💡 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 | |
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1 | 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. | ||||||||||||||||
2 | 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. | ||||||||||||||||
3 | 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. | ||||||||||||||||
4 | 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. | ||||||||||||||||
5 | 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. | ||||||||||||||||
6 | 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 | ||||||||||||||||
7 | 5.266 | 4.911 | 61.203 | 4.568 | 34.162 | 2.727 | 24.476 | 4.623 | 15.033 | 2.217 | 4.803 | 32.026 | 0.529 | 1.662 | 92.624 | |
Anonymous. | ||||||||||||||||
8 | 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. | ||||||||||||||||
9 | 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. | ||||||||||||||||
10 | 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 | ||||||||||||||||
11 | 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 | |
π‘ 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 | ||||||||||||||||
12 | 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 | |
π‘ 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. | ||||||||||||||||
13 | 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 | |
π‘ 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. | ||||||||||||||||
14 | 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 | |
π‘ 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. | ||||||||||||||||
15 | 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 | |
π‘ 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. | ||||||||||||||||
16 | 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 | |
17 | 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 | |
π‘ 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. | ||||||||||||||||
18 |
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 |
π‘ 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. | ||||||||||||||||
19 | 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. | ||||||||||||||||
20 | 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 | |
π‘ 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. | ||||||||||||||||
21 |
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 |
π‘ 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. | ||||||||||||||||
22 |
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 |
π‘ 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 |
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 |
π‘ 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. | ||||||||||||||||
24 |
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 |
π‘ 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. | ||||||||||||||||
25 |
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 |
π‘ 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. | ||||||||||||||||
26 | 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 | |
π‘ 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. |