The KITTI Vision Benchmark Suite

The stereo / flow benchmark consists of 194 training image pairs and 195 test image pairs, saved in loss less png format. Our evaluation server computes the average number of bad pixels for all non-occluded or occluded (=all groundtruth) pixels. 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.

• Download stereo/optical flow data set (2 GB)
• Download stereo/optical flow calibration files (1 MB)
• Download multi-view extension (20 frames per scene, all cameras) (17 GB)
• Semantic and instance labels for 60 images and car labels for all training images (1 MB)
• Download stereo/optical flow development kit (3 MB)

Our evaluation table ranks all methods according to the number of non-occluded erroneous pixels at the specified disparity / end-point error threshold. 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. For each method we show:

• Out-Noc: Percentage of erroneous pixels in non-occluded areas
• Out-All: Percentage of erroneous pixels in total
• Avg-Noc: Average disparity / end-point error in non-occluded areas
• Avg-All: Average disparity / end-point error in total
• Density: Percentage of pixels for which ground truth has been provided by the method

Note: On 04.11.2013 we have improved the ground truth disparity maps and flow fields leading to slightly improvements for all methods. Please download the stereo/flow dataset with the improved ground truth for training again, if you have downloaded the dataset prior to 04.11.2013. Please consider reporting these new number for all future submissions. Links to last leaderboards before the updates: stereo and flow!

	Method	Setting	Code	Out-Noc	Out-All	Avg-Noc	Avg-All	Density	Runtime	Environment
1	DPCTF-F			1.93 %	5.73 %	0.6 px	1.3 px	100.00 %	0.09 s	GPU @ 2.5 Ghz (Python)
2	PPAC-HD3		code	2.01 %	5.09 %	0.6 px	1.2 px	100.00 %	0.19 s	NVIDIA GTX 1080 Ti
A. Wannenwetsch and S. Roth: Probabilistic Pixel-Adaptive Refinement Networks. CVPR 2020.
3	HD3F+MSDRNet			2.05 %	5.15 %	0.6 px	1.2 px	100.00 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
4	MaskFlownet		code	2.07 %	4.82 %	0.6 px	1.1 px	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.
5	UnDAF-MaskFlownet			2.11 %	5.25 %	0.8 px	1.4 px	100.00 %	0.06 s	GPU @ 2.5 Ghz (Python)
6	VCN+MSDRNet			2.20 %	5.39 %	0.6 px	1.2 px	100.00 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
7	IOFPL-CVr8-ft			2.25 %	5.17 %	0.7 px	1.2 px	100.00 %	-1	GPU @ 2.5 Ghz (Python)
8	HD^3-Flow		code	2.26 %	5.41 %	0.7 px	1.4 px	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.
9	Anonymous 872			2.28 %	5.35 %	0.7 px	1.3 px	100.00 %	0.26 s	GPU @ 2.5 Ghz (Python + C/C++)
10	OFBoost			2.28 %	5.42 %	0.7 px	1.3 px	100.00 %	-1 s	Nvidia GPU
11	IOFPL-ft			2.29 %	5.21 %	0.7 px	1.2 px	100.00 %	-1 s	GPU @ 2.5 Ghz (Python + C/C++)
12	MaskFlownet-S		code	2.29 %	5.24 %	0.6 px	1.1 px	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.
13	CoT-AMFlow			2.31 %	5.70 %	1.2 px	1.7 px	100.00 %	0.06 s	GPU @ 2.5 Ghz (Python)
14	DistillFlow+ft			2.33 %	5.23 %	0.6 px	1.2 px	100.00 %	0.03 s	1 core @ 2.5 Ghz (Python)
15	GCA-Net-ft+			2.35 %	5.81 %	0.6 px	1.3 px	100.00 %	0.03 s	NVIDIA GTX 1080 Ti
16	SGM			2.35 %	5.59 %	0.6 px	1.2 px	100.00 %	0.08 s	1 core @ 2.5 Ghz (Python)
17	MPSD-ft			2.44 %	5.49 %	0.7 px	1.3 px	100.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
18	PRSM		code	2.46 %	4.23 %	0.7 px	1.0 px	100.00 %	300 s	1 core @ 2.5 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. ijcv 2015.
19	LiteFlowNet3-S		code	2.49 %	5.91 %	0.7 px	1.3 px	100.00 %	0.07s	GTX 1080 (slower than Pascal Titan X)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation. European Conference on Computer Vision (ECCV) 2020.
20	LiteFlowNet3		code	2.51 %	5.90 %	0.7 px	1.3 px	100.00 %	0.07s	GTX 1080 (slower than Pascal Titan X)
T. Hui and C. Loy: LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation. European Conference on Computer Vision (ECCV) 2020.
21	RichFlow			2.58 %	5.62 %	0.7 px	1.3 px	100.00 %	0.13s	TITAN X MAXWELL
22	GCA-Net			2.60 %	6.12 %	0.7 px	1.3 px	100.00 %	0.03 s	NVIDIA GTX 1080 Ti
23	PRichFlow			2.60 %	5.63 %	0.7 px	1.3 px	100.00 %	0.1 s	GPU Titan X Maxwell
24	LiteFlowNet2		code	2.63 %	6.16 %	0.7 px	1.4 px	100.00 %	0.0486 s	GTX 1080 (slower than Pascal Titan X)
T. Hui, X. Tang and C. Loy: A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization. TPAMI 2020.
25	SwiftFlow			2.64 %	6.17 %	1.4 px	2.0 px	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 J., M. Liu, I. Pitas and R. Fan: ATG-PVD: Ticketing Parking Violations on A Drone. Proceedings of the European Conference on Computer Vision (ECCV) Workshops 2020.
26	ScopeFlow		code	2.68 %	5.66 %	0.7 px	1.3 px	100.00 %	-1 s	1 core @ 2.5 Ghz (Python)
A. Bar-Haim and L. Wolf: ScopeFlow: Dynamic Scene Scoping for Optical Flow. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
27	VC-SF			2.72 %	4.84 %	0.8 px	1.3 px	100.00 %	300 s	1 core @ 2.5 Ghz (Matlab + C/C++)
C. Vogel, S. Roth and K. Schindler: View-Consistent 3D Scene Flow Estimation over Multiple Frames. Proceedings of European Conference on Computer Vision. Lecture Notes in, Computer Science 2014.
28	PMC-PWC			2.76 %	5.84 %	0.7 px	1.4 px	100.00 %	0.19 s	Nvidia 2080Ti (Python)
29	SPS-StFl			2.82 %	5.61 %	0.8 px	1.3 px	100.00 %	35 s	1 core @ 3.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014.
30	OAS-Net			2.88 %	6.41 %	0.7 px	1.4 px	100.00 %	0.03 s	NVIDIA GTX 1080 Ti
31	FDFlowNet			3.19 %	7.17 %	0.8 px	1.5 px	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.
32	IRR-PWC		code	3.21 %	6.70 %	0.9 px	1.6 px	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.
33	SJTU_PAMI418			3.22 %	6.45 %	0.8 px	1.4 px	100.00 %	0.03 s	1 core @ 2.5 Ghz (Python)
34	GPS-PWC-Occ			3.26 %	6.58 %	0.8 px	1.4 px	100.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
35	LiteFlowNet		code	3.27 %	7.27 %	0.8 px	1.6 px	100.00 %	0.0885 s	GTX 1080 (slower than Pascal Titan X)
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.
36	SelFlow			3.32 %	6.19 %	0.9 px	1.5 px	100.00 %	0.09 s	NVIDIA GPU
P. Liu, M. Lyu, I. King and J. Xu: SelFlow: Self-Supervised Learning of Optical Flow. CVPR 2019.
37	FPCR-Net			3.33 %	6.65 %	0.8 px	1.4 px	100.00 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
38	PWC-Net+		code	3.36 %	6.72 %	0.8 px	1.4 px	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.
39	SPS-Fl			3.38 %	10.06 %	0.9 px	2.9 px	100.00 %	11 s	1 core @ 3.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014.
40	STC-Flow			3.39 %	6.84 %	0.9 px	1.5 px	100.00 %	0.06 s	GPU @ 2.5 Ghz (Python)
41	OSF		code	3.47 %	6.34 %	1.0 px	1.5 px	100.00 %	50 min	1 core @ 3.0 Ghz (Matlab + C/C++)
M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
42	PR-Sf+E			3.57 %	7.07 %	0.9 px	1.6 px	100.00 %	200 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. International Conference on Computer Vision (ICCV) 2013.
43	PCBP-Flow			3.64 %	8.28 %	0.9 px	2.2 px	100.00 %	3 min	4 cores @ 2.5 Ghz (Matlab + C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.
44	MPSD-noft			3.72 %	7.27 %	1.0 px	1.7 px	100.00 %	.1 s	1 core @ 2.5 Ghz (Python + C/C++)
45	PR-Sceneflow			3.76 %	7.39 %	1.2 px	2.8 px	100.00 %	150 sec	4 core @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. International Conference on Computer Vision (ICCV) 2013.
46	FastFlowNet			3.78 %	8.34 %	0.9 px	1.8 px	100.00 %	0.01 s	NVIDIA GTX 1080 Ti
47	SDF			3.80 %	7.69 %	1.0 px	2.3 px	100.00 %	TBA s	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.
48	MotionSLIC			3.91 %	10.56 %	0.9 px	2.7 px	100.00 %	11 s	1 core @ 3.0 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.
49	DistillFlow			3.91 %	7.18 %	0.9 px	1.6 px	100.00 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
50	Flow2Stereo			4.02 %	7.63 %	0.9 px	1.7 px	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.
51	SfM-PM			4.02 %	6.15 %	1.0 px	1.5 px	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.
52	PWC-Net		code	4.22 %	8.10 %	0.9 px	1.7 px	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.
53	UFlow		code	4.26 %	7.91 %	0.9 px	1.9 px	100.00 %	0.02 s	GPU @ 3.0 Ghz (Python)
R. Jonschkowski, A. Stone, J. Barron, A. Gordon, K. Konolige and A. Angelova: What Matters in Unsupervised Optical Flow. ECCV 2020.
54	UnFlow		code	4.28 %	8.42 %	0.9 px	1.7 px	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.
55	SelFlow			4.31 %	7.68 %	1.0 px	2.2 px	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.
56	MirrorFlow		code	4.38 %	8.20 %	1.2 px	2.6 px	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.
57	stflow_r			4.40 %	8.77 %	0.9 px	1.9 px	100.00 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
58	OccInpFlow		code	4.43 %	7.52 %	1.0 px	1.6 px	100.00 %	0.02 s	1 core @ 2.5 Ghz (Python)
59	intpflow			4.43 %	8.81 %	1.0 px	2.0 px	100.00 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
60	ProFlow			4.49 %	7.88 %	1.1 px	2.1 px	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.
61	ARFlow-mv		code	4.56 %	7.53 %	1.0 px	1.5 px	100.00 %	0.02 s	1 core @ 2.5 Ghz (Python)
62	DDFlow			4.57 %	8.86 %	1.1 px	3.0 px	100.00 %	0.06 s	GPU @ >3.5 Ghz (Python)
P. Liu, I. King and M. Xu: DDFlow: Learning Optical Flow with Unlabeled Data Distillation. AAAI 2019.
63	GeoFlow			4.62 %	8.16 %	1.0 px	1.9 px	100.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
64	ImpPB+SPCI		code	4.65 %	13.47 %	1.1 px	2.9 px	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.
65	Flownet2-IA			4.66 %	8.58 %	1.0 px	1.8 px	100.00 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
66	DIP-Flow-CPM			4.69 %	9.63 %	1.0 px	2.4 px	100.00 %	52 s	2 cores @ 3.6 Ghz (C/C++)
D. Maurer, M. Stoll and A. Bruhn: Directional Priors for Multi-Frame Optical Flow. BMVC 2018.
67	CNNF+PMBP			4.70 %	14.87 %	1.1 px	3.3 px	100.00 %	30 min	1 core @ 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.
68	ARFlow		code	4.71 %	8.49 %	1.0 px	1.8 px	100.00 %	0.012 s	GPU @ 2.5 Ghz (Python)
69	Flownet2-IAER			4.74 %	8.70 %	1.0 px	1.8 px	100.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
70	FlowNet2		code	4.82 %	8.80 %	1.0 px	1.8 px	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.
71	FlowFieldCNN			4.89 %	13.01 %	1.2 px	3.0 px	100.00 %	23 s	GPU @ 2.5 Ghz (C/C++)
C. Bailer, K. Varanasi and D. Stricker: CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.
72	IntrpNt-df		code	4.94 %	14.13 %	1.0 px	2.4 px	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.
73	Lavon			4.96 %	9.81 %	1.0 px	1.8 px	100.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
74	RicFlow			4.96 %	13.04 %	1.3 px	3.2 px	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.
75	DIP-Flow-DF			4.97 %	10.02 %	1.1 px	2.6 px	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.
76	FlowFields+			5.06 %	13.14 %	1.2 px	3.0 px	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. .
77	DF+OIR			5.17 %	10.43 %	1.1 px	2.9 px	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.
78	IntrpNt-cpm		code	5.28 %	14.57 %	1.0 px	2.5 px	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.
79	PatchBatch		code	5.29 %	14.17 %	1.3 px	3.3 px	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.
80	PCOF-SGBM			5.40 %	8.73 %	1.2 px	2.1 px	100.00 %	0.8 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.
81	SODA-Flow			5.57 %	10.71 %	1.3 px	2.8 px	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.
82	IntrpNt-ff		code	5.57 %	14.76 %	1.1 px	2.6 px	100.00 %	25 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.
83	PCOF			5.59 %	9.69 %	1.2 px	1.9 px	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.
84	OAR-Flow			5.69 %	10.72 %	1.4 px	2.8 px	100.00 %	90 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.
85	DDF		code	5.73 %	14.18 %	1.4 px	3.4 px	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.
86	PH-Flow			5.76 %	10.57 %	1.3 px	2.9 px	100.00 %	800 s	1 core @ 3.5 Ghz (Matlab + C/C++)
J. Yang and H. Li: Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model. CVPR 2015.
87	FlowFields		code	5.77 %	14.01 %	1.4 px	3.5 px	100.00 %	23 s	4 cores @ 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. International Conference on Computer Vision (ICCV) 2015.
88	CPM-Flow		code	5.79 %	13.70 %	1.3 px	3.2 px	100.00 %	4.2s	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.
89	IntrpNt-dm		code	5.85 %	15.03 %	1.1 px	2.7 px	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.
90	NLTGV-SC			5.93 %	11.96 %	1.6 px	3.8 px	100.00 %	16 s	GPU @ 2.5 Ghz (Matlab + C/C++)
R. Ranftl, K. Bredies and T. Pock: Non-Local Total Generalized Variation for Optical Flow Estimation. Proceedings of the 13th European Conference on Computer Vision 2014.
91	DDS-DF			6.03 %	13.08 %	1.6 px	4.2 px	100.00 %	1 min	1 core @ 2.5 Ghz (Matlab + C/C++)
D. Wei, C. Liu and W. Freeman: A Data-driven Regularization Model for Stereo and Flow. 3DTV-Conference, 2014 International Conference on 2014.
92	TGV2ADCSIFT			6.20 %	15.15 %	1.5 px	4.5 px	100.00 %	12s	GPU @ 2.4 Ghz (C/C++)
J. Braux-Zin, R. Dupont and A. Bartoli: A General Dense Image Matching Framework Combining Direct and Feature-based Costs. International Conference on Computer Vision (ICCV) 2013.
93	DiscreteFlow		code	6.23 %	16.63 %	1.3 px	3.6 px	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.
94	PPM		code	6.23 %	15.91 %	1.4 px	5.0 px	100.00 %	36 s	1 core @ 2.8 Ghz (C/C++)
F. Kuang: PatchMatch algorithms for motion estimation and stereo reconstruction. 2017.
95	BTF-ILLUM			6.52 %	11.03 %	1.5 px	2.8 px	100.00 %	80 seconds	1 core @ 3.0 Ghz (C/C++)
O. Demetz, M. Stoll, S. Volz, J. Weickert and A. Bruhn: Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow. Computer Vision -- ECCV 2014 2014.
96	DeepFlow2		code	6.61 %	17.35 %	1.4 px	5.3 px	100.00 %	22 s	1 core @ >3.5 Ghz (C/C++)
J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid: DeepMatching: Hierarchical Deformable Dense Matching. 2015.
97	Data-Flow		code	7.11 %	14.57 %	1.9 px	5.5 px	100.00 %	3 min	2 cores @ 2.5 Ghz (Matlab + C/C++)
C. Vogel, S. Roth and K. Schindler: An Evaluation of Data Costs for Optical Flow. German Conference on Pattern Recognition (GCPR) 2013.
98	DeepFlow		code	7.22 %	17.79 %	1.5 px	5.8 px	100.00 %	17 s	1 core @ 3.6Ghz (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.
99	EpicFlow		code	7.88 %	17.08 %	1.5 px	3.8 px	100.00 %	15 s	1 core @ 3.6 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.
100	TVL1-HOG			7.91 %	18.90 %	2.0 px	6.1 px	100.00 %	180 s	2 cores @ 3.0 Ghz (Matlab)
H. Rashwan, M. Mohamed, M. Garcia, B. Mertsching and D. Puig: Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients. German Conference on Pattern Recognition 2013 .
101	FastFlow			7.93 %	12.95 %	1.4 px	2.4 px	100.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
102	MLDP-OF			8.67 %	18.78 %	2.4 px	6.7 px	100.00 %	160 s	2 cores @ 2.5 Ghz (Matlab)
M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia and D. Puig: Illumination-Robust Optical Flow Using Local Directional Pattern . IEEE Transactions on Circuits and Systems for Video Technology 2014 .
103	SparseFlow		code	9.09 %	19.32 %	2.6 px	7.6 px	100.00 %	10 s	1 core @ 3.5 Ghz (Matlab + C/C++)
R. Timofte and L. Gool: SparseFlow: Sparse Matching for Small to Large Displacement Optical Flow . WACV 2015 .
104	CRTflow			9.43 %	18.72 %	2.7 px	6.5 px	100.00 %	18 s	GPU @ 1.0 Ghz (C/C++)
O. Demetz, D. Hafner and J. Weickert: The Complete Rank Transform: A Tool for Accurate and Morphologically Invariant Matching of Structure. Proc.~British Machine Vision Conference 2013 (BMVC) 2013.
105	C++		code	10.04 %	20.26 %	2.6 px	7.1 px	100.00 %	8.5 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.
106	TF+OFM		code	10.22 %	18.46 %	2.0 px	5.0 px	100.00 %	350 s	1 cores @ 2.5 Ghz (Matlab + C/C++)
R. Kennedy and C. Taylor: Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames. EMMCVPR 2015.
107	ROF-NND			10.44 %	21.23 %	2.5 px	6.5 px	100.00 %	50 s	4 cores @ 3.5 Ghz (Matlab + C/C++)
S. Ali, C. Daul, E. Galbrun and W. Blondel: Illumination invariant optical flow using neighborhood descriptors . Computer Vision and Image Understanding 2015.
108	C+NL		code	10.49 %	20.64 %	2.8 px	7.2 px	100.00 %	14.8 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.
109	DSPyNet			10.64 %	19.10 %	2.4 px	5.5 px	100.00 %	0.02 s	GPU @ 3.0 Ghz (C/C++)
Z. Sun and H. Wang: Deeper Spatial Pyramid Network with Refined Up-Sampling for Optical Flow Estimation. Proc. Pacific Rim Conference on Multimedia 2018.
110	fSGM			10.74 %	22.66 %	3.2 px	12.2 px	100.00 %	60 s	1 core @ 2.4 Ghz (C/C++)
S. Hermann and R. Klette: Hierarchical Scan Line Dynamic Programming for Optical Flow using Semi-Global Matching. ACCV Workshops 2012.
111	TGV2CENSUS		code	11.03 %	18.37 %	2.9 px	6.6 px	100.00 %	4 s	GPU+CPU @ 3.0 Ghz (Matlab + C/C++)
M. Werlberger: Convex Approaches for High Performance Video Processing. 2012. R. Ranftl, S. Gehrig, T. Pock and H. Bischof: Pushing the Limits of Stereo Using Variational Stereo Estimation. IV 2012.
112	PCA-Layers		code	12.02 %	19.11 %	2.5 px	5.2 px	100.00 %	3.2 s	1 core @ 2.5 Ghz (Python + C/C++)
J. Wulff and M. Black: Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015 2015.
113	AggregFlow			12.23 %	21.79 %	3.1 px	7.4 px	100.00 %	35 min	1 core @ 2.5 Ghz (C/C++)
D. Fortun, P. Bouthemy and C. Kervrann: Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow. Computer Vision and Image Understanding 2016.
114	SPyNet		code	12.31 %	20.97 %	2.0 px	4.1 px	100.00 %	0.16 s	Nvidia TitanX GPU (lua)
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.
115	C+NL-fast		code	12.36 %	22.28 %	3.2 px	7.9 px	100.00 %	2.9 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.
116	EPPM		code	12.75 %	23.55 %	2.5 px	9.2 px	100.00 %	0.25 s	GPU @ 1.0 Ghz (C/C++)
L. Bao, Q. Yang and H. Jin: Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014.
117	CPNFlow			13.01 %	19.17 %	2.0 px	3.6 px	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.
118	HS		code	14.75 %	24.11 %	4.0 px	9.0 px	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.
119	Grts-Flow-V2			15.63 %	26.41 %	3.2 px	8.4 px	100.00 %	0.3 s	1 core @ 1.5 Ghz (C/C++)
E. Zhu, Y. Li and Y. Shi: Fast Optical Flow Estimation Without Parallel Architectures. IEEE Transactions on Circuits and Systems for Video Technology 2016.
120	PCA-Flow		code	15.67 %	24.59 %	2.7 px	6.2 px	100.00 %	0.19 s	1 core @ 2.5 Ghz (Python + C/C++)
J. Wulff and M. Black: Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015 2015.
121	GC-BM-Bino			18.83 %	29.30 %	5.0 px	12.1 px	83.73 %	1.3 s	2 cores @ 2.5 Ghz (C/C++)
B. Kitt and H. Lategahn: Trinocular Optical Flow Estimation for Intelligent Vehicle Applications. ITSC 2012.
122	eFolki			19.31 %	28.79 %	5.2 px	10.9 px	100.00 %	0.026 s	GPU @ 700 Mhz (C/C++)
A. Plyer, G. Le Besnerais and F. Champagnat: Massively parallel Lucas Kanade optical flow for real-time video processing applications. Journal of Real-Time Image Processing 2014.
123	GC-BM-Mono			19.38 %	29.80 %	5.0 px	12.1 px	84.33 %	1.3 s	2 cores @ 2.5 Ghz (C/C++)
B. Kitt and H. Lategahn: Trinocular Optical Flow Estimation for Intelligent Vehicle Applications. ITSC 2012.
124	SVFilterOh			20.38 %	30.38 %	4.3 px	9.1 px	100.00 %	2 s	1 core @ 3 Ghz (C/C++), 1 GTX 780 GPU
M. Helala and F. Qureshi: Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering. Proc. 14th Conference on Computer and Robot Vision (CRV 17) 2017.
125	RSRS-Flow			20.78 %	29.75 %	6.2 px	12.1 px	100.00 %	4 min	1 core @ 2.5 Ghz (Matlab)
P. Ghosh and B. Manjunath: Robust Simultaneous Registration and Segmentation with Sparse Error Reconstruction. PAMI 2012.
126	ALD			21.37 %	30.71 %	10.9 px	16.0 px	100.00 %	110 s	1 core @ 2.5 Ghz (C/C++)
M. Stoll, S. Volz and A. Bruhn: Adaptive Integration of Feature Matches into Variational Optical Flow Methods. ACCV 2012.
127	LDOF		code	21.93 %	31.39 %	5.6 px	12.4 px	100.00 %	1 min	1 core @ 2.5 Ghz (C/C++)
T. Brox and J. Malik: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. PAMI 2011.
128	2Bit-BM-tele		code	24.10 %	33.59 %	7.1 px	15.2 px	100.00 %	6 min	1 core @ 2.4 Ghz (C/C++)
R. Xu and D. Taubman: Robust Dense Block-Based Motion Estimation Using a Two-Bit Transform on a Laplacian Pyramid. 20th Proc. IEEE Int. Conf. Image Proc. 2013 2013.
129	DB-TV-L1		code	30.87 %	39.25 %	7.9 px	14.6 px	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.
130	PyrLK		code	31.48 %	40.08 %	15.6 px	29.6 px	92.33 %	1.3 s	4 cores @ 3.5 Ghz (C/C++)
J. Bouguet: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. .
131	GCSF			33.17 %	41.71 %	7.0 px	15.3 px	48.27 %	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.
132	UnsupFlownet			34.85 %	43.15 %	4.6 px	11.3 px	100.00 %	0.03 s	GPU @ 3.0 Ghz (C/C++)
J. Yu, A. Harley and K. Derpanis: Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness. ECCV 2016.
133	HAOF		code	35.87 %	43.46 %	11.1 px	18.3 px	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.
134	FSDEF			36.85 %	44.65 %	8.8 px	16.4 px	41.81 %	0.26s	4 cores sandy bridge @ 3.5 Ghz (C/C++)
M. Garrigues and A. Manzanera: Fast Semi Dense Epipolar Flow Estimation. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
135	FlowNetS+ft		code	37.05 %	44.49 %	5.0 px	9.1 px	100.00 %	0.08 s	GPU @ 1.0 Ghz (C/C++)
A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. Smagt, D. Cremers and T. Brox: FlowNet: Learning Optical Flow with Convolutional Networks. ICCV 2015.
136	RLOF(IM-GM)			37.49 %	44.78 %	8.2 px	15.4 px	11.84 %	3.7 s	4 core @ 3.4 Ghz (C/C++)
T. Senst, J. Geistert and T. Sikora: Robust local optical flow: Long-range motions and varying illuminations. 2016 IEEE International Conference on Image Processing (ICIP) 2016.
137	BERLOF		code	37.66 %	45.27 %	8.5 px	16.2 px	15.26 %	0.231 s	GPU @ 700 Mhz (C/C++) GeForce GTX 680
T. Senst, J. Geistert, I. Keller and T. Sikora: Robust Local Optical Flow Estimation using Bilinear Equations for Sparse Motion Estimation. 20th IEEE International Conference on Image Processing 2013.
138	DIS-FAST		code	38.58 %	46.21 %	7.8 px	14.4 px	100.00 %	0.023	1 core @ 4 Ghz (C/C++)
T. Kroeger, R. Timofte, D. Dai and L. Van Gool: Fast Optical Flow using Dense Inverse Search. ECCV 2016.
139	RLOF		code	38.60 %	46.13 %	8.7 px	16.5 px	14.76 %	0.488 s	GPU @ 700 Mhz (C/C++) GeForce GTX 680
T. Senst, V. Eiselein and T. Sikora: Robust Local Optical Flow for Feature Tracking. TCSVT 2012.
140	Next-Flow		code	39.12 %	46.37 %	5.1 px	9.2 px	100.00 %	0.1 s	GPU @ 1.0 Ghz (C/C++)
N. Sedaghat, M. Zolfaghari and T. Brox: Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild. 2017.
141	PolyExpand			47.59 %	54.00 %	17.3 px	25.3 px	100.00 %	1 s	1 core @ 2.5 Ghz (C/C++)
G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion. SCIA 2003.
142	OCV-BM		code	63.50 %	68.19 %	24.4 px	33.3 px	100.00 %	1.5 min	1 core @ 2.5 Ghz (C/C++)
G. Bradski: The OpenCV Library. Dr. Dobb's Journal of Software Tools 2000.
143	Pyramid-LK		code	65.81 %	70.16 %	21.8 px	33.2 px	99.90 %	1.5 min	1 core @ 2.5 Ghz (Matlab)
J. Bouguet: Pyramidal implementation of the Lucas Kanade feature tracker. Intel 2000.
144	MEDIAN			79.37 %	82.46 %	16.0 px	24.0 px	99.94 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
145	AVERAGE			81.27 %	84.06 %	16.3 px	24.7 px	99.94 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

• 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 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.

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}