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L. Mehl, J. Schmalfuss, A. Jahedi, Y. Nalivayko, A. Bruhn — University of Stuttgart

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💡 Please note that methods marked "submitted by spring team" have not been finetuned on Spring.

Name 1px
total 		1px
low-det. 		1px
high-det. 		1px
matched 		1px
unmat. 		1px
rigid 		1px
non-rig. 		1px
not sky 		1px
sky 		1px
s0-10 		1px ▲
s10-40 		1px
s40+ 		EPE Fl WAUC
 
1
DPFlow  
3.442 3.102 56.941 2.859 27.563 1.500 18.132 3.522 2.218 1.188 3.998 20.786 0.340 1.311 94.980
Anonymous.
2
CroCo-Flow   code
4.565 4.209 60.594 3.848 34.200 2.194 22.501 4.479 5.868 1.225 4.332 33.134 0.498 1.508 93.660
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow. Weinzaepfel et al. ICCV 2023.
3
DPFlow (w/o ft)  
4.277 3.935 58.071 3.660 29.796 1.985 21.615 4.099 6.978 1.653 4.376 25.958 0.479 1.602 93.822
Anonymous.
4
StreamFlow  
4.152 3.790 61.297 3.424 34.304 1.986 20.544 3.986 6.678 1.236 4.381 27.935 0.467 1.424 94.404
Anonymous.
5
MemFlow   code
4.482 4.119 61.703 3.742 35.115 2.391 20.306 3.934 12.809 1.305 4.437 31.184 0.471 1.416 93.855
Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
6
StreamFlow-0-shot  
5.215 4.869 59.550 4.559 32.343 2.865 22.987 4.435 17.059 2.597 4.492 29.067 0.606 1.856 93.253
Anonymous.
7
RPKNet (w/o ft)   code
4.809 4.460 59.716 4.171 31.198 2.298 23.802 4.478 9.834 1.665 4.757 31.249 0.657 1.756 92.638
H. Morimitsu, X. Zhu, X. Ji, and X. Yin. "Recurrent Partial Kernel Network for Efficient Optical Flow Estimation". In The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024.
8
SEA-RAFT(M)   code
3.686 3.323 60.986 3.025 31.058 1.561 19.769 3.757 2.616 1.241 4.760 21.237 0.363 1.347 94.534
SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow. Wang et al. ECCV 2024
9
MemFlow(w/o ft)   code
5.759 5.394 63.348 5.107 32.755 3.293 24.422 4.494 24.990 2.918 4.820 32.071 0.627 2.114 92.253
Qiaole Dong, Yanwei Fu. MemFlow: Optical Flow Estimation and Prediction with Memory. CVPR 2024.
10
Win-Win  
5.371 5.003 63.211 4.624 36.274 2.706 25.531 4.965 11.535 1.318 4.854 40.679 0.475 1.621 92.720
Win-Win: Training High-Resolution Vision Transformers from Two Windows. Leroy et al. ICLR 2024.
11
SEA-RAFT(S)   code
3.904 3.536 61.951 3.172 34.228 1.662 20.871 3.974 2.855 1.264 4.871 23.378 0.377 1.389 94.182
SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow. Wang et al. ECCV 2024
12
MS-RAFT+   code
5.724 5.370 61.497 5.041 33.954 3.047 25.973 4.840 19.150 2.055 5.022 38.315 0.643 2.189 92.888
πŸ’‘ submitted by spring team | A. Jahedi, M. Luz, M. Rivinius, L. Mehl, and A. Bruhn. "MS-RAFT+: High Resolution Multi-Scale RAFT " International Journal of Computer Vision (IJCV), 2023
13
RAFT   code
6.790 6.426 64.087 5.999 39.481 4.107 27.088 5.250 30.183 3.134 5.301 41.403 1.476 3.198 90.920
πŸ’‘ submitted by spring team | Z. Teed, and J. Deng. "RAFT: Recurrent All-Pairs Field Transforms for Optical Flow." In European Conference on Computer Vision (ECCV), 2020.
14
GMA   code
7.074 6.699 66.203 6.281 39.892 4.276 28.247 5.614 29.263 3.645 5.389 40.327 0.914 3.079 90.722
πŸ’‘ submitted by spring team | S. Jiang, D. Campbell, Y. Lu, H. Li, and R. Hartley. "Learning to Estimate Hidden Motions with Global Motion Aggregation." In IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
15
FlowFormer   code
6.510 6.144 64.219 5.766 37.294 3.527 29.084 5.500 21.858 3.381 5.530 35.344 0.723 2.384 91.679
πŸ’‘ submitted by spring team | Z. Huang, X. Shi, C. Zhang, Q. Wang, K. C. Cheung, H. Qin, J. Dai, and H. Li. "FlowFormer: A Transformer Architecture for Optical Flow." In European Conference on Computer Vision (ECCV), 2022.
16
FlowNet2   code
6.710 6.346 64.061 5.691 48.892 3.711 29.404 6.039 16.908 1.862 5.816 49.693 1.040 2.823 90.907
πŸ’‘ submitted by spring team | E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox. "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
17
SplatFlow3D (C+T) + LEAStereo (Things); Two-frame [SF]   code
9.203 8.845 65.532 8.301 46.520 6.319 31.025 7.841 29.901 4.411 7.288 54.475 0.707 2.903 88.424
 
18
GMFlow   code
10.355 9.935 76.613 9.060 63.949 6.800 37.258 8.952 31.680 5.412 9.901 52.944 0.945 2.952 82.337
πŸ’‘ submitted by spring team | H. Xu, J. Zhang, J. Cai, H. Rezatofighi, and D. Tao. "GMFlow: Learning Optical Flow via Global Matching." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
19
RAFT-3D (K) [SF]   code
13.962 13.539 80.464 12.963 55.254 8.932 52.013 11.822 46.479 8.895 14.726 54.283 2.528 6.889 81.267
πŸ’‘ submitted by spring team | Z. Teed, and J. Deng. "RAFT-3D: Scene Flow using Rigid-Motion Embeddings." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
20
PWOC-3D [SF]   code
14.461 14.066 76.598 13.263 64.017 10.002 48.200 12.986 36.884 5.212 15.469 89.110 2.813 5.412 81.149
R. Saxena, R. Schuster, O. Wasenmuller, and D. Stricker. "PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation." In IEEE Intelligent Vehicles Symposium (IV), 2019.
21
SPyNet   code
29.963 29.661 77.450 28.783 78.766 26.442 56.601 25.832 92.738 24.803 24.201 88.714 4.162 12.866 67.150
πŸ’‘ submitted by spring team | A. Ranjan, and M. J. Black. "Optical Flow Estimation using a Spatial Pyramid Network." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
22
M-FUSE (F) [SF]   code
20.374 19.993 80.398 19.382 61.415 15.312 58.668 18.381 50.653 9.734 29.588 84.458 2.948 8.791 76.550
πŸ’‘ submitted by spring team | L. Mehl, A. Jahedi, J. Schmalfuss, and A. Bruhn. "M-FUSE: Multi-frame Fusion for Scene Flow Estimation." In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.
23
M-FUSE (K) [SF]   code
20.979 20.600 80.743 19.942 63.882 15.953 59.005 19.500 43.455 10.131 30.966 84.713 2.526 8.480 76.182
πŸ’‘ submitted by spring team | L. Mehl, A. Jahedi, J. Schmalfuss, and A. Bruhn. "M-FUSE: Multi-frame Fusion for Scene Flow Estimation." In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.
24
CamLiFlow (F) [SF]   code
24.012 23.694 74.084 23.112 61.234 21.203 45.265 21.791 57.763 15.394 33.769 69.710 27.774 17.216 74.082
πŸ’‘ submitted by spring team | 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." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
25
RAFT-3D (F) [SF]   code
48.066 47.883 76.933 47.662 64.791 48.200 47.056 48.798 36.942 42.335 68.531 40.645 4.784 34.921 50.686
πŸ’‘ submitted by spring team | Z. Teed, and J. Deng. "RAFT-3D: Scene Flow using Rigid-Motion Embeddings." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
26
CamLiFlow (K) [SF]   code
69.685 69.533 93.651 69.179 90.611 67.381 87.114 67.724 99.492 62.899 79.497 99.903 127.387 60.485 30.635
πŸ’‘ submitted by spring team | 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." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
27
PWCNet   code
82.265 82.268 81.747 82.069 90.400 82.817 78.090 81.575 92.761 81.402 82.189 89.693 2.288 4.889 45.670
πŸ’‘ submitted by spring team | D. Sun, X. Yang, M. Liu, and J. Kautz. "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.