\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf D1-bg} & {\bf D1-fg} & {\bf D1-all} & {\bf Density} & {\bf Runtime} & {\bf Environment}\\ \hline
GANet+ADL & & 1.38 \% & 2.40 \% & 1.55 \% & 100.00 \% & 1.8 s / GPU & \\
RCA-Stereo & & 1.36 \% & 2.51 \% & 1.55 \% & 100.00 \% & 0.40 s / 1 core & \\
IGEV-Stereo & & 1.38 \% & 2.67 \% & 1.59 \% & 100.00 \% & 0.18 s / & G. Xu, X. Wang, X. Ding and X. Yang: Iterative Geometry Encoding Volume for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.\\
CGF-ACV & & 1.32 \% & 3.08 \% & 1.61 \% & 100.00 \% & 0.24 s / & \\
UPFNet & & 1.38 \% & 2.85 \% & 1.62 \% & 100.00 \% & 0.25 s / 1 core & \\
UDG & & 1.39 \% & 2.88 \% & 1.64 \% & 100.00 \% & 0.4 s / 1 core & \\
DiffuVolume & & 1.39 \% & 2.93 \% & 1.65 \% & 100.00 \% & 0.36 s / GPU & \\
ERNet & & 1.36 \% & 3.09 \% & 1.65 \% & 100.00 \% & 0.2 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
M-FUSE & fl mv & 1.40 \% & 2.91 \% & 1.65 \% & 100.00 \% & 1.3 s / & L. Mehl, A. Jahedi, J. Schmalfuss and A. Bruhn: M-FUSE: Multi-frame Fusion for Scene Flow Estimation. Proc. Winter Conference on Applications of Computer Vision (WACV) 2023.\\
SF2SE3 & fl & 1.40 \% & 2.91 \% & 1.65 \% & 100.00 \% & 2.7 s / GPU & L. Sommer, P. Schröppel and T. Brox: SF2SE3: Clustering Scene Flow into SE (3)-Motions via Proposal and Selection. DAGM German Conference on Pattern Recognition 2022.\\
LEAStereo & & 1.40 \% & 2.91 \% & 1.65 \% & 100.00 \% & 0.30 s / GPU & 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.\\
ACVNet & & 1.37 \% & 3.07 \% & 1.65 \% & 100.00 \% & 0.2 s / & G. Xu, J. Cheng, P. Guo and X. Yang: Attention Concatenation Volume for Accurate and Efficient Stereo Matching. CVPR 2022.\\
ACVNet (v2) & & 1.31 \% & 3.37 \% & 1.65 \% & 100.00 \% & 0.2 s / 1 core & \\
DCANet & & 1.42 \% & 2.91 \% & 1.66 \% & 100.00 \% & 0.18 s / & \\
PCWNet & & 1.37 \% & 3.16 \% & 1.67 \% & 100.00 \% & 0.44 s / 1 core & 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.\\
LaC+GANet & & 1.44 \% & 2.83 \% & 1.67 \% & 100.00 \% & 1.8 s / GPU & 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.\\
GwcNet-DCA & & 1.43 \% & 2.91 \% & 1.68 \% & 100.00 \% & 0.24 s / GPU & \\
GwcNet+ADL & & 1.42 \% & 3.01 \% & 1.68 \% & 100.00 \% & 0.32 s / GPU & \\
CREStereo & & 1.45 \% & 2.86 \% & 1.69 \% & 100.00 \% & 0.41 s / GPU & 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.\\
DN + GANet & & 1.42 \% & 3.03 \% & 1.69 \% & 100.00 \% & 1.8 s / 1 core & \\
GU & & 1.42 \% & 3.05 \% & 1.69 \% & 100.00 \% & 1 s / GPU & \\
DuMa-Net & & 1.40 \% & 3.18 \% & 1.70 \% & 100.00 \% & 0.38 s / & S. Sun, R. liu and S. Sun: Range-free disparity estimation with self- adaptive dual-matching. IET Computer Vision .\\
IER-Net-W & & 1.40 \% & 3.20 \% & 1.70 \% & 100.00 \% & 0.48 s / 1 core & \\
CGF-Gwc & & 1.38 \% & 3.34 \% & 1.71 \% & 100.00 \% & 0.26 s / 1 core & \\
CroCo-Stereo & & 1.54 \% & 2.58 \% & 1.72 \% & 100.00 \% & 0.93s / GPU & \\
CFNet-RSSM & & 1.46 \% & 3.00 \% & 1.72 \% & 100.00 \% & 0.15 s / 1 core & \\
AGCVNet & & 1.44 \% & 3.12 \% & 1.72 \% & 100.00 \% & 0.66 s / 1 core & \\
UCFNet & & 1.45 \% & 3.07 \% & 1.72 \% & 100.00 \% & 0.21 s / 1 core & \\
HGRNet & & 1.39 \% & 3.34 \% & 1.72 \% & 100.00 \% & 0.41 s / 1 core & \\
PMS++ & & 1.55 \% & 2.71 \% & 1.74 \% & 100.00 \% & 0.2 s / 1 core & \\
PSMNet+ADL & & 1.44 \% & 3.25 \% & 1.74 \% & 100.00 \% & 0.41 s / GPU & \\
CSPN & & 1.51 \% & 2.88 \% & 1.74 \% & 100.00 \% & 1.0 s / GPU & 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.\\
DSNet & & 1.44 \% & 3.31 \% & 1.75 \% & 100.00 \% & 0.36 s / 1 core & \\
ASNet & & 1.45 \% & 3.27 \% & 1.75 \% & 100.00 \% & 0.17 s / GPU & \\
RDNet & & 1.44 \% & 3.31 \% & 1.75 \% & 100.00 \% & 0.60 s / 1 core & \\
DMLMBCVNet & & 1.48 \% & 3.15 \% & 1.76 \% & 100.00 \% & 0.3 s / 1 core & \\
MDANet & & 1.47 \% & 3.18 \% & 1.76 \% & 100.00 \% & 0.43 s / 1 core & \\
GMOStereo & & 1.60 \% & 2.54 \% & 1.76 \% & 100.00 \% & 0.30 s / GPU & \\
DLNR & & 1.60 \% & 2.59 \% & 1.76 \% & 100.00 \% & ~0.3 s / GPU & \\
LaC+GwcNet & & 1.43 \% & 3.44 \% & 1.77 \% & 100.00 \% & 0. 65 s / GPU & 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.\\
GMStereo & & 1.49 \% & 3.14 \% & 1.77 \% & 100.00 \% & 0.17 s / & H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao and A. Geiger: Unifying Flow, Stereo and Depth Estimation. arXiv preprint arXiv:2211.05783 2022.\\
CFNet+PPF & & 1.47 \% & 3.26 \% & 1.77 \% & 100.00 \% & 0.22 s / 1 core & \\
ATStereo-v2 & & 1.59 \% & 2.65 \% & 1.77 \% & 100.00 \% & 0.2 s / 1 core & \\
ATStereo\_v3 & & 1.57 \% & 2.74 \% & 1.77 \% & 100.00 \% & 0.1 s / 1 core & \\
NLCA-Net v2 & & 1.41 \% & 3.56 \% & 1.77 \% & 100.00 \% & 0.67 s / GPU & Z. Rao, D. Yuchao, S. Zhelun and H. Renjie: Rethinking Training Strategy in Stereo Matching. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS .\\
GANet+DSMNet & & 1.48 \% & 3.23 \% & 1.77 \% & 100.00 \% & 2.0 s / GPU & 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.\\
HFRANet & & 1.47 \% & 3.30 \% & 1.78 \% & 100.00 \% & 0.05 s / 1 core & \\
EAI-Stereo & & 1.63 \% & 2.55 \% & 1.78 \% & 100.00 \% & 0.3 s / 1 core & \\
AA-Flow-v2 & & 1.61 \% & 2.67 \% & 1.78 \% & 100.00 \% & 0.2 s / 1 core & \\
PFSMNet & & 1.54 \% & 3.02 \% & 1.79 \% & 100.00 \% & 0.31 s / 1 core & K. Zeng, Y. Wang, Q. Zhu, J. Mao and H. Zhang: Deep Progressive Fusion Stereo Network. IEEE Transactions on Intelligent Transportation Systems 2021.\\
CGF-PSM & & 1.46 \% & 3.47 \% & 1.80 \% & 100.00 \% & 0.3 s / 1 core & \\
SUW-Stereo & & 1.47 \% & 3.45 \% & 1.80 \% & 100.00 \% & 1.8 s / 1 core & 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.\\
CVP-AAR & & 1.51 \% & 3.29 \% & 1.81 \% & 100.00 \% & 0.15 s / GPU & \\
TemporalStereo & mv & 1.61 \% & 2.78 \% & 1.81 \% & 100.00 \% & 0.04 s / 1 core & \\
Binary TTC & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 2 s / GPU & 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.\\
optical\_flow3D & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / 1 core & \\
CamLiRAFT & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1 s / GPU & H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion. arXiv preprint arXiv:2303.12017 2023.\\
SFG & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / 1 core & \\
Scale-flow & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.8 s / GPU & H. Ling, Q. Sun, Z. Ren, Y. Liu, H. Wang and Z. Wang: Scale-flow: Estimating 3D Motion from Video. Proceedings of the 30th ACM International Conference on Multimedia 2022.\\
RAFT3D+mscv & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / 1 core & \\
CamLiRAFT-NR & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1 s / GPU & H. Liu, T. Lu, Y. Xu, J. Liu and L. Wang: Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion. arXiv preprint arXiv:2303.12017 2023.\\
TPCV+RAFT3D & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / 1 core & \\
RAFT-3D & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 2 s / GPU & Z. Teed and J. Deng: RAFT-3D: Scene Flow using Rigid-Motion Embeddings. arXiv preprint arXiv:2012.00726 2020.\\
ScaleRAFT3D & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1 s / 1 core & \\
GANet-deep & & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1.8 s / GPU & 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.\\
CamLiFlow & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1.2 s / GPU & 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.\\
Stereo expansion & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 2 s / GPU & G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow through Optical Expansion. CVPR 2020.\\
TPCV+RAFT & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / & \\
GMISF & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / GPU & \\
AA-Flow & & 1.66 \% & 2.64 \% & 1.82 \% & 100.00 \% & 0.1 s / 1 core & \\
GwcNet+PPF & & 1.53 \% & 3.27 \% & 1.82 \% & 100.00 \% & 0.3 s / 1 core & \\
OptStereo & & 1.50 \% & 3.43 \% & 1.82 \% & 100.00 \% & 0.10 s / GPU & 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.\\
RAFT-Stereo & & 1.58 \% & 3.05 \% & 1.82 \% & 100.00 \% & 0.38 s / 1 core & \\
raft+\_RVC & & 1.60 \% & 2.98 \% & 1.83 \% & 100.00 \% & 0.44 s / & \\
RCGSNP & & 1.56 \% & 3.17 \% & 1.83 \% & 100.00 \% & 0.12 s / GPU & \\
pmstereo & & 1.63 \% & 2.82 \% & 1.83 \% & 100.00 \% & 0.15 s / 1 core & \\
NLCA-Net-3 & & 1.45 \% & 3.78 \% & 1.83 \% & 100.00 \% & 0.44 s / >8 cores & 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.\\
OAGNet18 & & 1.71 \% & 2.51 \% & 1.84 \% & 100.00 \% & 0.4 s / GPU & \\
AMNet & & 1.53 \% & 3.43 \% & 1.84 \% & 100.00 \% & 0.9 s / GPU & X. Du, M. El-Khamy and J. Lee: AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks. 2019.\\
HCRNet & & 1.51 \% & 3.51 \% & 1.85 \% & 100.00 \% & 0.19 s / 1 core & \\
gwcnet+L\_norm & & 1.51 \% & 3.56 \% & 1.85 \% & 100.00 \% & 0.4 s / 1 core & \\
FAPEEM & & 1.61 \% & 3.08 \% & 1.85 \% & 100.00 \% & 0.35 s / 1 core & \\
MLA & & 1.53 \% & 3.49 \% & 1.86 \% & 100.00 \% & 0.2 s / 1 core & \\
UCFNet\_RVC & & 1.57 \% & 3.33 \% & 1.86 \% & 100.00 \% & 0.24 s / 1 core & \\
PSMNet+ & & 1.51 \% & 3.60 \% & 1.86 \% & 100.00 \% & 0.41 s / GPU & \\
GRNet & & 1.53 \% & 3.54 \% & 1.87 \% & 100.00 \% & 0.19 s / 1 core & \\
FGDS-Net & & 1.58 \% & 3.35 \% & 1.88 \% & 100.00 \% & 0.3 s / 1 core & \\
CFNet & & 1.54 \% & 3.56 \% & 1.88 \% & 100.00 \% & 0.18 s / 1 core & 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.\\
CREStereo++\_RVC & & 1.55 \% & 3.53 \% & 1.88 \% & 100.00 \% & 1 s / GPU & \\
RigidMask+ISF & fl & 1.53 \% & 3.65 \% & 1.89 \% & 100.00 \% & 3.3 s / GPU & G. Yang and D. Ramanan: Learning to Segment Rigid Motions from Two Frames. CVPR 2021.\\
AcfNet & & 1.51 \% & 3.80 \% & 1.89 \% & 100.00 \% & 0.48 s / GPU & 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.\\
SBS & & 1.65 \% & 3.12 \% & 1.89 \% & 100.00 \% & 0.1 s / 1 core & \\
TemporalCoEx & & 1.71 \% & 2.78 \% & 1.89 \% & 100.00 \% & 0.04 s / GPU & \\
PSMNet+PPF & & 1.55 \% & 3.63 \% & 1.89 \% & 100.00 \% & 0.35 s / 1 core & \\
sCroCo\_RVC & & 1.73 \% & 2.76 \% & 1.90 \% & 100.00 \% & 1.1 s / 1 core & \\
Fast-ACV+ & & 1.68 \% & 3.06 \% & 1.91 \% & 100.00 \% & 0.04 s / 1 core & \\
NLCA\_NET\_v2\_RVC & & 1.51 \% & 3.97 \% & 1.92 \% & 100.00 \% & 0.67 s / GPU & 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.\\
CDN & & 1.66 \% & 3.20 \% & 1.92 \% & 100.00 \% & 0.4 s / GPU & 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.\\
Abc-Net & & 1.47 \% & 4.20 \% & 1.92 \% & 100.00 \% & 0.83 s / 4 core & X. Li, Y. Fan, G. Lv and H. Ma: Area-based correlation and non-local attention network for stereo matching. The Visual Computer 2021.\\
GANet-15 & & 1.55 \% & 3.82 \% & 1.93 \% & 100.00 \% & 0.36 s / & 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.\\
PCVNet & & 1.68 \% & 3.19 \% & 1.93 \% & 100.00 \% & 0.05 s / GPU & \\
sanet & & 1.64 \% & 3.41 \% & 1.93 \% & 100.00 \% & 0.30 s / 1 core & \\
DSN & & 1.56 \% & 3.84 \% & 1.94 \% & 100.00 \% & 0.03 s / GPU & \\
CGI-Stereo & & 1.66 \% & 3.38 \% & 1.94 \% & 100.00 \% & 0.02 s / & \\
Cs-Net & & 1.58 \% & 3.79 \% & 1.95 \% & 100.00 \% & 0.5 s / GPU & \\
CAL-Net & & 1.59 \% & 3.76 \% & 1.95 \% & 100.00 \% & 0.44 s / 2 cores & 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.\\
SASNet & & 1.61 \% & 3.65 \% & 1.95 \% & 100.00 \% & 0.21 s / GPU & \\
NLCA-Net & & 1.53 \% & 4.09 \% & 1.96 \% & 100.00 \% & 0.6 s / 1 core & 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.\\
CFNet\_RVC & & 1.65 \% & 3.53 \% & 1.96 \% & 100.00 \% & 0.22 s / GPU & 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.\\
High\_U+A\_coex & & 1.63 \% & 3.60 \% & 1.96 \% & 100.00 \% & 0.35 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PGNet & & 1.64 \% & 3.60 \% & 1.96 \% & 100.00 \% & 0.7 s / 1 core & S. Chen, Z. Xiang, C. Qiao, Y. Chen and T. Bai: PGNet: Panoptic parsing guided deep stereo matching. Neurocomputing 2021.\\
GCGANet & & 1.65 \% & 3.59 \% & 1.97 \% & 100.00 \% & 0.15 s / 1 core & \\
CGFNet & & 1.71 \% & 3.36 \% & 1.98 \% & 100.00 \% & 0.03 s / GPU & \\
HITNet & & 1.74 \% & 3.20 \% & 1.98 \% & 100.00 \% & 0.02 s / GPU & 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.\\
SGNet & & 1.63 \% & 3.76 \% & 1.99 \% & 100.00 \% & 0.6 s / 1 core & 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.\\
MaskLacGwcNet\_RVC & & 1.65 \% & 3.68 \% & 1.99 \% & 100.00 \% & 0.35 s / 1 core & \\
CSN & & 1.59 \% & 4.03 \% & 2.00 \% & 100.00 \% & 0.6 s / 1 core & 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.\\
MKDNet & & 1.70 \% & 3.56 \% & 2.01 \% & 100.00 \% & 0.3 s / 1 core & \\
PCMAnet & & 1.71 \% & 3.55 \% & 2.02 \% & 100.00 \% & 0.27 s / GPU & \\
CoEx & & 1.74 \% & 3.41 \% & 2.02 \% & 100.00 \% & 0.027 s / & 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.\\
AAP+ & & 1.68 \% & 3.74 \% & 2.02 \% & 100.00 \% & 0.05 s / 1 core & \\
HD^3-Stereo & & 1.70 \% & 3.63 \% & 2.02 \% & 100.00 \% & 0.14 s / & Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition for Match Density Estimation. CVPR 2019.\\
GFGANet & & 1.62 \% & 4.04 \% & 2.02 \% & 100.00 \% & 0.39 s / 1 core & \\
GAMNet & & 1.67 \% & 3.78 \% & 2.02 \% & 100.00 \% & 1 s / GPU & \\
SCV-Stereo & & 1.67 \% & 3.78 \% & 2.02 \% & 100.00 \% & 0.08 s / GPU & 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.\\
AANet+ & & 1.65 \% & 3.96 \% & 2.03 \% & 100.00 \% & 0.06 s / & H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.\\
OB\_GWC & & 1.59 \% & 4.32 \% & 2.04 \% & 100.00 \% & 0.35 s / 1 core & \\
ED-Net & & 1.71 \% & 3.80 \% & 2.05 \% & 100.00 \% & 0.2 s / 1 core & \\
OGMNet\_WO\_GP\_SA & & 1.76 \% & 3.55 \% & 2.06 \% & 100.00 \% & 0.4 s / GPU & \\
LR-PSMNet & & 1.65 \% & 4.13 \% & 2.06 \% & 100.00 \% & 0.5 s / GPU & W. Chuah, R. Tennakoon, R. Hoseinnezhad, A. Bab-Hadiashar and D. Suter: Adjusting Bias in Long Range Stereo Matching: A semantics guided approach. 2020.\\
OB\_COEX\_1 & & 1.77 \% & 3.56 \% & 2.07 \% & 100.00 \% & 0.35 s / 1 core & \\
iRaftStereo\_RVC & & 1.88 \% & 3.03 \% & 2.07 \% & 100.00 \% & 0.5 s / GPU & \\
Lite-UAStereo & & 1.79 \% & 3.53 \% & 2.08 \% & 100.00 \% & 1 s / 1 core & \\
mfnet & & 1.65 \% & 4.22 \% & 2.08 \% & 100.00 \% & 0.16 s / GPU & \\
PSM + SMD-Nets & & 1.69 \% & 4.01 \% & 2.08 \% & 100.00 \% & 0.41 s / 1 core & F. Tosi, Y. Liao, C. Schmitt and A. Geiger: SMD-Nets: Stereo Mixture Density Networks. Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
MDCNet & & 1.76 \% & 3.68 \% & 2.08 \% & 100.00 \% & 0.05 s / 1 core & W. Chen, X. Jia, M. Wu and Z. Liang: Multi-Dimensional Cooperative Network for Stereo Matching. IEEE Robotics and Automation Letters 2022.\\
EdgeStereo-V2 & & 1.84 \% & 3.30 \% & 2.08 \% & 100.00 \% & 0.32s / & 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.\\
3D-MSNet / MSNet3D & & 1.75 \% & 3.87 \% & 2.10 \% & 100.00 \% & 1.5s / & 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.\\
GwcNet-g & & 1.74 \% & 3.93 \% & 2.11 \% & 100.00 \% & 0.32 s / GPU & X. Guo, K. Yang, W. Yang, X. Wang and H. Li: Group-wise correlation stereo network. CVPR 2019.\\
SSPCVNet & & 1.75 \% & 3.89 \% & 2.11 \% & 100.00 \% & 0.9 s / 1 core & 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.\\
CGNet & & 1.81 \% & 3.68 \% & 2.12 \% & 100.00 \% & 0.03 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
WSMCnet & & 1.72 \% & 4.19 \% & 2.13 \% & 100.00 \% & 0.39s / & 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.\\
iRaftStereo\_RVC & & 1.89 \% & 3.36 \% & 2.14 \% & 100.00 \% & 0.5 s / 1 core & \\
HSM-1.8x & & 1.80 \% & 3.85 \% & 2.14 \% & 100.00 \% & 0.14 s / & 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.\\
DeepPruner (best) & & 1.87 \% & 3.56 \% & 2.15 \% & 100.00 \% & 0.18 s / 1 core & S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. ICCV 2019.\\
Ct-Net & & 1.85 \% & 3.73 \% & 2.16 \% & 100.00 \% & 0.45 s / GPU & \\
CGF-F-B & & 1.75 \% & 4.20 \% & 2.16 \% & 100.00 \% & 0.26 s / GPU & \\
PMS++\_Fast & & 1.93 \% & 3.30 \% & 2.16 \% & 100.00 \% & 0.40 s / 1 core & \\
Stereo-fusion-SJTU & & 1.87 \% & 3.61 \% & 2.16 \% & 100.00 \% & 0.7 s / & 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.\\
OGMNet18 & & 1.97 \% & 3.16 \% & 2.17 \% & 100.00 \% & 0.2 s / GPU & \\
AutoDispNet-CSS & & 1.94 \% & 3.37 \% & 2.18 \% & 100.00 \% & 0.9 s / 1 core & 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.\\
MLA & & 1.71 \% & 4.54 \% & 2.18 \% & 100.00 \% & 0.2 s / 1 core & \\
EBNet & & 1.83 \% & 3.96 \% & 2.18 \% & 100.00 \% & 0.02 s / GPU & \\
BGNet+ & & 1.81 \% & 4.09 \% & 2.19 \% & 100.00 \% & 0.03 s / GPU & 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.\\
DFL-CCA-Net & & 1.81 \% & 4.13 \% & 2.19 \% & 100.00 \% & 0.49 s / & \\
OA\_COEX & & 1.85 \% & 3.95 \% & 2.20 \% & 100.00 \% & 0.35 s / 1 core & \\
Bi3D & & 1.95 \% & 3.48 \% & 2.21 \% & 100.00 \% & 0.48 s / GPU & 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.\\
NDR & & 1.88 \% & 3.87 \% & 2.21 \% & 100.00 \% & 0.05 s / 1 core & \\
pcanet & & 1.94 \% & 3.57 \% & 2.21 \% & 100.00 \% & 0.27 s / 1 core & \\
dh & & 1.86 \% & 4.01 \% & 2.22 \% & 100.00 \% & 1.9 s / 1 core & 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.\\
SENSE & fl & 2.07 \% & 3.01 \% & 2.22 \% & 100.00 \% & 0.32s / & 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.\\
SegStereo & & 1.88 \% & 4.07 \% & 2.25 \% & 100.00 \% & 0.6 s / & G. Yang, H. Zhao, J. Shi, Z. Deng and J. Jia: SegStereo: Exploiting Semantic Information for Disparity Estimation. ECCV 2018.\\
PSMNet\_FE & & 1.84 \% & 4.32 \% & 2.25 \% & 100.00 \% & 0.17 s / 1 core & \\
DTF\_SENSE & fl mv & 2.08 \% & 3.13 \% & 2.25 \% & 100.00 \% & 0.76 s / 1 core & 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.\\
SGNet & & 1.90 \% & 4.01 \% & 2.25 \% & 100.00 \% & 0.03 s / 1 core & \\
MCV-MFC & & 1.95 \% & 3.84 \% & 2.27 \% & 100.00 \% & 0.35 s / 1 core & 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.\\
HSM-1.5x & & 1.95 \% & 3.93 \% & 2.28 \% & 100.00 \% & 0.085 s / & 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.\\
GAANet & & 1.91 \% & 4.25 \% & 2.30 \% & 100.00 \% & 0.08 / 2080tiGPU & \\
Separable Convs & & 1.90 \% & 4.36 \% & 2.31 \% & 100.00 \% & 2 s / 1 core & R. Rahim, F. Shamsafar and A. Zell: Separable Convolutions for Optimizing 3D Stereo Networks. 2021 IEEE International Conference on Image Processing (ICIP) 2021.\\
Separable Convs & & 1.90 \% & 4.36 \% & 2.31 \% & 100.00 \% & 2 s / 1 core & R. Rahim, F. Shamsafar and A. Zell: Separable Convolutions for Optimizing 3D Stereo Networks. 2021 IEEE International Conference on Image Processing (ICIP) 2021.\\
CFP-Net & & 1.90 \% & 4.39 \% & 2.31 \% & 100.00 \% & 0.9 s / 8 cores & Z. Zhu, M. He, Y. Dai, Z. Rao and B. Li: Multi-scale Cross-form Pyramid Network for Stereo Matching. arXiv preprint 2019.\\
CGNet & & 1.96 \% & 4.12 \% & 2.32 \% & 100.00 \% & 0.03 s / GPU & \\
PSMNet & & 1.86 \% & 4.62 \% & 2.32 \% & 100.00 \% & 0.41 s / & J. Chang and Y. Chen: Pyramid Stereo Matching Network. arXiv preprint arXiv:1803.08669 2018.\\
CroCo\_RVC & & 2.04 \% & 3.75 \% & 2.33 \% & 100.00 \% & 1 s / 1 core & \\
GANetREF\_RVC & & 1.88 \% & 4.58 \% & 2.33 \% & 100.00 \% & 1.62 s / GPU & 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.\\
TriStereoNet & & 1.86 \% & 4.77 \% & 2.35 \% & 100.00 \% & 0.5 s / & F. Shamsafar and A. Zell: TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation. arXiv preprint arXiv:2111.12502 2021.\\
MABNet\_origin & & 1.89 \% & 5.02 \% & 2.41 \% & 100.00 \% & 0.38 s / & J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module. .\\
gsnet & & 1.93 \% & 4.95 \% & 2.43 \% & 100.00 \% & 0.2 s / GPU & \\
MVFNet & & 2.00 \% & 4.60 \% & 2.43 \% & 100.00 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
SAStereo & & 2.21 \% & 3.68 \% & 2.46 \% & 100.00 \% & 0.04 s / GPU & \\
SMT & & 2.04 \% & 4.63 \% & 2.47 \% & 98.22 \% & 0.4 s / 1 core & \\
ERSCNet & & 2.11 \% & 4.46 \% & 2.50 \% & 100.00 \% & 0.28 s / GPU & Anonymous: ERSCNet. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
BGNet & & 2.16 \% & 4.24 \% & 2.50 \% & 100.00 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
BGNet & & 2.07 \% & 4.74 \% & 2.51 \% & 100.00 \% & 0.02 s / GPU & 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.\\
UberATG-DRISF & fl & 2.16 \% & 4.49 \% & 2.55 \% & 100.00 \% & 0.75 s / CPU+GPU & W. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun: Deep Rigid Instance Scene Flow. CVPR 2019.\\
AANet & & 1.99 \% & 5.39 \% & 2.55 \% & 100.00 \% & 0.062 s / & H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.\\
MVFNet & & 2.10 \% & 4.82 \% & 2.55 \% & 100.00 \% & 0.02 s / GPU & \\
stroco & & 2.35 \% & 3.70 \% & 2.58 \% & 100.00 \% & 1.3 s / 1 core & \\
PDSNet & & 2.29 \% & 4.05 \% & 2.58 \% & 100.00 \% & 0.5 s / 1 core & 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.\\
DeepPruner (fast) & & 2.32 \% & 3.91 \% & 2.59 \% & 100.00 \% & 0.06 s / 1 core & S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. ICCV 2019.\\
FADNet & & 2.50 \% & 3.10 \% & 2.60 \% & 100.00 \% & 0.05 s / & 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.\\
MMStereo & & 2.25 \% & 4.38 \% & 2.61 \% & 100.00 \% & 0.04 s / & K. Shankar, M. Tjersland, J. Ma, K. Stone and M. Bajracharya: A Learned Stereo Depth System for Robotic Manipulation in Homes. .\\
SCV & & 2.22 \% & 4.53 \% & 2.61 \% & 100.00 \% & 0.36 s / & C. Lu, H. Uchiyama, D. Thomas, A. Shimada and R. Taniguchi: Sparse Cost Volume for Efficient Stereo Matching. Remote Sensing 2018.\\
WaveletStereo: & & 2.24 \% & 4.62 \% & 2.63 \% & 100.00 \% & 0.27 s / 1 core & Anonymous: WaveletStereo: Learning wavelet coefficients for stereo matching. arXiv: Computer Vision and Pattern Recognition 2019.\\
RLStereo & & 2.09 \% & 5.38 \% & 2.64 \% & 100.00 \% & 0.03 s / 1 core & Anonymous: RLStereo: Real-time Stereo Matching based on Reinforcement Learning. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
AANet\_RVC & & 2.23 \% & 4.89 \% & 2.67 \% & 100.00 \% & 0.1 s / GPU & H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.\\
CRL & & 2.48 \% & 3.59 \% & 2.67 \% & 100.00 \% & 0.47 s / & 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.\\
TSNnet\_Teacher & & 2.24 \% & 4.99 \% & 2.70 \% & 100.00 \% & 0.01 s / 1 core & \\
EASNet & & 2.34 \% & 4.51 \% & 2.70 \% & 100.00 \% & 0.1 s / GPU & \\
GEStereo\_RVC & & 2.29 \% & 4.79 \% & 2.71 \% & 100.00 \% & 0.22 s / GPU & \\
FANET\_L1 & & 2.68 \% & 3.29 \% & 2.78 \% & 100.00 \% & 0.04 s / 1 core & \\
2D-MSNet / MSNet2D & & 2.49 \% & 4.53 \% & 2.83 \% & 100.00 \% & 0.4s / & 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.\\
GC-NET & & 2.21 \% & 6.16 \% & 2.87 \% & 100.00 \% & 0.9 s / & 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.\\
EASNet-M & & 2.40 \% & 5.37 \% & 2.89 \% & 100.00 \% & 0.8 s / GPU & \\
BGNet & & 2.51 \% & 4.88 \% & 2.90 \% & 100.00 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
PVStereo & & 2.29 \% & 6.50 \% & 2.99 \% & 100.00 \% & 0.10 s / GPU & 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.\\
LRCR & & 2.55 \% & 5.42 \% & 3.03 \% & 100.00 \% & 49.2 s / & 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.\\
cas-stereo & & 2.62 \% & 5.17 \% & 3.04 \% & 100.00 \% & 0.1 s / 8 cores & \\
TSNnet\_student & & 2.35 \% & 6.70 \% & 3.07 \% & 100.00 \% & 0.01 s / 1 core & \\
OB\_COEX & & 2.44 \% & 6.23 \% & 3.07 \% & 100.00 \% & 0.02 s / 1 core & \\
Fast DS-CS & & 2.83 \% & 4.31 \% & 3.08 \% & 100.00 \% & 0.02 s / GPU & K. Yee and A. Chakrabarti: Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures. WACV 2020 (to appear).\\
AdaStereo & & 2.59 \% & 5.55 \% & 3.08 \% & 100.00 \% & 0.41 s / GPU & 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.\\
fca & & 2.55 \% & 5.75 \% & 3.09 \% & 100.00 \% & 0.12 s / 1 core & \\
RecResNet & & 2.46 \% & 6.30 \% & 3.10 \% & 100.00 \% & 0.3 s / GPU & K. Batsos and P. Mordohai: RecResNet: A Recurrent Residual CNN Architecture for Disparity Map Enhancement. In International Conference on 3D Vision (3DV) 2018.\\
NVStereoNet & & 2.62 \% & 5.69 \% & 3.13 \% & 100.00 \% & 0.6 s / & 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.\\
DRR & & 2.58 \% & 6.04 \% & 3.16 \% & 100.00 \% & 0.4 s / & S. Gidaris and N. Komodakis: Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling. arXiv preprint arXiv:1612.04770 2016.\\
TSNnet\_naive & & 2.64 \% & 6.47 \% & 3.28 \% & 100.00 \% & 0.01 s / 1 core & \\
DWARF & fl & 3.20 \% & 3.94 \% & 3.33 \% & 100.00 \% & 0.14s - 1.43s / & 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.\\
Swin-ATUNet & & 2.91 \% & 5.54 \% & 3.35 \% & 100.00 \% & 0.2 s / GPU & \\
SsSMnet & & 2.70 \% & 6.92 \% & 3.40 \% & 100.00 \% & 0.8 s / & Y. Zhong, Y. Dai and H. Li: Self-Supervised Learning for Stereo Matching with Self-Improving Ability. arXiv:1709.00930 2017.\\
BGNet & & 2.99 \% & 5.50 \% & 3.41 \% & 100.00 \% & 0.25 s / GPU & \\
L-ResMatch & & 2.72 \% & 6.95 \% & 3.42 \% & 100.00 \% & 48 s / 1 core & A. Shaked and L. Wolf: Improved Stereo Matching with Constant Highway Networks and Reflective Loss. arXiv preprint arxiv:1701.00165 2016.\\
EASNet-S & & 2.89 \% & 6.12 \% & 3.43 \% & 100.00 \% & 0.6 s / GPU & \\
Displets v2 & & 3.00 \% & 5.56 \% & 3.43 \% & 100.00 \% & 265 s / >8 cores & F. Guney and A. Geiger: Displets: Resolving Stereo Ambiguities using Object Knowledge. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.\\
LBPS & & 2.85 \% & 6.35 \% & 3.44 \% & 100.00 \% & 0.39 s / GPU & 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.\\
ACOSF & fl & 2.79 \% & 7.56 \% & 3.58 \% & 100.00 \% & 5 min / 1 core & 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.\\
CNNF+SGM & & 2.78 \% & 7.69 \% & 3.60 \% & 100.00 \% & 71 s / & F. Zhang and B. Wah: Fundamental Principles on Learning New Features for Effective Dense Matching. IEEE Transactions on Image Processing 2018.\\
PBCP & & 2.58 \% & 8.74 \% & 3.61 \% & 100.00 \% & 68 s / & A. Seki and M. Pollefeys: Patch Based Confidence Prediction for Dense Disparity Map. British Machine Vision Conference (BMVC) 2016.\\
SGM-Net & & 2.66 \% & 8.64 \% & 3.66 \% & 100.00 \% & 67 s / & A. Seki and M. Pollefeys: SGM-Nets: Semi-Global Matching With Neural Networks. CVPR 2017.\\
DSMNet-synthetic & & 3.11 \% & 6.72 \% & 3.71 \% & 100.00 \% & 1.6 s / 4 cores & 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.\\
HSM-Net\_RVC & & 2.74 \% & 8.73 \% & 3.74 \% & 100.00 \% & 0.97 s / GPU & 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.\\
MABNet\_tiny & & 3.04 \% & 8.07 \% & 3.88 \% & 100.00 \% & 0.11 s / & J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module. .\\
MC-CNN-acrt & & 2.89 \% & 8.88 \% & 3.89 \% & 100.00 \% & 67 s / & J. Zbontar and Y. LeCun: Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches. Submitted to JMLR .\\
FD-Fusion & & 3.22 \% & 7.44 \% & 3.92 \% & 100.00 \% & 0.01 s / 1 core & 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.\\
KDAT & & 3.27 \% & 7.48 \% & 3.97 \% & 100.00 \% & 30ms / 1 core & \\
Reversing-PSMNet & & 3.13 \% & 8.70 \% & 4.06 \% & 100.00 \% & 0.41 s / 1 core & 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.\\
DGS & & 3.21 \% & 8.62 \% & 4.11 \% & 100.00 \% & 0.32 s / GPU & 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.\\
PRSM & fl mv & 3.02 \% & 10.52 \% & 4.27 \% & 99.99 \% & 300 s / 1 core & C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. ijcv 2015.\\
CKDNet\_naïve & & 3.47 \% & 8.44 \% & 4.29 \% & 100.00 \% & 0.01 s / 1 core & \\
DispNetC & & 4.32 \% & 4.41 \% & 4.34 \% & 100.00 \% & 0.06 s / & 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.\\
SGM-Forest & & 3.11 \% & 10.74 \% & 4.38 \% & 99.92 \% & 6 seconds / 1 core & 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.\\
SSF & fl & 3.55 \% & 8.75 \% & 4.42 \% & 100.00 \% & 5 min / 1 core & Z. Ren, D. Sun, J. Kautz and E. Sudderth: Cascaded Scene Flow Prediction using Semantic Segmentation. International Conference on 3D Vision (3DV) 2017.\\
SMV & & 3.45 \% & 9.32 \% & 4.43 \% & 100.00 \% & 0.5 s / GPU & 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.\\
ISF & fl & 4.12 \% & 6.17 \% & 4.46 \% & 100.00 \% & 10 min / 1 core & 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.\\
Content-CNN & & 3.73 \% & 8.58 \% & 4.54 \% & 100.00 \% & 1 s / & W. Luo, A. Schwing and R. Urtasun: Efficient Deep Learning for Stereo Matching. CVPR 2016.\\
MADnet & & 3.75 \% & 9.20 \% & 4.66 \% & 100.00 \% & 0.02 s / GPU & 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.\\
Self-SuperFlow-ft & fl & 3.81 \% & 8.92 \% & 4.66 \% & 100.00 \% & 0.13 s / & K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. International Conference on Image Processing (ICIP) 2022.\\
DTF\_PWOC & fl mv & 3.91 \% & 8.57 \% & 4.68 \% & 100.00 \% & 0.38 s / & 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.\\
VN & & 4.29 \% & 7.65 \% & 4.85 \% & 100.00 \% & 0.5 s / GPU & P. Knöbelreiter and T. Pock: Learned Collaborative Stereo Refinement. German Conference on Pattern Recognition (GCPR) 2019.\\
MC-CNN-WS & & 3.78 \% & 10.93 \% & 4.97 \% & 100.00 \% & 1.35 s / & S. Tulyakov, A. Ivanov and F. Fleuret: Weakly supervised learning of deep metrics for stereo reconstruction. ICCV 2017.\\
3DMST & & 3.36 \% & 13.03 \% & 4.97 \% & 100.00 \% & 93 s / 1 core & X. Lincheng Li and L. Zhang: 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching. submitted to Applied Optics .\\
CBMV\_ROB & & 3.55 \% & 12.09 \% & 4.97 \% & 100.00 \% & 250 s / 6 core & 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.\\
OSF+TC & fl mv & 4.11 \% & 9.64 \% & 5.03 \% & 100.00 \% & 50 min / 1 core & M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.\\
CBMV & & 4.17 \% & 9.53 \% & 5.06 \% & 100.00 \% & 250 s / 6 cores & K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. 2018.\\
PWOC-3D & fl & 4.19 \% & 9.82 \% & 5.13 \% & 100.00 \% & 0.13 s / & 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.\\
OSF 2018 & fl & 4.11 \% & 11.12 \% & 5.28 \% & 100.00 \% & 390 s / 1 core & M. Menze, C. Heipke and A. Geiger: Object Scene Flow. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 2018.\\
SPS-St & & 3.84 \% & 12.67 \% & 5.31 \% & 100.00 \% & 2 s / 1 core & K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014.\\
sRRNet & & 4.44 \% & 9.79 \% & 5.33 \% & 100.00 \% & 0.03 s / 1 core & \\
MDP & st & 4.19 \% & 11.25 \% & 5.36 \% & 100.00 \% & 11.4 s / 4 cores & 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.\\
SFF++ & fl mv & 4.27 \% & 12.38 \% & 5.62 \% & 100.00 \% & 78 s / 4 cores & 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.\\
OSF & fl & 4.54 \% & 12.03 \% & 5.79 \% & 100.00 \% & 50 min / 1 core & M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.\\
pSGM & & 4.84 \% & 11.64 \% & 5.97 \% & 100.00 \% & 7.77 s / 4 cores & Y. Lee, M. Park, Y. Hwang, Y. Shin and C. Kyung: Memory-Efficient Parametric Semiglobal Matching. IEEE Signal Processing Letters 2018.\\
CSF & fl & 4.57 \% & 13.04 \% & 5.98 \% & 99.99 \% & 80 s / 1 core & 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.\\
MBM & & 4.69 \% & 13.05 \% & 6.08 \% & 100.00 \% & 0.13 s / 1 core & N. Einecke and J. Eggert: A Multi-Block-Matching Approach for Stereo. IV 2015.\\
CRD-Fusion & & 4.59 \% & 13.68 \% & 6.11 \% & 100.00 \% & 0.02 s / GPU & 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.\\
LDC-S & & 5.78 \% & 7.92 \% & 6.14 \% & 100.00 \% & 0.04 s / 1 core & \\
PR-Sceneflow & fl & 4.74 \% & 13.74 \% & 6.24 \% & 100.00 \% & 150 s / 4 core & C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.\\
DispSegNet & & 4.20 \% & 16.97 \% & 6.33 \% & 100.00 \% & 0.9 s / GPU & 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.\\
DeepCostAggr & & 5.34 \% & 11.35 \% & 6.34 \% & 99.98 \% & 0.03 s / GPU & 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.\\
SGM\_RVC & & 5.06 \% & 13.00 \% & 6.38 \% & 100.00 \% & 0.11 s / & H. Hirschm\"uller: Stereo Processing by Semi-Global Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008.\\
LDC-L & & 5.65 \% & 10.19 \% & 6.41 \% & 100.00 \% & 0.04 s / 1 core & \\
ssf-SGBM & & 4.93 \% & 13.81 \% & 6.41 \% & 99.99 \% & 1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SceneFFields & fl & 5.12 \% & 13.83 \% & 6.57 \% & 100.00 \% & 65 s / 4 cores & 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.\\
SPS+FF++ & fl & 5.47 \% & 12.19 \% & 6.59 \% & 100.00 \% & 36 s / 1 core & R. Schuster, O. Wasenmüller and D. Stricker: Dense Scene Flow from Stereo Disparity and Optical Flow. ACM Computer Science in Cars Symposium (CSCS) 2018.\\
Flow2Stereo & & 5.01 \% & 14.62 \% & 6.61 \% & 99.97 \% & 0.05 s / GPU & P. Liu, I. King, M. Lyu and J. Xu: Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching. CVPR 2020.\\
FSF+MS & fl ms mv & 5.72 \% & 11.84 \% & 6.74 \% & 100.00 \% & 2.7 s / 4 cores & 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.\\
AABM & & 4.88 \% & 16.07 \% & 6.74 \% & 100.00 \% & 0.08 s / 1 core & N. Einecke and J. Eggert: Stereo Image Warping for Improved Depth Estimation of Road Surfaces. IV 2013.\\
test\_ours & fl & 5.62 \% & 12.81 \% & 6.82 \% & 100.00 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SGM+C+NL & fl & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 4.5 min / 1 core & 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.\\
SGM+LDOF & fl & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 86 s / 1 core & 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.\\
SGM+SF & fl & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 45 min / 16 core & 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.\\
SNCC & & 5.36 \% & 16.05 \% & 7.14 \% & 100.00 \% & 0.08 s / 1 core & N. Einecke and J. Eggert: A Two-Stage Correlation Method for Stereoscopic Depth Estimation. DICTA 2010.\\
Permutation Stereo & & 5.53 \% & 15.47 \% & 7.18 \% & 99.93 \% & 30 s / GPU & P. Brousseau and S. Roy: A Permutation Model for the Self- Supervised Stereo Matching Problem. 2022 19th Conference on Robots and Vision (CRV) 2022.\\
PASMnet & & 5.41 \% & 16.36 \% & 7.23 \% & 100.00 \% & 0.5 s / GPU & 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.\\
AAFS & & 6.27 \% & 13.95 \% & 7.54 \% & 100.00 \% & 0.01 s / 1 core & 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.\\
Z2ZNCC & & 6.55 \% & 13.19 \% & 7.65 \% & 99.93 \% & 0.035s / Jetson TX2 GPU & 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.\\
ReS2tAC & st & 6.27 \% & 16.07 \% & 7.90 \% & 86.03 \% & 0.06 s / Jetson AGX GPU & 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.\\
MCP-HA-VQ & & 6.61 \% & 15.33 \% & 8.06 \% & 100.00 \% & 290 s / 8 cores & \\
Self-SuperFlow & fl & 5.78 \% & 19.76 \% & 8.11 \% & 100.00 \% & 0.13 s / & K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. International Conference on Image Processing (ICIP) 2022.\\
CSCT+SGM+MF & & 6.91 \% & 14.87 \% & 8.24 \% & 100.00 \% & 0.0064 s / Nvidia GTX Titan X & 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.\\
MBMGPU & & 6.61 \% & 16.70 \% & 8.29 \% & 100.00 \% & 0.0019 s / GPU & Q. Chang and T. Maruyama: Real-Time Stereo Vision System: A Multi-Block Matching on GPU. IEEE Access 2018.\\
MeshStereo & & 5.82 \% & 21.21 \% & 8.38 \% & 100.00 \% & 87 s / 1 core & 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.\\
PCOF + ACTF & fl & 6.31 \% & 19.24 \% & 8.46 \% & 100.00 \% & 0.08 s / GPU & 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.\\
PCOF-LDOF & fl & 6.31 \% & 19.24 \% & 8.46 \% & 100.00 \% & 50 s / 1 core & 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.\\
OASM-Net & & 6.89 \% & 19.42 \% & 8.98 \% & 100.00 \% & 0.73 s / GPU & A. Li and Z. Yuan: Occlusion Aware Stereo Matching via Cooperative Unsupervised Learning. Proceedings of the Asian Conference on Computer Vision, ACCV 2018.\\
ELAS\_RVC & & 7.38 \% & 21.15 \% & 9.67 \% & 100.00 \% & 0.19 s / 4 cores & A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.\\
ELAS & & 7.86 \% & 19.04 \% & 9.72 \% & 92.35 \% & 0.3 s / 1 core & A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.\\
REAF & & 8.43 \% & 18.51 \% & 10.11 \% & 100.00 \% & 1.1 s / 1 core & C. Cigla: Recursive Edge-Aware Filters for Stereo Matching. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2015.\\
self-raft3d & fl & 8.15 \% & 20.86 \% & 10.27 \% & 100.00 \% & 0.1 s / 1 core & \\
iGF & mv & 8.64 \% & 21.85 \% & 10.84 \% & 100.00 \% & 220 s / 1 core & 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.\\
OCV-SGBM & & 8.92 \% & 20.59 \% & 10.86 \% & 90.41 \% & 1.1 s / 1 core & H. Hirschmueller: Stereo processing by semiglobal matching and mutual information. PAMI 2008.\\
TW-SMNet & & 11.92 \% & 12.16 \% & 11.96 \% & 100.00 \% & 0.7 s / GPU & M. El-Khamy, H. Ren, X. Du and J. Lee: TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching. arXiv:1906.04463 2019.\\
SDM & & 9.41 \% & 24.75 \% & 11.96 \% & 62.56 \% & 1 min / 1 core & J. Kostkova: Stratified dense matching for stereopsis in complex scenes. BMVC 2003.\\
SGM&FlowFie+ & fl & 11.93 \% & 20.57 \% & 13.37 \% & 81.24 \% & 29 s / 1 core & 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.\\
GCSF & fl & 11.64 \% & 27.11 \% & 14.21 \% & 100.00 \% & 2.4 s / 1 core & J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.\\
MT-TW-SMNet & & 15.47 \% & 16.25 \% & 15.60 \% & 100.00 \% & 0.4s / GPU & 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.\\
RAFT-MSF-ft & fl & 14.19 \% & 24.79 \% & 15.95 \% & 100.00 \% & 0.18 s / 1 core & \\
Mono-SF & fl & 14.21 \% & 26.94 \% & 16.32 \% & 100.00 \% & 41 s / 1 core & 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.\\
CostFilter & & 17.53 \% & 22.88 \% & 18.42 \% & 100.00 \% & 4 min / 1 core & C. Rhemann, A. Hosni, M. Bleyer, C. Rother and M. Gelautz: Fast Cost-Volume Filtering for Visual Correspondence and Beyond. CVPR 2011.\\
MonoComb & fl & 17.89 \% & 21.16 \% & 18.44 \% & 100.00 \% & 0.58 s / & 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.\\
DWBSF & fl & 19.61 \% & 22.69 \% & 20.12 \% & 100.00 \% & 7 min / 4 cores & C. Richardt, H. Kim, L. Valgaerts and C. Theobalt: Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras. 3DV 2016.\\
monoResMatch & & 22.10 \% & 19.81 \% & 21.72 \% & 100.00 \% & 0.16 s / & 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.\\
Self-Mono-SF-ft & fl & 20.72 \% & 29.41 \% & 22.16 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
Multi-Mono-SF-ft & fl mv & 21.60 \% & 28.22 \% & 22.71 \% & 100.00 \% & 0.06 s / & J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.\\
OCV-BM & & 24.29 \% & 30.13 \% & 25.27 \% & 58.54 \% & 0.1 s / 1 core & G. Bradski: The OpenCV Library. Dr. Dobb's Journal of Software Tools 2000.\\
selfmono & fl & 25.47 \% & 29.76 \% & 26.18 \% & 100.00 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
VSF & fl & 27.31 \% & 21.72 \% & 26.38 \% & 100.00 \% & 125 min / 1 core & F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.\\
SED & & 25.01 \% & 40.43 \% & 27.58 \% & 4.02 \% & 0.68 s / 1 core & 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.\\
Multi-Mono-SF & fl mv & 27.48 \% & 47.30 \% & 30.78 \% & 100.00 \% & 0.06 s / & J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.\\
mts1 & & 28.03 \% & 46.55 \% & 31.11 \% & 2.52 \% & 0.18 s / 4 cores & R. Brandt, N. Strisciuglio, N. Petkov and M. Wilkinson: Efficient binocular stereo correspondence matching with 1-D Max-Trees. Pattern Recognition Letters 2020.\\
Self-Mono-SF & fl & 31.22 \% & 48.04 \% & 34.02 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
MST & & 45.83 \% & 38.22 \% & 44.57 \% & 100.00 \% & 7 s / 1 core & Q. Yang: A Non-Local Cost Aggregation Method for Stereo Matching. CVPR 2012.\\
FRLPSSF & fl & 56.60 \% & 73.05 \% & 59.34 \% & 9.26 \% & 2.5 s / 8 core & A. Erfan salehi and R. hoseuni: Real-time Low complexity Precision Sparse Scene-flow. 2022.
\end{tabular}