\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
StereoBase & & 1.28 \% & 2.26 \% & 1.44 \% & 100.00 \% & 0.29 s / GPU & X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline. arXiv preprint arXiv:2312.00343 2023.\\
NMRF-Stereo-SwinT & & 1.20 \% & 2.67 \% & 1.45 \% & 100.00 \% & 0.11 s / & \\
TC-Stereo & & 1.29 \% & 2.33 \% & 1.46 \% & 100.00 \% & 0.09 s / & J. Zeng, C. Yao, Y. Wu and Y. Jia: Temporally Consistent Stereo Matching. European conference on computer vision 2024.\\
IMC-Stereo & & 1.33 \% & 2.26 \% & 1.48 \% & 100.00 \% & 0.48 s / 1 core & \\
ST Selective-IGEV & & 1.30 \% & 2.48 \% & 1.50 \% & 100.00 \% & 0.24 s / GPU & \\
ViTAStereo & & 1.21 \% & 2.99 \% & 1.50 \% & 100.00 \% & 0.22 s / & C. Liu, Q. Chen and R. Fan: Playing to Vision Foundation Model's Strengths in Stereo Matching. IEEE Transactions on Intelligent Vehicles 2024.\\
IGEV++ & & 1.31 \% & 2.54 \% & 1.51 \% & 100.00 \% & 0.28 s / & G. Xu, X. Wang, Z. Zhang, J. Cheng, C. Liao and X. Yang: IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching. arXiv preprint arXiv:2409.00638 2024.\\
AEACV & & 1.35 \% & 2.38 \% & 1.52 \% & 100.00 \% & 0.61 s / 1 core & \\
WCG-NET & & 1.35 \% & 2.37 \% & 1.52 \% & 100.00 \% & 0.2 s / 1 core & \\
MoCha-V2 & & 1.35 \% & 2.40 \% & 1.52 \% & 100.00 \% & 0.33 s / & \\
PANet & & 1.32 \% & 2.58 \% & 1.53 \% & 100.00 \% & 0.2 s / 1 core & \\
MoCha-Stereo & & 1.36 \% & 2.43 \% & 1.53 \% & 100.00 \% & 0.34 s / & Z. Chen, W. Long, H. Yao, Y. Zhang, B. Wang, Y. Qin and J. Wu: MoCha-Stereo: Motif Channel Attention Network for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
MR\_Igev & & 1.35 \% & 2.49 \% & 1.54 \% & 100.00 \% & 0.5 s / & \\
DiffuVolume & & 1.35 \% & 2.51 \% & 1.54 \% & 100.00 \% & 0.36 s / GPU & D. Zheng, X. Wu, Z. Liu, J. Meng and W. Zheng: DiffuVolume: Diffusion Model for Volume based Stereo Matching. arXiv preprint arXiv:2308.15989 2023.\\
GANet+ADL & & 1.38 \% & 2.38 \% & 1.55 \% & 100.00 \% & 0.67s / & P. Xu, Z. Xiang, C. Qiao, J. Fu and T. Pu: Adaptive Multi-Modal Cross-Entropy Loss for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
Selective-IGEV & & 1.33 \% & 2.61 \% & 1.55 \% & 100.00 \% & 0.24 s / 1 core & X. Wang, G. Xu, H. Jia and X. Yang: Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
MIF-Stereo & & 1.36 \% & 2.51 \% & 1.55 \% & 100.00 \% & 0.46 s / & \\
MC-Stereo & & 1.36 \% & 2.51 \% & 1.55 \% & 100.00 \% & 0.40 s / GPU & M. Feng, J. Cheng, H. Jia, L. Liu, G. Xu and X. Yang: MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching. International Conference on 3D Vision (3DV) 2024.\\
SR Stereo\_32\_update & & 1.37 \% & 2.49 \% & 1.56 \% & 100.00 \% & 0.18 s / 1 core & W. Xiao and W. Zhao: Stepwise Regression and Pre-trained Edge for Robust Stereo Matching. arXiv preprint arXiv:2406.06953 2024.\\
DR Stereo & & 1.37 \% & 2.50 \% & 1.56 \% & 100.00 \% & 0.18 s / 1 core & \\
UGIF & & 1.38 \% & 2.47 \% & 1.56 \% & 100.00 \% & 0.15 s / 1 core & \\
IGEVStereo-DCA & & 1.40 \% & 2.39 \% & 1.57 \% & 100.00 \% & 0.3 s / 1 core & \\
HART & & 1.39 \% & 2.49 \% & 1.57 \% & 100.00 \% & 0.25 s / & \\
MaDis-Stereo & & 1.42 \% & 2.31 \% & 1.57 \% & 100.00 \% & 0.94 s / & \\
DMIO & & 1.34 \% & 2.74 \% & 1.57 \% & 100.00 \% & 0.3 s / 1 core & Y. Shi: Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective. arXiv preprint arXiv:2404.09051 2024.\\
NMRF-Stereo & & 1.28 \% & 3.07 \% & 1.57 \% & 100.00 \% & 0.09 s / & T. Guan, C. Wang and Y. Liu: Neural Markov Random Field for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.\\
IGEV-ICGNet & & 1.38 \% & 2.55 \% & 1.57 \% & 100.00 \% & 0.18 s / & \\
SPRNet & & 1.43 \% & 2.32 \% & 1.58 \% & 100.00 \% & 3 s / 1 core & \\
MDA & & 1.37 \% & 2.64 \% & 1.58 \% & 100.00 \% & 0.32 s / 1 core & \\
testnet & & 1.38 \% & 2.59 \% & 1.58 \% & 100.00 \% & 0.18 s / 1 core & \\
igev\_refine & & 1.36 \% & 2.68 \% & 1.58 \% & 100.00 \% & 0.18 s / 1 core & \\
bflnet & & 1.37 \% & 2.68 \% & 1.58 \% & 100.00 \% & 0.27 s / & \\
IGEV-GS & & 1.40 \% & 2.50 \% & 1.58 \% & 100.00 \% & 1 s / 1 core & \\
OpenStereo-IGEV & & 1.44 \% & 2.31 \% & 1.59 \% & 100.00 \% & 0.18 s / & X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline. arXiv preprint arXiv:2312.00343 2023.\\
GSSNet & & 1.31 \% & 2.96 \% & 1.59 \% & 100.00 \% & 0.78 s / 1 core & \\
ICGNet-abl & & 1.38 \% & 2.64 \% & 1.59 \% & 100.00 \% & 0.18 s / 1 core & \\
MA-Stereo & & 1.38 \% & 2.66 \% & 1.59 \% & 100.00 \% & 0.06 s / GPU & \\
CroCo-Stereo & & 1.38 \% & 2.65 \% & 1.59 \% & 100.00 \% & 0.93s / & P. Weinzaepfel, T. Lucas, V. Leroy, Y. Cabon, V. Arora, R. Br\'egier, G. Csurka, L. Antsfeld, B. Chidlovskii and J. Revaud: CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. ICCV 2023.\\
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. CVPR 2023.\\
DN+ACVNet & & 1.32 \% & 2.95 \% & 1.60 \% & 100.00 \% & 0.24 s / 1 core & J. Zhang, L. Huang, X. Bai, J. Zheng, L. Gu and E. Hancock: Exploring the Usage of Pre-trained Features for Stereo Matching. International Journal of Computer Vision 2024.\\
SR-Stereo\_step15\_par & & 1.36 \% & 2.79 \% & 1.60 \% & 100.00 \% & 0.10 s / 1 core & \\
EGLCR-Stereo & & 1.38 \% & 2.71 \% & 1.60 \% & 100.00 \% & 0.45 s / 1 core & \\
TEEV\_ & & 1.44 \% & 2.46 \% & 1.61 \% & 100.00 \% & 0.44 s / 1 core & \\
ACVNet-DCA & & 1.41 \% & 2.61 \% & 1.61 \% & 100.00 \% & 0.2 s / 1 core & \\
MVACVNet & & 1.33 \% & 3.09 \% & 1.62 \% & 100.00 \% & 0.01 s / GPU & \\
UPFNet & & 1.38 \% & 2.85 \% & 1.62 \% & 100.00 \% & 0.25 s / 1 core & Q. Chen, B. Ge and J. Quan: Unambiguous Pyramid Cost Volumes Fusion for Stereo Matching. IEEE Transactions on Circuits and Systems for Video Technology 2023.\\
IGEV\_MR & & 1.42 \% & 2.66 \% & 1.63 \% & 100.00 \% & 0.3 s / GPU & \\
RAFT-SFFRU & & 1.42 \% & 2.65 \% & 1.63 \% & 100.00 \% & 0.4 s / 1 core & \\
Selective-RAFT & & 1.41 \% & 2.71 \% & 1.63 \% & 100.00 \% & 0.45 s / 1 core & X. Wang, G. Xu, H. Jia and X. Yang: Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
GeoNet & & 1.40 \% & 2.80 \% & 1.63 \% & 100.00 \% & 0.18 s / 1 core & \\
ADBM & & 1.45 \% & 2.61 \% & 1.64 \% & 100.00 \% & 0.4 s / 1 core & \\
Stereo+ & & 1.33 \% & 3.24 \% & 1.65 \% & 100.00 \% & 0.1 s / 1 core & \\
raft-y & & 1.44 \% & 2.67 \% & 1.65 \% & 100.00 \% & 0.07 s / 1 core & \\
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.\\
SplatFlow3D & fl & 1.40 \% & 2.91 \% & 1.65 \% & 100.00 \% & 0.2 s / GPU & \\
LoS & & 1.42 \% & 2.81 \% & 1.65 \% & 100.00 \% & 0.19 s / 1 core & K. Li, L. Wang, Y. Zhang, K. Xue, S. Zhou and Y. Guo: LoS: Local Structure Guided Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
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.\\
Toi Depth & & 1.35 \% & 3.20 \% & 1.65 \% & 100.00 \% & 1 s / 8 cores & \\
croco & & 1.40 \% & 2.97 \% & 1.66 \% & 100.00 \% & 0.93 s / 1 core & \\
MPFV-Stereo & & 1.50 \% & 2.44 \% & 1.66 \% & 100.00 \% & 0.23 s / 1 core & \\
DCANet & & 1.42 \% & 2.91 \% & 1.66 \% & 100.00 \% & 0.19 s / 1 core & \\
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.\\
PCMAnet & & 1.42 \% & 2.92 \% & 1.67 \% & 100.00 \% & 0.27 s / 1 core & \\
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.\\
Sn-stereo & & 1.44 \% & 2.87 \% & 1.68 \% & 100.00 \% & 0.35 s / 1 core & \\
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.\\
ESM\_Net & & 1.37 \% & 3.30 \% & 1.69 \% & 100.00 \% & 0.03 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PCWNet-SCE & & 1.39 \% & 3.23 \% & 1.69 \% & 100.00 \% & 0.44 s / 1 core & \\
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 .\\
kpa & & 1.52 \% & 2.68 \% & 1.71 \% & 100.00 \% & 0.2 s / 1 core & \\
DANet-Stereo & & 1.41 \% & 3.26 \% & 1.72 \% & 100.00 \% & 2.7 s / GPU & \\
AEACV (RAFT-based) & & 1.52 \% & 2.72 \% & 1.72 \% & 100.00 \% & 0.41 s / 1 core & \\
DKT-IGEV & & 1.46 \% & 3.05 \% & 1.72 \% & 100.00 \% & 0.18 s / 1 core & J. Zhang, J. Li, L. Huang, X. Yu, L. Gu, J. Zheng and X. Bai: Robust Synthetic-to-Real Transfer for Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
GINet+ANE filter & & 1.45 \% & 3.07 \% & 1.72 \% & 100.00 \% & 0.11 s / 4 cores & \\
ls & & 1.53 \% & 2.69 \% & 1.72 \% & 100.00 \% & 0.32 s / 1 core & \\
GPDF-Net & & 1.41 \% & 3.33 \% & 1.73 \% & 100.00 \% & 0.2 s / 1 core & \\
Patchmatch Stereo++ & & 1.55 \% & 2.71 \% & 1.74 \% & 100.00 \% & 0.2 s / & W. Ren, Q. Liao, Z. Shao, X. Lin, X. Yue, Y. Zhang and Z. Lu: Patchmatch Stereo++: Patchmatch Binocular Stereo with Continuous Disparity Optimization. Proceedings of the 31st ACM International Conference on Multimedia 2023.\\
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.\\
NeXt-Stereo & & 1.51 \% & 2.93 \% & 1.75 \% & 100.00 \% & 0.06 s / 1 core & \\
4D-IteraStereo & & 1.60 \% & 2.48 \% & 1.75 \% & 100.00 \% & 0.4 s / GPU & \\
TEEV1 & & 1.52 \% & 2.98 \% & 1.76 \% & 100.00 \% & 0.32 s / 2 cores & \\
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.\\
UNI & & 1.51 \% & 3.06 \% & 1.77 \% & 100.00 \% & 2 s / 1 core & \\
D2Stereo & & 1.58 \% & 2.70 \% & 1.77 \% & 100.00 \% & 0.25 s / GPU & \\
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 .\\
GHUStereo-4-nce & & 1.48 \% & 3.21 \% & 1.77 \% & 100.00 \% & 0.034 s / & \\
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.\\
ClearDepth & & 1.58 \% & 2.74 \% & 1.77 \% & 100.00 \% & 0.47 s / GPU & \\
IVF-Astereo & & 1.62 \% & 2.56 \% & 1.78 \% & 100.00 \% & 0.15 s / GPU & \\
DVANet & & 1.47 \% & 3.32 \% & 1.78 \% & 100.00 \% & 0.03 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.\\
FSCN & & 1.57 \% & 2.91 \% & 1.79 \% & 100.00 \% & 0.05 s / 1 core & \\
RT-IGEV++ & & 1.48 \% & 3.37 \% & 1.79 \% & 100.00 \% & 0.05 s / 1 core & G. Xu, X. Wang, Z. Zhang, J. Cheng, C. Liao and X. Yang: IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching. arXiv preprint arXiv:2409.00638 2024.\\
DIGEV & & 1.66 \% & 2.47 \% & 1.80 \% & 100.00 \% & 0.18 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.\\
FGDS-Net & & 1.47 \% & 3.53 \% & 1.81 \% & 100.00 \% & 0.3 s / 1 core & \\
TemporalStereo & mv & 1.61 \% & 2.78 \% & 1.81 \% & 100.00 \% & 0.04 s / 1 core & Y. Zhang, M. Poggi and S. Mattoccia: TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network. IROS 2023.\\
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.\\
ScaleRAFT & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / 1 core & \\
MonoFusion & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.7 s / GPU & \\
FP-TTC & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.15 s / 1 core & \\
ScaleFlow++RBO & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / GPU & \\
ScaleFlow++\_SAG & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.05 s / GPU & \\
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. TPAMI 2023.\\
Scale-flow-ADF58 & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / 1 core & \\
GAOSF & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1 s / GPU & \\
GS58\_ScaleRES & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / GPU & \\
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.\\
RAFT-3D++ & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.5 s / 1 core & \\
ScaleFlow++ & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / GPU & \\
GS\_ScaleFlow & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / GPU & \\
ScaleRAFTRBO & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / 1 core & \\
OAMaskFlow & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.5 s / GPU & \\
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.\\
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.\\
ADF\_RESScaleFlow & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 s / GPU & \\
GS58\_Scale & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.1 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.\\
Self-scale-flow-nerf & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 0.2 s / 1 core & \\
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.\\
Urban-3D & & 1.54 \% & 3.16 \% & 1.81 \% & 100.00 \% & 0.14 s / GPU & \\
AIO-Stereo & & 1.63 \% & 2.72 \% & 1.82 \% & 100.00 \% & 0.23 s / 1 core & \\
Sn-Stereo & & 1.66 \% & 2.63 \% & 1.82 \% & 100.00 \% & 0.35 s / GPU & \\
LightStereo-L* & & 1.60 \% & 2.92 \% & 1.82 \% & 100.00 \% & 0.05 s / 1 core & X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation. arXiv preprint arXiv:2406.19833 2024.\\
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.\\
LoS\_RVC & & 1.58 \% & 3.08 \% & 1.83 \% & 100.00 \% & 0.19 s / 1 core & K. Li, L. Wang, Y. Zhang, K. Xue, S. Zhou and Y. Guo: LoS: Local Structure Guided Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024.\\
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.\\
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.\\
HCR & & 1.51 \% & 3.51 \% & 1.85 \% & 100.00 \% & 0.19 s / GPU & Y. Tuming Yuan: Hourglass cascaded recurrent stereo matching network. Image and Vision computing 2024.\\
GHUStereo-4-gwce & & 1.50 \% & 3.64 \% & 1.85 \% & 100.00 \% & 0.036 s / & \\
UCFNet\_RVC & & 1.57 \% & 3.33 \% & 1.86 \% & 100.00 \% & 0.21 s / GPU & Z. Shen, X. Song, Y. Dai, D. Zhou, Z. Rao and L. Zhang: Digging Into Uncertainty-Based Pseudo- Label for Robust Stereo Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 2023.\\
MAF-Stereo & & 1.62 \% & 3.15 \% & 1.87 \% & 100.00 \% & 0.07 s / GPU & \\
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.Z. Shen, X. Song, Y. Dai, D. Zhou, Z. Rao and L. Zhang: Digging Into Uncertainty-Based Pseudo- Label for Robust Stereo Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 2023.\\
non-parametric & & 1.56 \% & 3.49 \% & 1.88 \% & 100.00 \% & 0.34 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.\\
DCVSMNet & & 1.60 \% & 3.33 \% & 1.89 \% & 100.00 \% & 0.07 s / GPU & M. Tahmasebi, S. Huq, K. Meehan and M. McAfee: DCVSMNet: Double Cost Volume Stereo Matching Network. 2024.\\
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.\\
DualNet* & & 1.63 \% & 3.36 \% & 1.92 \% & 100.00 \% & 0.17 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.\\
UAIStereo & & 1.66 \% & 3.26 \% & 1.92 \% & 100.00 \% & 0.06 s / GPU & \\
LightStereo-L & & 1.78 \% & 2.64 \% & 1.93 \% & 100.00 \% & 0.03 s / 1 core & X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation. arXiv preprint arXiv:2406.19833 2024.\\
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 & J. Zeng, C. Yao, L. Yu, Y. Wu and Y. Jia: Parameterized Cost Volume for Stereo Matching. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.\\
CBUR & & 1.65 \% & 3.33 \% & 1.93 \% & 100.00 \% & 0.05 s / GPU & \\
FusionStereo & & 1.60 \% & 3.67 \% & 1.94 \% & 100.00 \% & 16 s / 1 core & \\
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.\\
CCAStereo & & 1.58 \% & 3.81 \% & 1.95 \% & 100.00 \% & 0.05 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.Z. Shen, X. Song, Y. Dai, D. Zhou, Z. Rao and L. Zhang: Digging Into Uncertainty-Based Pseudo- Label for Robust Stereo Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 2023.\\
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.\\
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.\\
GEMA-Stereo & & 1.66 \% & 3.65 \% & 1.99 \% & 100.00 \% & 0.03 s / GPU & \\
ICGNet-gwc & & 1.62 \% & 3.90 \% & 2.00 \% & 100.00 \% & 0.15 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.\\
SG\_PSMnet & & 1.77 \% & 3.13 \% & 2.00 \% & 100.00 \% & 1 s / 1 core & \\
Fast-ACVNet+ & & 1.70 \% & 3.53 \% & 2.01 \% & 100.00 \% & 0.05 s / & G. Xu, Y. Wang, J. Cheng, J. Tang and X. Yang: Accurate and efficient stereo matching via attention concatenation volume. IEEE Transactions on Pattern Analysis and Machine Intelligence 2023.\\
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.\\
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.\\
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.\\
LightStereo-M & & 1.81 \% & 3.22 \% & 2.04 \% & 100.00 \% & 0.02 s / 1 core & X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation. arXiv preprint arXiv:2406.19833 2024.\\
CFNet\_SFC & & 1.75 \% & 3.53 \% & 2.05 \% & 100.00 \% & 0.12 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.\\
iRaftStereo\_RVC & & 1.88 \% & 3.03 \% & 2.07 \% & 100.00 \% & 0.5 s / GPU & H. Jiang, R. Xu and W. Jiang: An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022. arXiv preprint arXiv:2210.12785 2022.\\
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.\\
GHUStereo-8-gwce & & 1.88 \% & 3.34 \% & 2.12 \% & 100.00 \% & 0.021 s / & \\
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.\\
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.\\
SG & & 1.75 \% & 4.13 \% & 2.15 \% & 100.00 \% & 1 s / 1 core & \\
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.\\
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.\\
MCVFNet & & 1.82 \% & 3.94 \% & 2.18 \% & 100.00 \% & 0.029 s / & \\
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.\\
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.\\
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.\\
Q & & 1.84 \% & 4.05 \% & 2.21 \% & 100.00 \% & 1 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.\\
AGDNet & & 1.77 \% & 4.44 \% & 2.22 \% & 100.00 \% & 0.08 s / 2 cores & \\
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.\\
GHUStereo-8-nce & & 1.92 \% & 3.79 \% & 2.23 \% & 100.00 \% & 0.019 s / & \\
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.\\
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.\\
OpenStereo-PSMNet & & 1.80 \% & 4.58 \% & 2.26 \% & 100.00 \% & 0.21 s / & X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline. arXiv preprint arXiv:2312.00343 2023.\\
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.\\
LightStereo-S & & 2.00 \% & 3.80 \% & 2.30 \% & 100.00 \% & 0.01 s / 1 core & X. Guo, C. Zhang, D. Nie, W. Zheng, Y. Zhang and L. Chen: LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation. arXiv preprint arXiv:2406.19833 2024.\\
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.\\
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.\\
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. .\\
GhostStereoNet & & 1.91 \% & 5.08 \% & 2.44 \% & 100.00 \% & 0.04 s / GPU & \\
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 & & 2.07 \% & 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.\\
GASN-FA & & 2.25 \% & 4.13 \% & 2.56 \% & 100.00 \% & 0.05 s / & \\
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.\\
LI-ACVNet & & 2.20 \% & 4.59 \% & 2.60 \% & 100.00 \% & 0.14 s / GPU & \\
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.\\
CKDNet\_1.0 & & 2.26 \% & 5.02 \% & 2.72 \% & 100.00 \% & 1 s / 1 core & \\
SG\_small & & 2.29 \% & 4.95 \% & 2.73 \% & 100.00 \% & 1 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.\\
DualNet & & 2.46 \% & 5.25 \% & 2.92 \% & 100.00 \% & 0.17 s / 1 core & \\
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.\\
CKDNet\_0.5 & & 2.35 \% & 6.70 \% & 3.07 \% & 100.00 \% & 1 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.\\
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.\\
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.\\
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.\\
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.\\
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.\\
CKDNet\_0.3 & & 2.84 \% & 7.77 \% & 3.66 \% & 100.00 \% & 1 s / 1 core & \\
SMFormer & & 2.79 \% & 8.17 \% & 3.68 \% & 100.00 \% & 0.36 s / GPU & \\
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.\\
CAS++ & & 3.10 \% & 6.89 \% & 3.73 \% & 99.98 \% & .1 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
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.\\
DualNet-one stage & & 2.89 \% & 8.73 \% & 3.86 \% & 100.00 \% & 0.17 s / 1 core & \\
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.\\
ADCPNet & & 3.27 \% & 7.58 \% & 3.98 \% & 100.00 \% & 0.007 s / GPU & H. Dai, X. Zhang, Y. Zhao, H. Sun and N. Zheng: Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching. IEEE Transactions on Circuits and Systems for Video Technology 2022.\\
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.\\
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.\\
SSMNet & & 3.93 \% & 7.85 \% & 4.58 \% & 100.00 \% & 0.01 s / GPU & \\
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.\\
P3SNet+ & & 4.15 \% & 7.59 \% & 4.72 \% & 100.00 \% & 0.01 s / 1 core & A. Emlek and M. Peker: P3SNet: Parallel Pyramid Pooling Stereo Network. IEEE Transactions on Intelligent Transportation Systems 2023.\\
SAFT-Stereo & & 3.44 \% & 11.48 \% & 4.78 \% & 100.00 \% & 0.007 s / & \\
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.\\
Anonymous & fl & 4.07 \% & 9.41 \% & 4.96 \% & 100.00 \% & 0.1 s / GPU & \\
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.\\
P3SNet & & 4.40 \% & 8.28 \% & 5.05 \% & 100.00 \% & 0.01 s / GPU & A. Emlek and M. Peker: P3SNet: Parallel Pyramid Pooling Stereo Network. IEEE Transactions on Intelligent Transportation Systems 2023.\\
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.\\
StereoVAE & & 4.25 \% & 10.18 \% & 5.23 \% & 100.00 \% & 0.03 s / & Q. Chang, X. Li, X. Xu, X. Liu, Y. Li and J. Miyazaki: StereoVAE: A lightweight stereo matching system using embedded GPUs. International Conference on Robotics and Automation 2023.\\
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.\\
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.\\
UFD-PRiME & st fl & 3.66 \% & 15.05 \% & 5.55 \% & 100.00 \% & 0.55 s / GPU & \\
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.\\
TinyStereo & & 4.99 \% & 9.33 \% & 5.71 \% & 100.00 \% & 0.02 s / & Q. Chang, X. Xu, A. Zha, M. Er, Y. Sun and Y. Li: TinyStereo: A Tiny Coarse-to-Fine Framework for Vision-Based Depth Estimation on Embedded GPUs. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024.\\
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.\\
StereoNet\_unsup\_DMB & & 4.68 \% & 12.06 \% & 5.91 \% & 100.00 \% & 0.02 min / GPU & \\
CFNet\_unsup\_DMB & & 4.64 \% & 12.33 \% & 5.92 \% & 100.00 \% & 0.02 s / GPU & \\
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.\\
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.\\
UHP & & 5.00 \% & 13.70 \% & 6.45 \% & 100.00 \% & 0.02 s / GPU & R. Yang, X. Li, R. Cong and J. Du: Unsupervised Hierarchical Iterative Tile Refinement Network with 3D Planar Segmentation Loss. IEEE Robotics and Automation Letters 2024.\\
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.\\
spsm-gan & & 5.42 \% & 12.84 \% & 6.65 \% & 100.00 \% & 0.8 s / GPU & \\
PASMnet\_DMB & & 5.24 \% & 13.96 \% & 6.69 \% & 100.00 \% & 10 s / 1 core & \\
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.\\
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.\\
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.\\
StereoNet\_unsup & & 7.31 \% & 17.77 \% & 9.05 \% & 99.96 \% & 0.02 s / GPU & \\
CFNet\_Sup & & 7.22 \% & 18.54 \% & 9.11 \% & 100.00 \% & 0.05 s / GPU & \\
SGM-DMB & & 7.96 \% & 16.68 \% & 9.41 \% & 99.98 \% & 10 s / GPU & \\
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.\\
EMR-MSF & fl & 8.61 \% & 15.15 \% & 9.70 \% & 100.00 \% & 0.25 s / GPU & Z. Jiang and M. Okutomi: EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.\\
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.\\
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.\\
SGM & & 8.95 \% & 20.55 \% & 10.88 \% & 99.77 \% & 10 s / 1 core & \\
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.\\
3DG-DVO & fl & 12.94 \% & 26.10 \% & 15.13 \% & 100.00 \% & 0.04 s / GPU & \\
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.\\
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.\\
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.\\
Stereo-RSSF & fl & 56.60 \% & 73.05 \% & 59.34 \% & 9.26 \% & 2.5 s / 8 core & E. Salehi, A. Aghagolzadeh and R. Hosseini: Stereo-RSSF: stereo robust sparse scene-flow estimation. The Visual Computer 2023.
\end{tabular}