Optical Flow Evaluation 2015


The stereo 2015 / flow 2015 / scene flow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks, it comprises dynamic scenes for which the ground truth has been established in a semi-automatic process. Our evaluation server computes the percentage of bad pixels averaged over all ground truth pixels of all 200 test images. For this benchmark, we consider a pixel to be correctly estimated if the disparity or flow end-point error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity maps and the flow map). We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing disparity maps and flow fields. More details can be found in Object Scene Flow for Autonomous Vehicles (CVPR 2015).

Our evaluation table ranks all methods according to the number of erroneous pixels. All methods providing less than 100 % density have been interpolated using simple background interpolation as explained in the corresponding header file in the development kit. Legend:

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


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

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Multiview: Method uses more than 2 temporally adjacent images
  • Motion stereo: Method uses epipolar geometry for computing optical flow
  • Additional training data: Use of additional data sources for training (see details)

Evaluation ground truth        Evaluation area

Method Setting Code Fl-bg Fl-fg Fl-all Density Runtime Environment
1 CamLiRAFT
This method uses stereo information.
 code 2.08 % 7.37 % 2.96 % 100.00 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
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.
2 CamLiFlow
This method uses stereo information.
 code 2.31 % 7.04 % 3.10 % 100.00 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
H. Liu, T. Lu, Y. Xu, J. Liu, W. Li and L. Chen: CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation. CVPR 2022.
3 CamLiRAFT-NR
This method uses stereo information.
 code 2.76 % 6.78 % 3.43 % 100.00 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
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.
4 M-FUSE
This method uses stereo information.
This method makes use of multiple (>2) views.
 code 2.66 % 7.47 % 3.46 % 100.00 % 1.3 s GPU
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.
5 RigidMask+ISF
This method uses stereo information.
 code 2.63 % 7.85 % 3.50 % 100.00 % 3.3 s GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Learning to Segment Rigid Motions from Two Frames. CVPR 2021.
6 ScaleFlow3D
This method uses stereo information.
 2.37 % 9.26 % 3.51 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
7 TPCV+RAFT3D
This method uses stereo information.
 2.48 % 10.19 % 3.76 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
8 RAFT-it+_RVC code 3.62 % 5.33 % 3.90 % 100.00 % 0.14 s 1 core @ 2.5 Ghz (Python)
D. Sun, C. Herrmann, F. Reda, M. Rubinstein, D. Fleet and W. Freeman: Disentangling Architecture and Training for Optical Flow. ECCV 2022.
9 RAFT-OCTC 3.72 % 5.39 % 4.00 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
J. Jeong, J. Lin, F. Porikli and N. Kwak: Imposing Consistency for Optical Flow Estimation (Qualcomm AI Research). CVPR 2022.
10 RCA-Flow 3.67 % 6.25 % 4.10 % 100.00 % 0.16 s 1 core @ 2.5 Ghz (Python)
11 SF2SE3
This method uses stereo information.
 code 3.17 % 8.79 % 4.11 % 100.00 % 2.7 s GPU @ >3.5 Ghz (Python)
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.
12 RAFT-CF-CE-PL3 3.80 % 5.65 % 4.11 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
13 RAFT-S-AF code 3.86 % 5.38 % 4.12 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
14 MS_RAFT+_corr_RVC code 3.83 % 5.71 % 4.15 % 100.00 % 0.65 s GPU @ 2.5 Ghz (Python + C/C++)
A. Jahedi, M. Luz, L. Mehl, M. Rivinius and A. Bruhn: High Resolution Multi-Scale RAFT. Robust Vision Challenge 2022, arXiv preprint arXiv:2210.16900 2022.
A. Jahedi, L. Mehl, M. Rivinius and A. Bruhn: Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation. IEEE International Conference on Image Processing (ICIP) 2022.
15 MS_RAFT+_RVC 3.89 % 5.67 % 4.19 % 100.00 % 0.65 s GPU @ 2.5 Ghz (Python + C/C++)
16 DIP code 3.86 % 5.96 % 4.21 % 100.00 % 0.15 s 1 core @ 2.5 Ghz (Python)
Z. Zheng, N. Nie, Z. Ling, P. Xiong, J. Liu, H. Wang and J. Li: DIP: Deep Inverse Patchmatch for High- Resolution Optical Flow. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
17 APCAFlow 3.86 % 6.14 % 4.24 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
18 CrossFlow 3.92 % 5.99 % 4.27 % 100.00 % 0.4 s 1 core @ 2.5 Ghz (Python)
19 RAFT-3D
This method uses stereo information.
 3.39 % 8.79 % 4.29 % 100.00 % 2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Teed and J. Deng: RAFT-3D: Scene Flow using Rigid-Motion Embeddings. arXiv preprint arXiv:2012.00726 2020.
20 DiffusionFlow 4.02 % 5.83 % 4.32 % 100.00 % 1 s 1 core @ 2.5 Ghz (Python)
21 TransFlow 4.03 % 5.77 % 4.32 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
22 GLFlow 4.11 % 5.66 % 4.37 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
23 GMFlow_RVC code 4.16 % 5.67 % 4.41 % 100.00 % 0.2 s GPU (Python)
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.
24 AnyFlow 4.15 % 5.76 % 4.41 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
25 CCH-Flow 4.20 % 5.50 % 4.42 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
26 GAFlow-FF 4.03 % 6.39 % 4.42 % 100.00 % 0.4 s 1 core @ 2.5 Ghz (Python)
27 GMFlow+ code 4.27 % 5.60 % 4.49 % 100.00 % 0.2 s GPU (Python)
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.
28 EMD-Flow 4.21 % 6.01 % 4.51 % 100.00 % 0.14 s GPU @ 2.5 Ghz (Python)
29 GAFlow 4.15 % 6.36 % 4.52 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
30 DFlow 4.04 % 6.93 % 4.52 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
31 SeparableFlow code 4.25 % 5.92 % 4.53 % 100.00 % 0.5 s GPU
F. Zhang, O. Woodford, V. Prisacariu and P. Torr: Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
32 LSHRAFT 4.25 % 5.93 % 4.53 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
33 GCC 4.34 % 5.69 % 4.56 % 100.00 % 0.6 s 1 core @ 3.5 Ghz (Python)
34 G 4.35 % 5.84 % 4.60 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
35 KPA-Flow 4.17 % 6.77 % 4.60 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
A. Luo, F. Yang, X. Li and S. Liu: Learning Optical Flow With Kernel Patch Attention. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
36 SwinTR-RAFT code 4.32 % 6.05 % 4.61 % 100.00 % 0.6 s 8 cores @ 2.5 Ghz (Python)
37 AGM-FlowNet 4.25 % 6.42 % 4.61 % 100.00 % 0.38 s GPU @ 2.5 Ghz (Python)
38 LGUM 4.20 % 6.70 % 4.61 % 100.00 % 0.25 s 1 core @ 2.5 Ghz (Python)
39 MatchFlow(G) code 4.33 % 6.11 % 4.63 % 100.00 % 0.3 s GPU (Python)
Q. Dong, C. Cao and Y. Fu: Rethinking Optical Flow from Geometric Matching Consistent Perspective. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.
40 DGA-Flow 4.34 % 6.11 % 4.64 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
41 FlowFormer code 4.37 % 6.18 % 4.68 % 100.00 % 0.3 s GPU (Python)
Z. Huang, X. Shi, C. Zhang, Q. Wang, K. Cheung, H. Qin, J. Dai and H. Li: FlowFormer: A Transformer Architecture for Optical Flow. European conference on computer vision 2022.
42 TPCV+RAFT
This method uses stereo information.
 4.53 % 5.52 % 4.69 % 100.00 % 0.2 s 1 core 2.5ghz gpu
43 SSTM_T [MV] 4.39 % 6.40 % 4.72 % 100.00 % 0.4 s GPU @ 2.5 Ghz (C/C++)
44 MatchFlow(R) code 4.51 % 5.78 % 4.72 % 100.00 % 0.26 s GPU (Python)
Q. Dong, C. Cao and Y. Fu: Rethinking Optical Flow from Geometric Matching Consistent Perspective. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.
45 UberATG-DRISF
This method uses stereo information.
 3.59 % 10.40 % 4.73 % 100.00 % 0.75 s CPU+GPU @ 2.5 Ghz (Python)
W. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun: Deep Rigid Instance Scene Flow. CVPR 2019.
46 SSTMT++-tt-main [mv] code 4.36 % 6.65 % 4.74 % 100.00 % 0.4 s GPU @ 2.5 Ghz (Python)
47 SKII 4.57 % 5.66 % 4.75 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
48 ErrorMatch-RAFT code 4.46 % 6.23 % 4.75 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
49 ErrorMatch-GMA code 4.53 % 5.87 % 4.75 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
50 Super 4.43 % 6.43 % 4.76 % 100.00 % 0.07 s GPU @ 2.5 Ghz (Python)
51 RAFT-A code 4.54 % 5.99 % 4.78 % 100.00 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
D. Sun, D. Vlasic, C. Herrmann, V. Jampani, M. Krainin, H. Chang, R. Zabih, W. Freeman and C. Liu: AutoFlow: Learning a Better Training Set for Optical Flow. CVPR 2021.
52 EMD-Flow 4.41 % 6.65 % 4.78 % 100.00 % 0.11 s GPU @ 2.5 Ghz (Python)
53 CRAFT code 4.58 % 5.85 % 4.79 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
X. Sui, S. Li, X. Geng, Y. Wu, X. Xu, Y. Liu, R. Goh and H. Zhu: CRAFT: Cross-Attentional Flow Transformers for Robust Optical Flow. CVPR 2022.
54 GMFlowNet code 4.39 % 6.84 % 4.79 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
S. Zhao, L. Zhao, Z. Zhang, E. Zhou and D. Metaxas: Global Matching with Overlapping Attention for Optical Flow Estimation. CVPR 2022.
55 SKFlow code 4.64 % 5.83 % 4.84 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
56 RAFT-DFlow 4.52 % 6.48 % 4.84 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
57 FTGAN 4.48 % 6.81 % 4.87 % 100.00 % 0.25 s 2 cores @ 2.5 Ghz (C/C++)
58 MS_RAFT 4.58 % 6.38 % 4.88 % 100.00 % 0.3 s GPU: Nvidia A100 (Python)
A. Jahedi, L. Mehl, M. Rivinius and A. Bruhn: Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation. IEEE International Conference on Image Processing (ICIP) 2022.
59 AGFlow code 4.52 % 6.75 % 4.89 % 100.00 % 0.2 s 8 cores @ 2.5 Ghz (Python)
A. Luo, F. Yang, K. Luo, X. Li, H. Fan and S. Liu: Learning Optical Flow with Adaptive Graph Reasoning. AAAI 2022.
60 DEQ-Flow-H code 4.68 % 6.06 % 4.91 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
S. Bai, Z. Geng, Y. Savani and Z. Kolter: Deep Equilibrium Optical Flow Estimation. CVPR 2022.
61 RR-RAFT code 4.63 % 6.29 % 4.91 % 100.00 % 1 s 1 core @ 2.5 Ghz (Python)
62 CVE-RAFT 4.53 % 6.94 % 4.93 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
63 optical_flow3D
This method uses stereo information.
 4.78 % 5.77 % 4.94 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
64 GL 4.55 % 6.89 % 4.94 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
65 CVEFlow 4.57 % 6.83 % 4.94 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
66 DynOFlow 4.59 % 6.75 % 4.95 % 100.00 % 0.12 s NVIDIA RTX 3090 (PyTorch)
67 cgcv code 4.61 % 6.68 % 4.96 % 100.00 % 0.27 s 1 core @ 2.5 Ghz (C/C++)
68 MCPFlow_RVC 4.71 % 6.27 % 4.97 % 100.00 % 0.38 s GPU @ 2.5 Ghz (Python)
69 CVM code 4.72 % 6.24 % 4.97 % 100.00 % 0.27 s GPU @ 2.5 Ghz (Python)
70 DFlow-test 4.65 % 6.60 % 4.98 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
71 SSTMT++-main [mv] 4.62 % 6.76 % 4.98 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
72 anonymous code 4.71 % 6.34 % 4.98 % 100.00 % 0.20 s GPU @ 2.5 Ghz (C/C++)
73 GMA-RECLoss 4.64 % 6.75 % 4.99 % 100.00 % 0.11 s 1 core @ 2.5 Ghz (Python)
74 IIN code 4.90 % 5.50 % 5.00 % 100.00 % 0.27 s 1 core @ 2.5 Ghz (C/C++)
75 CSFlow code 4.71 % 6.46 % 5.00 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
H. Shi, Y. Zhou, K. Yang, X. Yin and K. Wang: CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving. arXiv preprint arXiv:2202.00909 2022.
76 opl 4.69 % 6.61 % 5.01 % 100.00 % 0.27 s 1 core @ 2.5 Ghz (C/C++)
77 SSTM 4.58 % 7.20 % 5.02 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
78 newmfc 4.75 % 6.40 % 5.02 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
79 RAFT-LC 4.67 % 6.88 % 5.04 % 100.00 % 0.24 s 1 core @ 2.5 Ghz (Python)
80 SSTM+[mv] 4.64 % 7.04 % 5.04 % 100.00 % 0.4 s GPU @ 2.5 Ghz (Python)
81 INN code 4.71 % 6.73 % 5.05 % 100.00 % 0.27 s 1 core @ 2.5 Ghz (C/C++)
82 RAFT-RECLoss 4.73 % 6.61 % 5.05 % 100.00 % 0.13 s 1 core @ 2.5 Ghz (Python)
83 FCTR 4.65 % 7.07 % 5.05 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
84 RAFT+AOIR 4.68 % 6.99 % 5.07 % 100.00 % 10 s GPU @ 2.5 Ghz (Python + C/C++)
L. Mehl, C. Beschle, A. Barth and A. Bruhn: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. SSVM 2021.
85 RAFT code 4.74 % 6.87 % 5.10 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
Z. Teed and J. Deng: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow. ECCV 2020.
86 test-dip 4.83 % 6.44 % 5.10 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
87 GMA+LCT-Flow code 4.75 % 6.86 % 5.10 % 100.00 % 0.65 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
88 CGCV code 4.82 % 6.55 % 5.10 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
89 RAFT-Original code 4.76 % 6.85 % 5.11 % 100.00 % 0.45 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
90 RAFT-LC+ 4.76 % 6.97 % 5.13 % 100.00 % 2.4 s 1 core @ 2.5 Ghz (C/C++)
91 test 4.89 % 6.47 % 5.15 % 100.00 % 1 s 1 core @ 2.5 Ghz (Python)
92 GMA-test 4.78 % 7.03 % 5.15 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
93 raft_test 4.78 % 7.03 % 5.15 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
94 SFG
This method uses stereo information.
 4.84 % 6.85 % 5.17 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
95 GMISF
This method uses stereo information.
 4.92 % 6.79 % 5.23 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
96 GMA-base code 4.90 % 7.00 % 5.25 % 100.00 % 0.5 s GPU @ 1.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
97 S_RAFT 5.03 % 6.58 % 5.28 % 100.00 % 2.1 s 1 core @ 2.5 Ghz (Python)
98 RAFT_XT code 5.05 % 6.68 % 5.32 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
99 Scale flow
This method uses stereo information.
 5.24 % 5.71 % 5.32 % 100.00 % 0.8 s GPU @ 2.5 Ghz (Python)
100 PRAFlow_RVC 5.08 % 7.21 % 5.43 % 100.00 % 0.5 s GPU @ NVIDIA RTX 2080Ti (Python)
Z. Wan, Y. Mao and Y. Dai: PRAFlow_RVC: Pyramid Recurrent All- Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020. 2020.
101 ERRN 5.08 % 7.36 % 5.46 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
102 RAFT-TF_RVC 5.32 % 6.75 % 5.56 % 100.00 % 0.7 s GPU @ 2.5 Ghz (Python)
D. Sun, C. Herrmann, V. Jampani, M. Krainin, F. Cole, A. Stone, R. Jonschkowski, R. Zabih, W. Freeman and C. Liu: A TensorFlow implementation of RAFT. 2020.
103 MRRN 5.38 % 6.51 % 5.57 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
104 RAFT-Illumination 5.34 % 7.51 % 5.70 % 100.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
105 ACOSF
This method uses stereo information.
 4.56 % 12.00 % 5.79 % 100.00 % 5 min 1 core @ 3.0 Ghz (Matlab + C/C++)
C. Li, H. Ma and Q. Liao: Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model. International Conference on Pattern Recognition (ICPR) 2020.
106 PPAC-HD3 code 5.78 % 7.48 % 6.06 % 100.00 % 0.19 s NVIDIA GTX 1080 Ti
A. Wannenwetsch and S. Roth: Probabilistic Pixel-Adaptive Refinement Networks. CVPR 2020.
107 GMA-base2 code 5.47 % 9.16 % 6.09 % 100.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
108 LRFlow 5.82 % 7.53 % 6.10 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
109 MaskFlownet code 5.79 % 7.70 % 6.11 % 100.00 % 0.06 s NVIDIA TITAN Xp
S. Zhao, Y. Sheng, Y. Dong, E. Chang and Y. Xu: MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
110 RAFT+LCT-Flow code 5.49 % 9.19 % 6.11 % 100.00 % 0.65 s GPU @ 1.5 Ghz (Python + C/C++)
J. Chen: Motion Estimation with L0 norm Regularization (Extended Version). IEEE 7th International Conference on Virtual Reality(ICVR) 2021.
111 ISF
This method uses stereo information.
 5.40 % 10.29 % 6.22 % 100.00 % 10 min 1 core @ 3 Ghz (C/C++)
A. Behl, O. Jafari, S. Mustikovela, H. Alhaija, C. Rother and A. Geiger: Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. International Conference on Computer Vision (ICCV) 2017.
112 VCN+LCV code 5.75 % 8.80 % 6.25 % 100.00 % 0.26 s 1 core @ 2.5 Ghz (Python)
T. Xiao, J. Yuan, D. Sun, Q. Wang, X. Zhang, K. Xu and M. Yang: Learnable Cost Volume using the Cayley Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
113 RAFT+LCV code 5.73 % 8.90 % 6.26 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Xiao, J. Yuan, D. Sun, Q. Wang, X. Zhang, K. Xu and M. Yang: Learnable Cost Volume using the Cayley Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
114 RAFT-base code 5.69 % 9.26 % 6.28 % 100.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
115 PRichFlow 6.18 % 6.89 % 6.30 % 100.00 % 0.1 s TITAN X MAXWELL
X. Wang, D. Zhu, J. Song, Y. Liu, J. Li and X. Zhang: Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement. .
116 VCN code 5.83 % 8.66 % 6.30 % 100.00 % 0.18 s Titan X Pascal
G. Yang and D. Ramanan: Volumetric Correspondence Networks for Optical Flow. NeurIPS 2019.
117 Stereo expansion
This method uses stereo information.
 code 5.83 % 8.66 % 6.30 % 100.00 % 2 s GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow through Optical Expansion. CVPR 2020.
118 Binary TTC
This method uses stereo information.
 5.84 % 8.67 % 6.31 % 100.00 % 2 s GPU @ 1.0 Ghz (Python)
A. Badki, O. Gallo, J. Kautz and P. Sen: Binary TTC: A Temporal Geofence for Autonomous Navigation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
119 MonoComb
This method uses stereo information.
 5.84 % 8.67 % 6.31 % 100.00 % 0.58 s RTX 2080 Ti
R. Schuster, C. Unger and D. Stricker: MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow. ACM Computer Science in Cars Symposium (CSCS) 2020.
120 HD^3-Flow code 6.05 % 9.02 % 6.55 % 100.00 % 0.10 s NVIDIA Pascal Titan XP
Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition for Match Density Estimation. CVPR 2019.
121 PRSM
This method uses stereo information.
This method makes use of multiple (>2) views.
 code 5.33 % 13.40 % 6.68 % 100.00 % 300 s 1 core @ 2.5 Ghz (C/C++)
C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. ijcv 2015.
122 RAFT-SA code 5.90 % 11.09 % 6.76 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
123 MaskFlownet-S code 6.53 % 8.21 % 6.81 % 100.00 % 0.03 s NVIDIA TITAN Xp
S. Zhao, Y. Sheng, Y. Dong, E. Chang and Y. Xu: MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
124 ScopeFlow code 6.72 % 7.36 % 6.82 % 100.00 % -1 s Nvidia GPU
A. Bar-Haim and L. Wolf: ScopeFlow: Dynamic Scene Scoping for Optical Flow. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
125 SMURF code 6.04 % 10.75 % 6.83 % 100.00 % .2 s 1 core @ 2.5 Ghz (C/C++)
A. Stone, D. Maurer, A. Ayvaci, A. Angelova and R. Jonschkowski: SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
126 RAFT-VM 6.49 % 8.65 % 6.85 % 100.00 % 0.4 s GPU @ 2.5 Ghz (C/C++)
127 OSF+TC
This method uses stereo information.
This method makes use of multiple (>2) views.
 5.76 % 13.31 % 7.02 % 100.00 % 50 min 1 core @ 2.5 Ghz (C/C++)
M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.
128 DPCTF-F 7.22 % 6.47 % 7.09 % 100.00 % 0.07 s GPU @ 2.5 Ghz (C/C++)
Y. Deng, J. Xiao, S. Zhou and J. Feng: Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow. IEEE Transactions on Image Processing 2021.
129 SSF
This method uses stereo information.
 5.63 % 14.71 % 7.14 % 100.00 % 5 min 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Ren, D. Sun, J. Kautz and E. Sudderth: Cascaded Scene Flow Prediction using Semantic Segmentation. International Conference on 3D Vision (3DV) 2017.
130 MFF
This method makes use of multiple (>2) views.
 7.15 % 7.25 % 7.17 % 100.00 % 0.05 s NVIDIA Pascal Titan X (Python)
Z. Ren, O. Gallo, D. Sun, M. Yang, E. Sudderth and J. Kautz: A Fusion Approach for Multi-Frame Optical Flow Estimation. IEEE Winter Conference on Applications of Computer Vision 2019.
131 LiteFlowNet3-S code 7.27 % 6.96 % 7.22 % 100.00 % 0.07s GTX 1080 (slower than Titan X Pascal)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation. European Conference on Computer Vision (ECCV) 2020.
132 PMC-PWC code 7.27 % 6.94 % 7.22 % 100.00 % TBD s GPU @ 2.5 Ghz (Python)
C. Zhang, C. Feng, Z. Chen, W. Hu and M. Li: Parallel multiscale context-based edge- preserving optical flow estimation with occlusion detection. Signal Processing: Image Communication 2022.
133 SwiftFlow 6.85 % 9.11 % 7.23 % 100.00 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Wang, Y. Liu, H. Huang, Y. Pan, W. Yu, J. Jiang, D. Lyu, M. Bocus, M. Liu, I. Pitas and others: ATG-PVD: Ticketing parking violations on a drone. European Conference on Computer Vision 2020.
134 IRR-deconv code 7.28 % 7.30 % 7.29 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
135 IRR-docs code 7.43 % 6.65 % 7.30 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
136 LiteFlowNet3 code 7.26 % 7.75 % 7.34 % 100.00 % 0.07s GTX 1080 (slower than Titan X Pascal)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation. European Conference on Computer Vision (ECCV) 2020.
137 OSF 2018
This method uses stereo information.
 code 5.38 % 17.61 % 7.41 % 100.00 % 390 s 1 core @ 2.5 Ghz (Matlab + C/C++)
M. Menze, C. Heipke and A. Geiger: Object Scene Flow. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 2018.
138 IRR-CS-full code 7.58 % 7.56 % 7.58 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
139 LiteFlowNet2 code 7.62 % 7.64 % 7.62 % 100.00 % 0.0486 s GTX 1080 (slower than Titan X Pascal)
T. Hui, X. Tang and C. Loy: A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization. TPAMI 2020.
140 SENSE
This method uses stereo information.
 code 7.30 % 9.33 % 7.64 % 100.00 % 0.32s GPU, GTX 2080Ti
H. Jiang, D. Sun, V. Jampani, Z. Lv, E. Learned-Miller and J. Kautz: SENSE: A Shared Encoder Network for Scene-Flow Estimation. The IEEE International Conference on Computer Vision (ICCV) 2019.
141 IRR-PWC code 7.68 % 7.52 % 7.65 % 100.00 % 0.18 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019.
142 STaRFlow code 7.51 % 8.35 % 7.65 % 100.00 % 0.24 s GPU @ 2.0 Ghz (Python)
P. Godet, A. Boulch, A. Plyer and G. Besnerais: STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation. ICPR 2020.
143 DTF_SENSE
This method uses stereo information.
This method makes use of multiple (>2) views.
 7.31 % 9.48 % 7.67 % 100.00 % 0.76 s 1 core @ 2.5 Ghz (C/C++)
R. Schuster, C. Unger and D. Stricker: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.
144 IRR-CS0829 code 7.74 % 7.58 % 7.71 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
145 PWC-Net+ code 7.69 % 7.88 % 7.72 % 100.00 % 0.03 s NVIDIA Pascal Titan X
D. Sun, X. Yang, M. Liu and J. Kautz: Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation. arXiv preprint arXiv:1809.05571 2018.
146 OSF
This method uses stereo information.
 code 5.62 % 18.92 % 7.83 % 100.00 % 50 min 1 core @ 2.5 Ghz (C/C++)
M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
147 LRFlow-test 7.79 % 8.04 % 7.83 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
148 TEST 7.81 % 8.20 % 7.87 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
149 Separable-Sim2real 7.30 % 11.01 % 7.92 % 100.00 % 0.25 s GPU
F. Zhang, O. Woodford, V. Prisacariu and P. Torr: Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
150 IRR-CS code 8.45 % 7.39 % 8.27 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
151 LSM_FLOW_RVC code 7.33 % 13.06 % 8.28 % 100.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Tang, L. Yuan and P. Tan: LSM: Learning Subspace Minimization for Low-Level Vision. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
152 AL-OF_r0.2 code 7.25 % 13.53 % 8.30 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Yuan, X. Sun, H. Kim, S. Yu and C. Tomasi: Optical Flow Training Under Limited Label Budget via Active Learning. ECCV 2022.
153 IRR-PWC_RVC code 7.61 % 12.22 % 8.38 % 100.00 % 0.18 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation. CVPR 2019.
154 SemARFlow 7.48 % 12.91 % 8.38 % 100.00 % 0.0168s GPU @ 2.5 Ghz (Python)
155 SelFlow
This method makes use of multiple (>2) views.
 7.61 % 12.48 % 8.42 % 100.00 % 0.09 s GPU @ 2.5 Ghz (Python)
P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical Flow. CVPR 2019.
156 RAFT-MSF-ft
This method uses stereo information.
 8.35 % 11.02 % 8.80 % 100.00 % 0.18 s 1 core @ 2.5 Ghz (Python)
157 MDFlow 8.14 % 12.80 % 8.91 % 100.00 % 0.03 s NVIDIA GTX 1080 Ti
L. Kong and J. Yang: MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation. IEEE Transactions on Circuits and Systems for Video Technology 2022.
158 ULDENet 7.81 % 15.74 % 9.13 % 100.00 % 0.05 s GPU @ >3.5 Ghz (Python)
159 GMFlow code 9.67 % 7.57 % 9.32 % 100.00 % 0.071 s A100 GPU (Python)
H. Xu, J. Zhang, J. Cai, H. Rezatofighi and D. Tao: GMFlow: Learning Optical Flow via Global Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
160 FDFlowNet 9.31 % 9.71 % 9.38 % 100.00 % 0.02 s NVIDIA GTX 1080 Ti
L. Kong and J. Yang: FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network. IEEE International Conference on Image Processing (ICIP) 2020.
161 LiteFlowNet code 9.66 % 7.99 % 9.38 % 100.00 % 0.0885 s GTX 1080 (slower than Titan X Pascal)
T. Hui, X. Tang and C. Loy: LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
162 PWC-Net code 9.66 % 9.31 % 9.60 % 100.00 % 0.03 s NVIDIA Pascal Titan X
D. Sun, X. Yang, M. Liu and J. Kautz: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. CVPR 2018.
163 DCVNet 9.08 % 12.33 % 9.62 % 100.00 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
164 ContinualFlow_ROB
This method makes use of multiple (>2) views.
 8.54 % 17.48 % 10.03 % 100.00 % 0.15 s GPU - NVidia 1080Ti
M. Neoral, J. Šochman and J. Matas: Continual Occlusions and Optical Flow Estimation. 14th Asian Conference on Computer Vision (ACCV) 2018.
165 VCN_RVC code 8.53 % 18.30 % 10.15 % 100.00 % 0.36 s GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Volumetric Correspondence Networks for Optical Flow. NeurIPS 2019.
166 NccFLow 8.81 % 17.36 % 10.24 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
G. Wang, S. Ren and H. Wang: NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry. arXiv preprint arXiv:2107.03610 2021.
167 MirrorFlow code 8.93 % 17.07 % 10.29 % 100.00 % 11 min 4 core @ 2.2 Ghz (C/C++)
J. Hur and S. Roth: MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation. ICCV 2017.
168 CoT-AMFlow 10.02 % 11.95 % 10.34 % 100.00 % 0.06 s GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan and M. Liu: CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation. Conference on Robot Learning (CoRL) 2020.
169 DWARF
This method uses stereo information.
 9.80 % 13.37 % 10.39 % 100.00 % 0.14s - 1.43s TitanXP - JetsonTX2
F. Aleotti, M. Poggi, F. Tosi and S. Mattoccia: Learning end-to-end scene flow by distilling single tasks knowledge. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) 2020.
170 FlowNet2 code 10.75 % 8.75 % 10.41 % 100.00 % 0.1 s GPU @ 2.5 Ghz (C/C++)
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy and T. Brox: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
171 sub_pnp 9.77 % 16.46 % 10.88 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
172 SDF 8.61 % 23.01 % 11.01 % 100.00 % TBA 1 core @ 2.5 Ghz (C/C++)
M. Bai*, W. Luo*, K. Kundu and R. Urtasun: Exploiting Semantic Information and Deep Matching for Optical Flow. ECCV 2016.
173 Flow2Stereo 9.99 % 16.67 % 11.10 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
P. Liu, I. King, M. Lyu and J. Xu: Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching. CVPR 2020.
174 UnFlow code 10.15 % 15.93 % 11.11 % 100.00 % 0.12 s GPU @ 1.5 Ghz (Python + C/C++)
S. Meister, J. Hur and S. Roth: UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss. AAAI 2018.
175 UFlow code 9.78 % 17.87 % 11.13 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
R. Jonschkowski, A. Stone, J. Barron, A. Gordon, K. Konolige and A. Angelova: What Matters in Unsupervised Optical Flow. ECCV 2020.
176 test_ours
This method uses stereo information.
 8.87 % 22.72 % 11.18 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
177 FastFlowNet code 11.20 % 11.30 % 11.22 % 100.00 % 0.01 s NVIDIA GTX 1080 Ti
L. Kong, C. Shen and J. Yang: FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation. 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021.
178 FSF+MS
This method uses stereo information.
This method makes use of the epipolar geometry.
This method makes use of multiple (>2) views.
 8.48 % 25.43 % 11.30 % 100.00 % 2.7 s 4 cores @ 3.5 Ghz (C/C++)
T. Taniai, S. Sinha and Y. Sato: Fast Multi-frame Stereo Scene Flow with Motion Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) 2017.
179 MDFlow-Fast 10.75 % 14.81 % 11.43 % 100.00 % 0.01 s NVIDIA GTX 1080 Ti
L. Kong and J. Yang: MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation. IEEE Transactions on Circuits and Systems for Video Technology 2022.
180 CNNF+PMBP 10.08 % 18.56 % 11.49 % 100.00 % 45 min 1 cores @ 3.5 Ghz (C/C++)
F. Zhang and B. Wah: Fundamental Principles on Learning New Features for Effective Dense Matching. IEEE Transactions on Image Processing 2018.
181 PWC-Net_RVC code 11.22 % 13.69 % 11.63 % 100.00 % 0.03 s NVIDIA Pascal Titan X
D. Sun, X. Yang, M. Liu and J. Kautz: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. CVPR 2018.
182 SFF++
This method uses stereo information.
This method makes use of multiple (>2) views.
 10.63 % 17.48 % 11.77 % 100.00 % 78 s 4 cores @ 3.5 Ghz (C/C++)
R. Schuster, O. Wasenmüller, C. Unger, G. Kuschk and D. Stricker: SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation. International Journal of Computer Vision (IJCV) 2019.
183 SfM-PM
This method makes use of multiple (>2) views.
 9.66 % 22.73 % 11.83 % 100.00 % 69 s 3 cores @ 3.6 Ghz (C/C++)
D. Maurer, N. Marniok, B. Goldluecke and A. Bruhn: Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation. ECCV 2018.
184 Self-SuperFlow-ft
This method uses stereo information.
 10.65 % 19.44 % 12.12 % 100.00 % 0.13 s GTX 1080 Ti
K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. International Conference on Image Processing (ICIP) 2022.
185 MR-Flow
This method makes use of multiple (>2) views.
 code 10.13 % 22.51 % 12.19 % 100.00 % 8 min 1 core @ 2.5 Ghz (Python + C/C++)
J. Wulff, L. Sevilla-Lara and M. Black: Optical Flow in Mostly Rigid Scenes. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2017.
186 DTF_PWOC
This method uses stereo information.
This method makes use of multiple (>2) views.
 10.78 % 19.99 % 12.31 % 100.00 % 0.38 s RTX 2080 Ti
R. Schuster, C. Unger and D. Stricker: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.
187 pnp 11.47 % 19.19 % 12.75 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
188 Mono-SF
This method uses stereo information.
 11.40 % 19.64 % 12.77 % 100.00 % 41 s 1 core @ 3.5 Ghz (Matlab + C/C++)
F. Brickwedde, S. Abraham and R. Mester: Mono-SF: Multi-View Geometry meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes. Proc. of International Conference on Computer Vision (ICCV) 2019.
189 SceneFFields
This method uses stereo information.
 10.58 % 24.41 % 12.88 % 100.00 % 65 s 4 cores @ 3.7 Ghz (C/C++)
R. Schuster, O. Wasenmüller, G. Kuschk, C. Bailer and D. Stricker: SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences. IEEE Winter Conference on Applications of Computer Vision (WACV) 2018.
190 CSF
This method uses stereo information.
 10.40 % 25.78 % 12.96 % 100.00 % 80 s 1 core @ 2.5 Ghz (C/C++)
Z. Lv, C. Beall, P. Alcantarilla, F. Li, Z. Kira and F. Dellaert: A Continuous Optimization Approach for Efficient and Accurate Scene Flow. European Conf. on Computer Vision (ECCV) 2016.
191 PWOC-3D
This method uses stereo information.
 code 12.40 % 15.78 % 12.96 % 100.00 % 0.13 s GTX 1080 Ti
R. Saxena, R. Schuster, O. Wasenmüller and D. Stricker: PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation. Intelligent Vehicles Symposium (IV) 2019.
192 Multi-Mono-SF-ft
This method uses stereo information.
This method makes use of multiple (>2) views.
 code 12.41 % 18.20 % 13.37 % 100.00 % 0.06 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.
193 UnsupSimFlow code 12.60 % 17.27 % 13.38 % 100.00 % 0.03 s 8 cores @ 3.0 Ghz (Python + C/C++)
W. Im, T. Kim and S. Yoon: Unsupervised Learning of Optical Flow with Deep Feature Similarity. The European Conference on Computer Vision (ECCV) 2020.
194 PR-Sceneflow
This method uses stereo information.
 code 11.73 % 24.33 % 13.83 % 100.00 % 150 s 4 core @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.
195 self-raft3d
This method uses stereo information.
 12.38 % 22.48 % 14.06 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
196 DDFlow+LCV 12.98 % 19.83 % 14.12 % 100.00 % 0.1 s GPU @ 2.5 Ghz (Python)
T. Xiao, J. Yuan, D. Sun, Q. Wang, X. Zhang, K. Xu and M. Yang: Learnable Cost Volume using the Cayley Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
197 SelFlow
This method makes use of multiple (>2) views.
 12.68 % 21.74 % 14.19 % 100.00 % 0.09 s GPU @ 2.5 Ghz (Python)
P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical Flow. CVPR 2019.
198 DDFlow 13.08 % 20.40 % 14.29 % 100.00 % 0.06 s GPU @ >3.5 Ghz (Python + C/C++)
P. Liu, I. King, M. Lyu and J. Xu: DDFlow: Learning Optical Flow with Unlabeled Data Distillation. AAAI 2019.
199 DCFlow code 13.10 % 23.70 % 14.86 % 100.00 % 8.6 s GPU @ 3.0 Ghz (Matlab + C/C++)
J. Xu, R. Ranftl and V. Koltun: Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.
200 ProFlow
This method makes use of multiple (>2) views.
 13.86 % 20.91 % 15.04 % 100.00 % 112 s GPU+CPU @ 3.6 Ghz (Python + C/C++)
D. Maurer and A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.
201 FlowFields++ code 14.82 % 17.77 % 15.31 % 100.00 % 29 s 1 core @ 3.5 Ghz (C/C++)
R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. International Conference on Image Processing (ICIP) 2018.
202 ProFlow_ROB
This method makes use of multiple (>2) views.
 14.15 % 21.82 % 15.42 % 100.00 % 112 s GPU+CPU @ 3.6 Ghz (Python + C/C++)
D. Maurer and A. Bruhn: ProFlow: Learning to Predict Optical Flow. BMVC 2018.
203 Self-Mono-SF-ft
This method uses stereo information.
 code 15.51 % 17.96 % 15.91 % 100.00 % 0.09 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.
204 FF++_ROB 15.32 % 19.27 % 15.97 % 100.00 % 29 s 1 core @ 3.5 Ghz (C/C++)
R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. International Conference on Image Processing (ICIP) 2018.
205 SOF code 14.63 % 22.83 % 15.99 % 100.00 % 6 min 1 core @ 2.5 Ghz (Matlab)
L. Sevilla-Lara, D. Sun, V. Jampani and M. Black: Optical Flow with Semantic Segmentation and Localized Layers. CVPR 2016.
206 DIP-Flow-DF
This method makes use of multiple (>2) views.
 14.93 % 23.37 % 16.33 % 100.00 % 104s 2 cores @ 3.6 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
207 JFS
This method makes use of the epipolar geometry.
 15.90 % 19.31 % 16.47 % 100.00 % 13 min 1 core @ 3.2 Ghz (C/C++)
J. Hur and S. Roth: Joint Optical Flow and Temporally Consistent Semantic Segmentation. ECCV Workshops 2016.
208 DF+OIR 15.11 % 23.45 % 16.50 % 100.00 % 3 min 1 core @ 3.5 Ghz (Matlab + C/C++)
D. Maurer, M. Stoll and A. Bruhn: Order-Adaptive and Illumination Aware Variational Optical Flow Refinement. BMVC 2017.
209 SPS+FF++
This method uses stereo information.
 code 15.91 % 20.27 % 16.64 % 100.00 % 36 s 1 core @ 3.5 Ghz (C/C++)
R. Schuster, O. Wasenmüller and D. Stricker: Dense Scene Flow from Stereo Disparity and Optical Flow. ACM Computer Science in Cars Symposium (CSCS) 2018.
210 DIP-Flow-CPM
This method makes use of multiple (>2) views.
 15.57 % 23.84 % 16.95 % 100.00 % 52 s 2 core @ 3.6 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
211 ImpPB+SPCI code 17.25 % 20.44 % 17.78 % 100.00 % 60 s GPU @ 2.5 Ghz (Python)
T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.
212 PCOF-LDOF
This method uses stereo information.
 14.34 % 38.32 % 18.33 % 100.00 % 50 s 1 core @ 3.0 Ghz (C/C++)
M. Derome, A. Plyer, M. Sanfourche and G. Le Besnerais: A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. German Conference on Pattern Recognition 2016.
213 FlowFieldCNN 18.33 % 20.42 % 18.68 % 100.00 % 23 s GPU/CPU 4 core @ 3.5 Ghz (C/C++)
C. Bailer, K. Varanasi and D. Stricker: CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
214 RicFlow 18.73 % 19.09 % 18.79 % 100.00 % 5 s 1 core @ 3.5 Ghz (C/C++)
Y. Hu, Y. Li and R. Song: Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.
215 selfmono
This method uses stereo information.
 17.73 % 26.08 % 19.12 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
216 HCSH 18.05 % 26.23 % 19.41 % 100.00 % 3.5 s 1 core @ 3.0 Ghz (C/C++)
J. Fan, Y. Wang and L. Guo: Hierarchical coherency sensitive hashing and interpolation with RANSAC for large displacement optical flow. Computer Vision and Image Understanding 2018.
217 OmegaNet 17.43 % 29.69 % 19.47 % 100.00 % 0.01 s GPU @ 1.5 Ghz (Python)
F. Tosi, F. Aleotti, P. Ramirez, M. Poggi, S. Salti, L. Di Stefano and S. Mattoccia: Distilled semantics for comprehensive scene understanding from videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2020.
218 UJG code 18.57 % 24.02 % 19.48 % 100.00 % 0.03 s GPU @ 2.5 Ghz (Python)
J. Li, J. Zhao, S. Song and T. Feng: Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video. arXiv preprint arXiv:2105.14520 2021.
219 Multi-Mono-SF
This method uses stereo information.
This method makes use of multiple (>2) views.
 code 18.13 % 26.59 % 19.54 % 100.00 % 0.06 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Multi-Frame Monocular Scene Flow. CVPR 2021.
220 PGM-G 18.90 % 23.43 % 19.66 % 100.00 % 5.05 s 1 core @ 3.1 Ghz (C/C++)
Y. Li: Pyramidal Gradient Matching for Optical Flow Estimation. CoRR 2017.
221 FlowFields+ 19.51 % 21.26 % 19.80 % 100.00 % 28s 1 core @ 3.5 Ghz (C/C++)
C. Bailer, B. Taetz and D. Stricker: Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation. .
222 EPC++ (stereo)
This method uses stereo information.
 19.24 % 26.93 % 20.52 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
C. Luo, Z. Yang, P. Wang, Y. Wang, W. Xu, R. Nevatia and A. Yuille: Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding. IEEE transactions on pattern analysis and machine intelligence 2019.
223 PatchBatch code 19.98 % 26.50 % 21.07 % 100.00 % 50 s GPU @ 2.5 Ghz (Python)
D. Gadot and L. Wolf: PatchBatch: a Batch Augmented Loss for Optical Flow. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.
224 DDF code 20.36 % 25.19 % 21.17 % 100.00 % ~1 min GPU @ 2.5 Ghz (C/C++)
F. G\"uney and A. Geiger: Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.
225 SODA-Flow 20.01 % 29.14 % 21.53 % 100.00 % 96 s 2 cores @ 3.5 Ghz (C/C++)
D. Maurer, M. Stoll, S. Volz, P. Gairing and A. Bruhn: A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow. SSVM 2017.
226 DiscreteFlow code 21.53 % 21.76 % 21.57 % 100.00 % 3 min 1 core @ 2.5 Ghz (Matlab + C/C++)
M. Menze, C. Heipke and A. Geiger: Discrete Optimization for Optical Flow. German Conference on Pattern Recognition (GCPR) 2015.
227 SGM+SF
This method uses stereo information.
 20.91 % 25.50 % 21.67 % 100.00 % 45 min 16 core @ 3.2 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
M. Hornacek, A. Fitzgibbon and C. Rother: SphereFlow: 6 DoF Scene Flow from RGB-D Pairs. CVPR 2014.
228 OAR-Flow 20.62 % 27.67 % 21.79 % 100.00 % 100 s 2 cores @ 3.5 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. SSVM 2017.
229 CPM-Flow code 22.32 % 22.81 % 22.40 % 100.00 % 4.2 s 1 core @ 3.5 Ghz (C/C++)
Y. Hu, R. Song and Y. Li: Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.
230 PCOF + ACTF
This method uses stereo information.
 14.89 % 60.15 % 22.43 % 100.00 % 0.08 s GPU @ 2.0 Ghz (C/C++)
M. Derome, A. Plyer, M. Sanfourche and G. Le Besnerais: A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. German Conference on Pattern Recognition 2016.
231 SegFlow(d0=3) 22.21 % 23.72 % 22.46 % 100.00 % 6.6 s 1 core @ >3.5 Ghz (C/C++)
J. Chen, Z. Cai, J. Lai and X. Xie: Efficient Segmentation-based PatchMatch for Large displacement Optical Flow Estimation. IEEE TCSVT 2018.
232 IntrpNt-df code 22.15 % 26.03 % 22.80 % 100.00 % 3 min GPU @ 2.5 Ghz (Python)
S. Zweig and L. Wolf: InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
233 SGM&FlowFie+
This method uses stereo information.
 22.83 % 22.75 % 22.82 % 81.24 % 29 s 1 core @ 3.5 Ghz (C/C++)
R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: Combining Stereo Disparity and Optical Flow for Basic Scene Flow. Commercial Vehicle Technology Symposium (CVTS) 2018.
234 Back2FutureFlow(UFO)
This method makes use of multiple (>2) views.
 code 22.67 % 24.27 % 22.94 % 100.00 % 0.12 s GPU @ 2.5 Ghz (LUA/Torch)
J. Janai, F. Güney, A. Ranjan, M. Black and A. Geiger: Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. Proc. of the European Conf. on Computer Vision (ECCV) 2018.
235 MotionSLIC
This method makes use of the epipolar geometry.
 code 14.86 % 64.44 % 23.11 % 100.00 % 30 s 4 cores @ 2.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.
236 IntrpNt-cpm code 22.51 % 26.54 % 23.18 % 100.00 % 5.6 s GPU @ 2.5 Ghz (Python)
S. Zweig and L. Wolf: InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
237 FullFlow 23.09 % 24.79 % 23.37 % 100.00 % 4 min 4 cores @ >3.5 Ghz (Matlab and C++)
Q. Chen and V. Koltun: Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.
238 HiLM code 23.73 % 21.79 % 23.41 % 100.00 % 8 sec P6000 (C/C++)
M. Fathy, Q. Tran, M. Zia, P. Vernaza and M. Chandraker: Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences. European Conference on Computer Vision (ECCV) 2018.
239 Self-Mono-SF
This method uses stereo information.
 code 23.26 % 24.93 % 23.54 % 100.00 % 0.09 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.
240 Self-SuperFlow
This method uses stereo information.
 22.70 % 28.55 % 23.67 % 100.00 % 0.13 s GTX 1080 Ti
K. Bendig, R. Schuster and D. Stricker: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. International Conference on Image Processing (ICIP) 2022.
241 IntrpNt-dm code 23.46 % 26.27 % 23.93 % 100.00 % 15 s GPU @ 2.5 Ghz (Python)
S. Zweig and L. Wolf: InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
242 SPM-BP 24.06 % 24.97 % 24.21 % 100.00 % 10 s 2 cores @ 2.5 Ghz (C/C++)
Y. Li, D. Min, M. Brown, M. Do and J. Lu: SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs. Proceedings of the IEEE International Conference on Computer Vision 2015.
243 PPM code 25.87 % 23.67 % 25.50 % 100.00 % 17.3 s 1 core @ 2.5 Ghz (C/Chttps://github.c++)
F. Kuang: PatchMatch algorithms for motion estimation and stereo reconstruction. 2017.
244 3DFlow 25.56 % 29.33 % 26.19 % 100.00 % 448s Matlab with embedded C++ code
J. Chen, Z. Cai, J. Lai and X. Xie: A Filtering Based Framework for Optical Flow Estimation. IEEE TCSVT 2018.
245 EpicFlow code 25.81 % 28.69 % 26.29 % 100.00 % 15 s 1 core @ >3.5 Ghz (C/C++)
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid: EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015 - IEEE Conference on Computer Vision \& Pattern Recognition 2015.
246 SegFlow(d0=11) 28.97 % 22.64 % 27.91 % 100.00 % 4.5 s 1 core @ 3.5 Ghz (C/C++)
J. Chen, Z. Cai, J. Lai and X. Xie: Efficient Segmentation-based PatchMatch for Large displacement Optical Flow Estimation. IEEE TCSVT 2018.
247 DeepFlow code 27.96 % 31.06 % 28.48 % 100.00 % 17 s 1 core @ >3.5 Ghz (Python + C/C++)
P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid: DeepFlow: Large displacement optical flow with deep matching. IEEE Intenational Conference on Computer Vision (ICCV) 2013.
248 CPNFlow 31.05 % 27.16 % 30.40 % 100.00 % 0.1 s GPU @ 1.5 Ghz (Python)
Y. Yang and S. Soatto: Conditional prior networks for optical flow. Proceedings of the European Conference on Computer Vision (ECCV) 2018.
249 IIOF-NLDP 30.23 % 32.44 % 30.60 % 100.00 % 350 s 4 cores @ 3.5 Ghz (Matlab + C/C++)
D. Trinh, W. Blondel and C. Daul: A General Form of Illumination- Invariant Descriptors in Variational Optical Flow Estimation. IEEE Int. Conf. on Image Processing (ICIP) 2017.
250 DMF_ROB code 30.74 % 30.07 % 30.63 % 100.00 % 150 s 1 core @ 2.5 Ghz (C/C++)
P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid: DeepFlow: Large displacement optical flow with deep matching. ICCV - IEEE International Conference on Computer Vision 2013.
251 SPyNet code 33.36 % 43.62 % 35.07 % 100.00 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
A. Ranjan and M. Black: Optical Flow Estimation using a Spatial Pyramid Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
252 SGM+C+NL
This method uses stereo information.
 code 34.24 % 42.46 % 35.61 % 93.83 % 4.5 min 1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them. IJCV 2013.
253 DWBSF
This method uses stereo information.
 40.74 % 31.16 % 39.14 % 100.00 % 7 min 4 cores @ 3.5 Ghz (C/C++)
C. Richardt, H. Kim, L. Valgaerts and C. Theobalt: Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras. 3DV 2016.
254 SGM+LDOF
This method uses stereo information.
 code 40.81 % 31.92 % 39.33 % 95.89 % 86 s 1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
T. Brox and J. Malik: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. PAMI 2011.
255 HS code 39.90 % 51.39 % 41.81 % 100.00 % 2.6 min 1 core @ 3.0 Ghz (Matlab)
D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and The Principles Behind Them. 2014.
256 GCSF
This method uses stereo information.
 code 47.38 % 41.50 % 46.40 % 100.00 % 2.4 s 1 core @ 2.5 Ghz (C/C++)
J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.
257 DB-TV-L1 code 47.52 % 48.27 % 47.64 % 100.00 % 16 s 1 core @ 2.5 Ghz (Matlab)
C. Zach, T. Pock and H. Bischof: A Duality Based Approach for Realtime TV- L1 Optical Flow. DAGM 2007.
258 VSF
This method uses stereo information.
 code 50.06 % 45.40 % 49.28 % 100.00 % 125 min 1 core @ 2.5 Ghz (C/C++)
F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.
259 HAOF code 49.89 % 50.74 % 50.04 % 100.00 % 16.2 s 1 core @ 2.5 Ghz (C/C++)
T. Brox, A. Bruhn, N. Papenberg and J. Weickert: High accuracy optical flow estimation based on a theory for warping. ECCV 2004.
260 TVL1_ROB code 51.15 % 51.12 % 51.14 % 100.00 % 3 s 4 cores @ 2.5 Ghz (C/C++)
J. Sánchez Pérez, E. Meinhardt-Llopis and G. Facciolo: TV-L1 Optical Flow Estimation. Image Processing On Line 2013.
261 PolyExpand 52.00 % 58.56 % 53.09 % 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion. SCIA 2003.
262 H+S_ROB code 68.22 % 76.49 % 69.60 % 100.00 % 8 s 4 cores @ 2.5 Ghz (C/C++)
E. Meinhardt-Llopis, J. Sánchez Pérez and D. Kondermann: Horn-Schunck Optical Flow with a Multi-Scale Strategy. Image Processing On Line 2013.
263 FRLPSSF
This method uses stereo information.
 70.68 % 73.60 % 71.17 % 9.26 % 2.5 s 8 core @ 2.5 Ghz (Matlab)
A. Erfan salehi and R. hoseuni: Real-time Low complexity Precision Sparse Scene-flow. 2022.
264 Pyramid-LK code 71.84 % 76.82 % 72.67 % 100.00 % 1.5 min 1 core @ 2.5 Ghz (Matlab)
J. Bouguet: Pyramidal implementation of the Lucas Kanade feature tracker. Intel 2000.
265 MEDIAN 87.37 % 92.80 % 88.27 % 99.86 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
266 AVERAGE 88.47 % 92.08 % 89.07 % 99.86 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
Table as LaTeX | Only published Methods




Related Datasets

  • HCI/Bosch Robust Vision Challenge: Optical flow and stereo vision challenge on high resolution imagery recorded at a high frame rate under diverse weather conditions (e.g., sunny, cloudy, rainy). The Robert Bosch AG provides a prize for the best performing method.
  • Image Sequence Analysis Test Site (EISATS): Synthetic image sequences with ground truth information provided by UoA and Daimler AG. Some of the images come with 3D range sensor information.
  • Middlebury Stereo Evaluation: The classic stereo evaluation benchmark, featuring four test images in version 2 of the benchmark, with very accurate ground truth from a structured light system. 38 image pairs are provided in total.
  • Daimler Stereo Dataset: Stereo bad weather highway scenes with partial ground truth for freespace
  • Make3D Range Image Data: Images with small-resolution ground truth used to learn and evaluate depth from single monocular images.
  • Lubor Ladicky's Stereo Dataset: Stereo Images with manually labeled ground truth based on polygonal areas.
  • Middlebury Optical Flow Evaluation: The classic optical flow evaluation benchmark, featuring eight test images, with very accurate ground truth from a shape from UV light pattern system. 24 image pairs are provided in total.

Citation

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



eXTReMe Tracker