\begin{tabular}{c | c | c | c | c | c | c | c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf D1-bg} & {\bf D1-fg} & {\bf D1-all} & {\bf D2-bg} & {\bf D2-fg} & {\bf D2-all} & {\bf Fl-bg} & {\bf Fl-fg} & {\bf Fl-all} & {\bf SF-bg} & {\bf SF-fg} & {\bf SF-all} & {\bf Density} & {\bf Runtime} & {\bf Environment}\\ \hline
SplatFlow3D & & 1.40 \% & 2.91 \% & 1.65 \% & 1.88 \% & 6.29 \% & 2.61 \% & 2.27 \% & 6.02 \% & 2.89 \% & 2.84 \% & 10.04 \% & 4.04 \% & 100.00 \% & 0.2 s / GPU & \\
ScaleRAFTRBO & & 1.48 \% & 3.46 \% & 1.81 \% & 1.93 \% & 7.72 \% & 2.89 \% & 2.27 \% & 5.63 \% & 2.83 \% & 2.86 \% & 10.91 \% & 4.20 \% & 100.00 \% & 0.1 s / 1 core & \\
RBO & & 1.48 \% & 3.46 \% & 1.81 \% & 1.91 \% & 8.62 \% & 3.02 \% & 2.13 \% & 6.15 \% & 2.80 \% & 2.72 \% & 11.68 \% & 4.21 \% & 100.00 \% & 1 s / 1 core & \\
GAOSF & & 1.48 \% & 3.46 \% & 1.81 \% & 1.92 \% & 8.39 \% & 2.99 \% & 2.08 \% & 7.37 \% & 2.96 \% & 2.65 \% & 12.27 \% & 4.25 \% & 100.00 \% & 1 s / GPU & \\
CamLiRAFT & & 1.48 \% & 3.46 \% & 1.81 \% & 1.91 \% & 8.11 \% & 2.94 \% & 2.08 \% & 7.37 \% & 2.96 \% & 2.68 \% & 12.16 \% & 4.26 \% & 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.\\
RAFT-3D++ & & 1.48 \% & 3.46 \% & 1.81 \% & 1.91 \% & 9.05 \% & 3.10 \% & 2.09 \% & 8.55 \% & 3.16 \% & 2.63 \% & 12.87 \% & 4.34 \% & 100.00 \% & 0.5 s / 1 core & \\
EFLOW & & 1.40 \% & 2.91 \% & 1.65 \% & 1.90 \% & 7.50 \% & 2.84 \% & 2.27 \% & 7.10 \% & 3.07 \% & 2.87 \% & 11.69 \% & 4.34 \% & 100.00 \% & 0.06 s / 1 core & \\
CamLiFlow & & 1.48 \% & 3.46 \% & 1.81 \% & 1.92 \% & 8.14 \% & 2.95 \% & 2.31 \% & 7.04 \% & 3.10 \% & 2.87 \% & 12.23 \% & 4.43 \% & 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.\\
M-FUSE & mv & 1.40 \% & 2.91 \% & 1.65 \% & 2.14 \% & 8.10 \% & 3.13 \% & 2.66 \% & 7.47 \% & 3.46 \% & 3.43 \% & 11.84 \% & 4.83 \% & 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.\\
RigidMask+ISF & & 1.53 \% & 3.65 \% & 1.89 \% & 2.09 \% & 8.92 \% & 3.23 \% & 2.63 \% & 7.85 \% & 3.50 \% & 3.25 \% & 13.08 \% & 4.89 \% & 100.00 \% & 3.3 s / GPU & G. Yang and D. Ramanan: Learning to Segment Rigid Motions from Two Frames. CVPR 2021.\\
RAFT3DMR & & 1.48 \% & 3.46 \% & 1.81 \% & 1.92 \% & 8.38 \% & 3.00 \% & 2.52 \% & 9.81 \% & 3.74 \% & 3.05 \% & 14.39 \% & 4.94 \% & 100.00 \% & 1 s / 1 core & \\
CamLiRAFT-NR & & 1.48 \% & 3.46 \% & 1.81 \% & 2.05 \% & 7.86 \% & 3.02 \% & 2.76 \% & 6.78 \% & 3.43 \% & 3.64 \% & 11.66 \% & 4.97 \% & 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.\\
SF2SE3 & & 1.40 \% & 2.91 \% & 1.65 \% & 2.20 \% & 7.66 \% & 3.11 \% & 3.17 \% & 8.79 \% & 4.11 \% & 3.75 \% & 13.15 \% & 5.32 \% & 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.\\
RAFT-3D & & 1.48 \% & 3.46 \% & 1.81 \% & 2.51 \% & 9.46 \% & 3.67 \% & 3.39 \% & 8.79 \% & 4.29 \% & 4.27 \% & 13.27 \% & 5.77 \% & 100.00 \% & 2 s / GPU & Z. Teed and J. Deng: RAFT-3D: Scene Flow using Rigid-Motion Embeddings. arXiv preprint arXiv:2012.00726 2020.\\
MonoFusion & & 1.48 \% & 3.46 \% & 1.81 \% & 2.34 \% & 9.18 \% & 3.47 \% & 3.93 \% & 5.97 \% & 4.27 \% & 4.81 \% & 11.61 \% & 5.94 \% & 100.00 \% & 0.7 s / GPU & \\
ScaleR & & 1.48 \% & 3.46 \% & 1.81 \% & 2.14 \% & 8.33 \% & 3.17 \% & 4.25 \% & 5.29 \% & 4.43 \% & 5.10 \% & 10.98 \% & 6.08 \% & 100.00 \% & 0.2 s / 1 core & \\
ScaleRAFT & & 1.48 \% & 3.46 \% & 1.81 \% & 2.31 \% & 7.42 \% & 3.16 \% & 4.45 \% & 4.76 \% & 4.50 \% & 5.31 \% & 10.18 \% & 6.12 \% & 100.00 \% & 0.1 s / 1 core & \\
UberATG-DRISF & & 2.16 \% & 4.49 \% & 2.55 \% & 2.90 \% & 9.73 \% & 4.04 \% & 3.59 \% & 10.40 \% & 4.73 \% & 4.39 \% & 15.94 \% & 6.31 \% & 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.\\
Scale-flow-ADF58 & & 1.48 \% & 3.46 \% & 1.81 \% & 2.44 \% & 7.95 \% & 3.36 \% & 4.36 \% & 7.00 \% & 4.80 \% & 5.28 \% & 11.71 \% & 6.35 \% & 100.00 \% & 0.1 s / 1 core & \\
Scale-flow & & 1.48 \% & 3.46 \% & 1.81 \% & 2.55 \% & 8.24 \% & 3.50 \% & 5.24 \% & 5.71 \% & 5.32 \% & 6.06 \% & 11.32 \% & 6.94 \% & 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.\\
ACOSF & & 2.79 \% & 7.56 \% & 3.58 \% & 3.82 \% & 12.74 \% & 5.31 \% & 4.56 \% & 12.00 \% & 5.79 \% & 5.61 \% & 19.38 \% & 7.90 \% & 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.\\
ISF & & 4.12 \% & 6.17 \% & 4.46 \% & 4.88 \% & 11.34 \% & 5.95 \% & 5.40 \% & 10.29 \% & 6.22 \% & 6.58 \% & 15.63 \% & 8.08 \% & 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.\\
Stereo expansion & & 1.48 \% & 3.46 \% & 1.81 \% & 3.39 \% & 8.54 \% & 4.25 \% & 5.83 \% & 8.66 \% & 6.30 \% & 7.06 \% & 13.44 \% & 8.12 \% & 100.00 \% & 2 s / GPU & G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow through Optical Expansion. CVPR 2020.\\
Binary TTC & & 1.48 \% & 3.46 \% & 1.81 \% & 3.84 \% & 9.39 \% & 4.76 \% & 5.84 \% & 8.67 \% & 6.31 \% & 7.45 \% & 13.74 \% & 8.50 \% & 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.\\
PRSM & mv & 3.02 \% & 10.52 \% & 4.27 \% & 5.13 \% & 15.11 \% & 6.79 \% & 5.33 \% & 13.40 \% & 6.68 \% & 6.61 \% & 20.79 \% & 8.97 \% & 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.\\
DTF\_SENSE & mv & 2.08 \% & 3.13 \% & 2.25 \% & 4.82 \% & 9.02 \% & 5.52 \% & 7.31 \% & 9.48 \% & 7.67 \% & 8.21 \% & 14.08 \% & 9.18 \% & 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.\\
OSF+TC & mv & 4.11 \% & 9.64 \% & 5.03 \% & 5.18 \% & 15.12 \% & 6.84 \% & 5.76 \% & 13.31 \% & 7.02 \% & 7.08 \% & 20.03 \% & 9.23 \% & 100.00 \% & 50 min / 1 core & M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.\\
SENSE & & 2.07 \% & 3.01 \% & 2.22 \% & 4.90 \% & 10.83 \% & 5.89 \% & 7.30 \% & 9.33 \% & 7.64 \% & 8.36 \% & 15.49 \% & 9.55 \% & 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.\\
OSF 2018 & & 4.11 \% & 11.12 \% & 5.28 \% & 5.01 \% & 17.28 \% & 7.06 \% & 5.38 \% & 17.61 \% & 7.41 \% & 6.68 \% & 24.59 \% & 9.66 \% & 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.\\
SSF & & 3.55 \% & 8.75 \% & 4.42 \% & 4.94 \% & 17.48 \% & 7.02 \% & 5.63 \% & 14.71 \% & 7.14 \% & 7.18 \% & 24.58 \% & 10.07 \% & 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.\\
OSF & & 4.54 \% & 12.03 \% & 5.79 \% & 5.45 \% & 19.41 \% & 7.77 \% & 5.62 \% & 18.92 \% & 7.83 \% & 7.01 \% & 26.34 \% & 10.23 \% & 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.\\
DWARF & & 3.20 \% & 3.94 \% & 3.33 \% & 6.21 \% & 9.38 \% & 6.73 \% & 9.80 \% & 13.37 \% & 10.39 \% & 11.72 \% & 18.06 \% & 12.78 \% & 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.\\
SFF++ & mv & 4.27 \% & 12.38 \% & 5.62 \% & 7.31 \% & 18.12 \% & 9.11 \% & 10.63 \% & 17.48 \% & 11.77 \% & 12.44 \% & 25.33 \% & 14.59 \% & 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.\\
DTF\_PWOC & mv & 3.91 \% & 8.57 \% & 4.68 \% & 6.25 \% & 14.03 \% & 7.55 \% & 10.78 \% & 19.99 \% & 12.31 \% & 12.42 \% & 25.74 \% & 14.64 \% & 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.\\
Self-SuperFlow-ft & & 3.81 \% & 8.92 \% & 4.66 \% & 7.13 \% & 16.27 \% & 8.65 \% & 10.65 \% & 19.44 \% & 12.12 \% & 12.33 \% & 26.73 \% & 14.73 \% & 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.\\
FSF+MS & ms mv & 5.72 \% & 11.84 \% & 6.74 \% & 7.57 \% & 21.28 \% & 9.85 \% & 8.48 \% & 25.43 \% & 11.30 \% & 11.17 \% & 33.91 \% & 14.96 \% & 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.\\
PWOC-3D & & 4.19 \% & 9.82 \% & 5.13 \% & 7.21 \% & 14.73 \% & 8.46 \% & 12.40 \% & 15.78 \% & 12.96 \% & 14.30 \% & 22.66 \% & 15.69 \% & 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.\\
CSF & & 4.57 \% & 13.04 \% & 5.98 \% & 7.92 \% & 20.76 \% & 10.06 \% & 10.40 \% & 25.78 \% & 12.96 \% & 12.21 \% & 33.21 \% & 15.71 \% & 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.\\
SceneFFields & & 5.12 \% & 13.83 \% & 6.57 \% & 8.47 \% & 21.83 \% & 10.69 \% & 10.58 \% & 24.41 \% & 12.88 \% & 12.48 \% & 32.28 \% & 15.78 \% & 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.\\
Self-scale-flow-nerf & & 1.48 \% & 3.46 \% & 1.81 \% & 8.07 \% & 10.52 \% & 8.47 \% & 13.08 \% & 15.45 \% & 13.47 \% & 15.17 \% & 20.18 \% & 16.01 \% & 100.00 \% & 0.2 s / 1 core & \\
PR-Sceneflow & & 4.74 \% & 13.74 \% & 6.24 \% & 11.14 \% & 20.47 \% & 12.69 \% & 11.73 \% & 24.33 \% & 13.83 \% & 13.49 \% & 31.22 \% & 16.44 \% & 100.00 \% & 150 s / 4 core & C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.\\
SPS+FF++ & & 5.47 \% & 12.19 \% & 6.59 \% & 13.06 \% & 20.83 \% & 14.35 \% & 15.91 \% & 20.27 \% & 16.64 \% & 18.98 \% & 29.51 \% & 20.73 \% & 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.\\
Anonymous & & 4.07 \% & 9.41 \% & 4.96 \% & 12.75 \% & 14.42 \% & 13.02 \% & 17.91 \% & 18.08 \% & 17.93 \% & 21.07 \% & 25.08 \% & 21.74 \% & 100.00 \% & 0.1 s / GPU & \\
Mono-SF & & 14.21 \% & 26.94 \% & 16.32 \% & 16.89 \% & 33.07 \% & 19.59 \% & 11.40 \% & 19.64 \% & 12.77 \% & 19.79 \% & 39.57 \% & 23.08 \% & 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.\\
SGM+SF & & 5.15 \% & 15.29 \% & 6.84 \% & 14.10 \% & 23.13 \% & 15.60 \% & 20.91 \% & 25.50 \% & 21.67 \% & 23.09 \% & 34.46 \% & 24.98 \% & 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.\\
MonoComb & & 17.89 \% & 21.16 \% & 18.44 \% & 22.34 \% & 25.85 \% & 22.93 \% & 5.84 \% & 8.67 \% & 6.31 \% & 27.06 \% & 33.55 \% & 28.14 \% & 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.\\
Self-SuperFlow & & 5.78 \% & 19.76 \% & 8.11 \% & 19.88 \% & 30.03 \% & 21.57 \% & 22.70 \% & 28.55 \% & 23.67 \% & 26.31 \% & 40.72 \% & 28.71 \% & 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.\\
PCOF-LDOF & & 6.31 \% & 19.24 \% & 8.46 \% & 19.09 \% & 30.54 \% & 20.99 \% & 14.34 \% & 38.32 \% & 18.33 \% & 25.26 \% & 49.39 \% & 29.27 \% & 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.\\
PCOF + ACTF & & 6.31 \% & 19.24 \% & 8.46 \% & 19.15 \% & 36.27 \% & 22.00 \% & 14.89 \% & 60.15 \% & 22.43 \% & 25.77 \% & 67.75 \% & 32.76 \% & 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.\\
Multi-Mono-SF-ft & mv & 21.60 \% & 28.22 \% & 22.71 \% & 25.47 \% & 31.72 \% & 26.51 \% & 12.41 \% & 18.20 \% & 13.37 \% & 31.18 \% & 42.68 \% & 33.09 \% & 100.00 \% & 0.06 s / & J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.\\
SGM&FlowFie+ & & 11.93 \% & 20.57 \% & 13.37 \% & 27.02 \% & 31.71 \% & 27.80 \% & 22.83 \% & 22.75 \% & 22.82 \% & 32.26 \% & 40.12 \% & 33.57 \% & 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.\\
Self-Mono-SF-ft & & 20.72 \% & 29.41 \% & 22.16 \% & 23.83 \% & 32.29 \% & 25.24 \% & 15.51 \% & 17.96 \% & 15.91 \% & 31.51 \% & 45.77 \% & 33.88 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
RAFT-MSF & & 18.10 \% & 36.82 \% & 21.21 \% & 24.98 \% & 40.19 \% & 27.51 \% & 17.98 \% & 20.33 \% & 18.37 \% & 31.59 \% & 51.97 \% & 34.98 \% & 100.00 \% & 0.18 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
SGM+C+NL & & 5.15 \% & 15.29 \% & 6.84 \% & 28.77 \% & 25.65 \% & 28.25 \% & 34.24 \% & 42.46 \% & 35.61 \% & 38.21 \% & 50.95 \% & 40.33 \% & 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.\\
3DG-DVO & & 7.62 \% & 11.44 \% & 8.26 \% & 21.56 \% & 20.84 \% & 21.44 \% & 34.04 \% & 50.52 \% & 36.78 \% & 37.94 \% & 56.97 \% & 41.11 \% & 100.00 \% & 0.04 s / GPU & \\
SGM+LDOF & & 5.15 \% & 15.29 \% & 6.84 \% & 29.58 \% & 23.48 \% & 28.56 \% & 40.81 \% & 31.92 \% & 39.33 \% & 43.99 \% & 42.09 \% & 43.67 \% & 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.\\
Multi-Mono-SF & mv & 27.48 \% & 47.30 \% & 30.78 \% & 32.39 \% & 44.56 \% & 34.41 \% & 18.13 \% & 26.59 \% & 19.54 \% & 40.29 \% & 62.78 \% & 44.04 \% & 100.00 \% & 0.06 s / & J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.\\
DWBSF & & 19.61 \% & 22.69 \% & 20.12 \% & 35.72 \% & 28.15 \% & 34.46 \% & 40.74 \% & 31.16 \% & 39.14 \% & 46.42 \% & 40.76 \% & 45.48 \% & 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.\\
Self-Mono-SF & & 31.22 \% & 48.04 \% & 34.02 \% & 34.89 \% & 43.59 \% & 36.34 \% & 23.26 \% & 24.93 \% & 23.54 \% & 46.68 \% & 63.82 \% & 49.54 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
GCSF & & 11.64 \% & 27.11 \% & 14.21 \% & 32.94 \% & 35.77 \% & 33.41 \% & 47.38 \% & 41.50 \% & 46.40 \% & 52.92 \% & 56.68 \% & 53.54 \% & 100.00 \% & 2.4 s / 1 core & J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.\\
VSF & & 27.31 \% & 21.72 \% & 26.38 \% & 59.51 \% & 44.93 \% & 57.08 \% & 50.06 \% & 45.40 \% & 49.28 \% & 67.69 \% & 62.93 \% & 66.90 \% & 100.00 \% & 125 min / 1 core & F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.\\
Stereo-RSSF & & 56.60 \% & 73.05 \% & 59.34 \% & 58.86 \% & 74.41 \% & 61.45 \% & 70.68 \% & 73.60 \% & 71.17 \% & 76.21 \% & 81.62 \% & 77.11 \% & 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}