 3.30 % 1.78 % 100.00 % 0.56 s 1 core @ 2.5 Ghz (C/C++)
37 HFRANet 1.47 % 3.30 % 1.78 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
38 SSA 1.46 % 3.40 % 1.78 % 100.00 % 0.40 s 1 core @ 2.5 Ghz (Python)
39 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.
40 AGNet 1.45 % 3.51 % 1.79 % 100.00 % 0.43 s 1 core @ 2.5 Ghz (Python)
41 ICG-PSM 1.46 % 3.47 % 1.80 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
42 DADCA code 1.52 % 3.21 % 1.80 % 100.00 % 0.28 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 FENet 1.44 % 3.64 % 1.80 % 100.00 % 0.4 s 8 core @ 2.5 Ghz (Python)
45 CVP-AAR 1.51 % 3.29 % 1.81 % 100.00 % 0.15 s GPU @ 2.5 Ghz (C/C++)
46 TemporalStereo(M)
This method makes use of multiple (>2) views.
1.61 % 2.78 % 1.81 % 100.00 % 0.048 s GPU @ 2.5 Ghz (Python)
47 Binary TTC
This method uses optical flow information.
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.
48 CamLiFlow++
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
49 Scale-flow-split
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 1.6 s GPU 2.5GHZ
50 optical_flow3D
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
51 Scale flow
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 0.8 s GPU @ 2.5 Ghz (Python)
52 RAFT-3D
This method uses optical flow information.
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.
53 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.
54 CamLiFlow
This method uses optical flow information.
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.
55 Stereo expansion
This method uses optical flow information.
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.
56 TPOF
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 0.2 s 1 core 2.5ghz gpu
57 GMISF
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
58 EAI-Stereo 1.59 % 2.92 % 1.81 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python + C/C++)
59 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.
60 MMNet 1.58 % 3.06 % 1.83 % 100.00 % 0.13 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 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.
63 HCRNet 1.51 % 3.51 % 1.85 % 100.00 % 0.19 s 1 core @ 2.5 Ghz (Python)
64 DCANet 1.54 % 3.37 % 1.85 % 100.00 % 0.10 s 1 core @ 2.5 Ghz (Python)
65 PSMNet+ 1.52 % 3.53 % 1.86 % 100.00 % 0.41 s GPU @ 2.5 Ghz (Python)
66 MLA 1.53 % 3.49 % 1.86 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
67 ACV-Net 1.62 % 3.08 % 1.86 % 100.00 % 0.46 s 1 core @ 2.5 Ghz (C/C++)
68 CTNet 1.51 % 3.67 % 1.87 % 100.00 % 0.4 s 8 cores @ 2.5 Ghz (C/C++)
69 PSMNet-DCA 1.59 % 3.33 % 1.88 % 100.00 % 0.34 s 1 core @ 2.5 Ghz (Python)
70 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.
71 UCNet 1.57 % 3.47 % 1.88 % 100.00 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
72 RigidMask+ISF
This method uses optical flow information.
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.
73 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.
74 sCroCo code 1.73 % 2.76 % 1.90 % 100.00 % 1.1 s 1 core @ 2.5 Ghz (Python)
75 SPNet 1.71 % 2.92 % 1.91 % 100.00 % 0.1 s GPU @ 2.5 Ghz (Python)
76 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.
77 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.
78 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.
79 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.
80 sanet 1.64 % 3.41 % 1.93 % 100.00 % 0.30 s 1 core @ 2.5 Ghz (Python + C/C++)
81 AAPNet 1.64 % 3.46 % 1.94 % 100.00 % 0.08 s 1 core @ 2.5 Ghz (Python)
82 ICG-Stereo 1.66 % 3.38 % 1.94 % 100.00 % 0.02 s NVIDIA RTX 3090 (PyTorch)
83 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.
84 SMGWCnet code 1.67 % 3.37 % 1.95 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 gwcnet+DCA 1.57 % 3.91 % 1.96 % 100.00 % 0.32 s 1 core @ 2.5 Ghz (Python)
86 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.
87 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.
88 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.
89 LSAnet 1.65 % 3.58 % 1.97 % 100.00 % 0.36 s 1 core @ 2.5 Ghz (C/C++)
90 CGFNet code 1.71 % 3.36 % 1.98 % 100.00 % 0.03 s GPU @ >3.5 Ghz (Python)
91 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.
92 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.
93 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.
94 Fast-ACV+ code 1.70 % 3.53 % 2.01 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
95 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.
96 AAP+ 1.68 % 3.74 % 2.02 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
97 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.
98 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.
99 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.
100 TemporalStereo(S) 1.89 % 2.85 % 2.05 % 100.00 % 0.044 s 1 core @ 2.5 Ghz (C/C++)
101 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.
102 spsm2 1.69 % 4.01 % 2.08 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
103 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.
104 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.
105 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.
106 multi attention 1.71 % 4.03 % 2.10 % 100.00 % 1.60 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 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.
109 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.
110 DeepPruner14 1.85 % 3.42 % 2.11 % 100.00 % 500 s 1 core @ 2.5 Ghz (C/C++)
111 CGNet code 1.81 % 3.68 % 2.12 % 100.00 % 0.03 s GPU @ >3.5 Ghz (Python)
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112 Fast-ACV code 1.82 % 3.63 % 2.12 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
113 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.
114 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.
115 SMCFnet code 1.79 % 3.90 % 2.14 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
116 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.
117 CGF-F-B 1.75 % 4.20 % 2.16 % 100.00 % 0.26 s GPU @ 2.5 Ghz (Python)
118 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.
119 SMDeeppruner code 1.82 % 3.96 % 2.17 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 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.
121 BG22 1.81 % 4.04 % 2.18 % 100.00 % 500 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
122 MLA 1.71 % 4.54 % 2.18 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
123 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.
124 EBNet 1.83 % 3.98 % 2.19 % 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
125 DFL-CCA-Net 1.81 % 4.13 % 2.19 % 100.00 % 0.49 s Nvidia Titan Xp
126 PSMNet-edge 1.82 % 4.11 % 2.20 % 100.00 % 0.52 s 1 core @ 2.5 Ghz (C/C++)
127 SS 1.84 % 4.05 % 2.20 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
128 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.
129 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.
130 LWNet 1.87 % 3.97 % 2.22 % 100.00 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
131 SENSE
This method uses optical flow information.
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.
132 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.
133 PSMNet_FE 1.84 % 4.32 % 2.25 % 100.00 % 0.17 s 1 core @ 2.5 Ghz (C/C++)
134 DTF_SENSE
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
135 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.
136 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.
137 Separable Convs code 1.90 % 4.36 % 2.31 % 100.00 % 2 s GPU @ 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.
138 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.
139 CGNet code 1.96 % 4.12 % 2.32 % 100.00 % 0.03 s GPU @ >3.5 Ghz (Python)
140 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.
141 MSSF code 1.92 % 4.36 % 2.33 % 100.00 % 0.14 s 1 core @ 2.5 Ghz (Python)
142 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.
143 ACVNet-fast code 1.87 % 4.68 % 2.34 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
144 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.
145 APVNet 1.94 % 4.42 % 2.35 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
146 Multi_Stereo 1.92 % 4.74 % 2.39 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
147 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. .
148 APNet 2.05 % 4.35 % 2.43 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
149 MVFNet 2.00 % 4.60 % 2.43 % 100.00 % 0.02 s GPU @ >3.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
150 MMM 2.11 % 4.30 % 2.48 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
151 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.
152 BGNet code 2.16 % 4.24 % 2.50 % 100.00 % 0.02 s GPU @ >3.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
153 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.
154 ED-Net 1.97 % 5.38 % 2.53 % 100.00 % 0.36 s 1 core @ 2.5 Ghz (C/C++)
155 UberATG-DRISF
This method uses optical flow information.
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.
156 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.
157 MVFNet 2.10 % 4.82 % 2.55 % 100.00 % 0.02 s GPU @ >3.5 Ghz (C/C++)
158 stroco code 2.35 % 3.70 % 2.58 % 100.00 % 1.3 s 1 core @ 2.5 Ghz (Python)
159 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.
160 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.
161 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.
162 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. .
163 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.
164 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.
165 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.
166 Multi_Stereo 2.12 % 5.31 % 2.65 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
167 Syn2CoEx code 2.19 % 5.04 % 2.67 % 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
168 OURNET 2.11 % 5.45 % 2.67 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
169 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.
170 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.
171 EASNet 2.34 % 4.51 % 2.70 % 100.00 % 0.1 s GPU @ 1.5 Ghz (Python)
172 OURNET 2.14 % 5.54 % 2.71 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
173 OURNET 2.22 % 5.18 % 2.71 % 100.00 % 0.16 s 2 cores @ 2.5 Ghz (Python)
174 FANET_L1 code 2.68 % 3.29 % 2.78 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
175 LJTE 2.15 % 5.97 % 2.79 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
176 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.
177 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.
178 EASNet-M 2.40 % 5.37 % 2.89 % 100.00 % 0.8 s GPU @ 1.5 Ghz (Python)
179 BGNet code 2.51 % 4.88 % 2.90 % 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
180 COTEM 2.28 % 6.11 % 2.92 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
181 TSv1 2.49 % 5.37 % 2.97 % 100.00 % 0.022 s GPU @ (Python)
182 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.
183 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.
184 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).
185 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.
X. Song, G. Yang, X. Zhu, H. Zhou, Y. Ma, Z. Wang and J. Shi: AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach. IJCV 2021.
186 fca 2.55 % 5.75 % 3.09 % 100.00 % 0.12 s 1 core @ 2.5 Ghz (Python)
187 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.
188 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.
189 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.
190 DWARF
This method uses optical flow information.
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.
191 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.
192 BGNet code 2.99 % 5.50 % 3.41 % 100.00 % 0.25 s GPU @ >3.5 Ghz (Python)
193 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.
194 EASNet-S 2.89 % 6.12 % 3.43 % 100.00 % 0.6 s GPU @ 1.5 Ghz (Python)
195 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.
196 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.
197 ACOSF
This method uses optical flow information.
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.
198 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.
199 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.
200 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.
201 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.
202 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.
203 RT+AANet 3.03 % 7.40 % 3.76 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
204 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. .
205 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 .
206 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.
207 KDAT code 3.27 % 7.48 % 3.97 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
208 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.
209 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.
210 PRSM
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
211 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.
212 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.
213 SSF
This method uses optical flow information.
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.
214 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.
215 ISF
This method uses optical flow information.
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.
216 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.
217 ATSA_net 3.71 % 9.19 % 4.62 % 100.00 % 0.07 s 1 core @ 2.5 Ghz (Python)
218 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.
219 Self-SuperFlow-ft
This method uses optical flow information.
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. 2022.
220 DTF_PWOC
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
221 LEAT1 3.94 % 9.29 % 4.83 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
222 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.
223 LEATS 4.46 % 6.84 % 4.85 % 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
224 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.
225 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 .
226 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.
227 OSF+TC
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
228 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.
229 PWOC-3D
This method uses optical flow information.
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.
230 NLTS 4.49 % 8.82 % 5.21 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
231 OSF 2018
This method uses optical flow information.
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.
232 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.
233 sRRNet code 4.44 % 9.79 % 5.33 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
234 SBP code 3.85 % 12.81 % 5.34 % 99.64 % 16ms s 1 core @ 2.5 Ghz (C/C++)
235 MDP
This method uses stereo information.
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.
236 BSF
This method uses optical flow information.
4.09 % 11.86 % 5.39 % 100.00 % 162 s 1 core @ 2.5 Ghz (Matlab)
237 SFF++
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
238 OSF
This method uses optical flow information.
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.
239 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.
240 CSF
This method uses optical flow information.
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.
241 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.
242 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.
243 PR-Sceneflow
This method uses optical flow information.
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.
244 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.
245 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.
246 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.
247 SceneFFields
This method uses optical flow information.
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.
248 SPS+FF++
This method uses optical flow information.
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.
249 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.
250 FSF+MS
This method uses optical flow information.
This method makes use of the epipolar geometry.
This method makes use of multiple (>2) views.
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.
251 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.
252 SGM+C+NL
This method uses optical flow information.
code 5.15 % 15.29 % 6.84 % 100.00 % 4.5 min 1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them. IJCV 2013.
253 SGM+LDOF
This method uses optical flow information.
code 5.15 % 15.29 % 6.84 % 100.00 % 86 s 1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
T. Brox and J. Malik: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. PAMI 2011.
254 SGM+SF
This method uses optical flow information.
5.15 % 15.29 % 6.84 % 100.00 % 45 min 16 core @ 3.2 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
M. Hornacek, A. Fitzgibbon and C. Rother: SphereFlow: 6 DoF Scene Flow from RGB-D Pairs. CVPR 2014.
255 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.
256 PASM + Permutation 5.31 % 16.52 % 7.17 % 100.00 % 30 s GPU @ 2.5 Ghz (Matlab)
257 Permutation Stereo 5.53 % 15.47 % 7.18 % 99.93 % 30 s GPU @ 2.5 Ghz (Matlab)
258 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.
259 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.
260 Z2ZNCC 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.
261 ReS2tAC
This method uses stereo information.
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.
262 Self-SuperFlow
This method uses optical flow information.
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. 2022.
263 ASDNET 6.55 % 15.94 % 8.11 % 100.00 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
264 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.
265 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.
266 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.
267 PCOF + ACTF
This method uses optical flow information.
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.
268 PCOF-LDOF
This method uses optical flow information.
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.
269 ASMM 7.10 % 16.44 % 8.65 % 100.00 % 0.3 s GPU @ 2.5 Ghz (C/C++)
270 OASM-Net 6.89 % 19.42 % 8.98 % 100.00 % 0.73 s GPU @ 2.5 Ghz (Python)
A. Li and Z. Yuan: Occlusion Aware Stereo Matching via Cooperative Unsupervised Learning. Proceedings of the Asian Conference on Computer Vision, ACCV 2018.
271 ELAS_RVC code 7.38 % 21.15 % 9.67 % 100.00 % 0.19 s 4 cores @ >3.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.
272 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.
273 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.
274 iGF
This method makes use of multiple (>2) views.
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.
275 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.
276 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.
277 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.
278 SGM&FlowFie+
This method uses optical flow information.
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.
279 GCSF
This method uses optical flow information.
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.
280 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.
281 RAFT-MSF-ft
This method uses optical flow information.
14.19 % 24.79 % 15.95 % 100.00 % 0.18 s 1 core @ 2.5 Ghz (Python)
282 Mono-SF
This method uses optical flow information.
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.
283 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.
284 MonoComb
This method uses optical flow information.
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.
285 DWBSF
This method uses optical flow information.
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.
286 RAFT-MSF
This method uses optical flow information.
18.10 % 36.82 % 21.21 % 100.00 % 0.18 s NVIDIA GTX 1080 Ti
287 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.
288 Self-Mono-SF-ft
This method uses optical flow information.
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.
289 Multi-Mono-SF-ft
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
290 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.
291 VSF
This method uses optical flow information.
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.
292 RealtimeStereo code 26.26 % 28.84 % 26.69 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
293 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.
294 Multi-Mono-SF
This method uses optical flow information.
This method makes use of multiple (>2) views.
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.
295 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.
296 Self-Mono-SF
This method uses optical flow information.
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.
297 RLSSF
This method uses optical flow information.
37.84 % 54.56 % 40.62 % 16.51 % 1.2 s 8 cores @ 3.2 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.
298 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.
299 HNRLSSF
This method uses optical flow information.
53.78 % 69.28 % 56.36 % 10.52 % 2.5 s 1 core @ 2.5 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.
300 RLPSSF
This method uses optical flow information.
56.60 % 73.05 % 59.34 % 9.26 % 2.5 s 8 core @ 2.5 Ghz (Matlab)
A. Erfan salehi and R. hoseuni: Real-time Low complexity Precision Sparse Scene-flow. International journal of computer vision 2022.
301 PRLSSF
This method uses optical flow information.
58.62 % 75.07 % 61.36 % 6.96 % 1.5 s 8 cores @ 2.5 Ghz (Matlab) and (C++)
ERROR: Wrong syntax in BIBTEX file.
Table as LaTeX | Only published Methods




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.
  • Middlebury Optical Flow Evaluation: The classic optical flow evaluation benchmark, featuring eight test images, with very accurate ground truth from a shape from UV light pattern system. 24 image pairs are provided in total.

Citation

When using this dataset in your research, we will be happy if you cite us:
@ARTICLE{Menze2018JPRS,
  author = {Moritz Menze and Christian Heipke and Andreas Geiger},
  title = {Object Scene Flow},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing (JPRS)},
  year = {2018}
}
@INPROCEEDINGS{Menze2015ISA,
  author = {Moritz Menze and Christian Heipke and Andreas Geiger},
  title = {Joint 3D Estimation of Vehicles and Scene Flow},
  booktitle = {ISPRS Workshop on Image Sequence Analysis (ISA)},
  year = {2015}
}



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