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	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.
2	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.
3	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.
4	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.
5	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.
6	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.
7	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.
8	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.
9	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.
10	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.
11	CNNF+PMBP			4.70 %	14.87 %	1.1 px	3.3 px	100.00 %	30 min	1 core @ 3.5 Ghz (C/C++)
Anonymous submission
12	PatchBatch_s			4.81 %	13.64 %	1.1 px	3.0 px	100.00 %	60 s	GPU @ 2.5 Ghz (Python)
Anonymous submission
13	CNN-HPM			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 Loss. 2016.
14	Patchbatch_i			4.92 %	13.84 %	1.2 px	3.3 px	100.00 %	60 s	GPU @ 2.5 Ghz (Python)
Anonymous submission
15	PatchBatch			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.
16	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.
17	PGM-G-noK			5.53 %	15.12 %	1.2 px	3.5 px	100.00 %	5.05 s	1 core @ 3.1 Ghz (C/C++)
Anonymous submission
18	PGM-G			5.59 %	13.87 %	1.2 px	3.3 px	100.00 %	5.05 s	1 core @ 3.1 Ghz (C/C++)
Anonymous submission
19	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.
20	DDF		code	5.73 %	14.18 %	1.4 px	3.4 px	100.00 %	1 min	GPU @ 2.5 Ghz (C/C++)
Anonymous submission
21	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.
22	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.
23	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.
24	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.
25	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.
26	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.
27	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.
28	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.
29	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.
30	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.
31	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.
32	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.
33	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 .
34	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 .
35	faldoi			8.81 %	19.93 %	1.7 px	5.0 px	100.00 %	130 s	1 core @ 1.7 Ghz (Python + C/C++)
Anonymous submission
36	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 .
37	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.
38	NNF-EAC			9.72 %	19.56 %	1.7 px	4.7 px	100.00 %	630 s	1 core @ 2.5 Ghz (Matlab)
39	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.
40	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.
41	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.
42	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.
43	NNF-Local			10.68 %	21.09 %	2.7 px	7.4 px	100.00 %	1073 s	1 core @ 2.5 Ghz (Matlab)
44	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.
45	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.
46	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.
47	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.
48	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.
49	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.
50	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.
51	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++)
Anonymous submission
52	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.
53	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.
54	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.
55	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.
56	DSTF			19.96 %	30.58 %	4.0 px	12.4 px	100.00 %	0.07s	GPU @ 2.5 Ghz (C/C++)
Anonymous submission
57	RLOF(DENSE)			20.13 %	27.89 %	4.7 px	9.9 px	100.00 %	0.5 s	4 cores @ 2.5 Ghz (C/C++)
Anonymous submission
58	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
Anonymous submission
59	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.
60	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.
61	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.
62	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.
63	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.
64	FSDEF			31.39 %	39.96 %	7.2 px	14.7 px	50.57 %	0.26 s	4 cores sandy bridge @ 3.5 Ghz (C/C++)
65	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. .
66	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.
67	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.
68	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.
69	FlowNetS+ft			37.05 %	44.49 %	5.0 px	9.1 px	100.00 %	0.08 s	GPU @ 1.0 Ghz (C/C++)
Anonymous submission
70	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.
71	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.
72	DIS-FAST		code	38.58 %	46.21 %	7.8 px	14.4 px	100.00 %	0.023	1 core @ 4 Ghz (C/C++)
73	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.
74	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.
75	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.
76	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.
77	MEDIAN			79.37 %	82.46 %	16.0 px	24.0 px	99.94 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
78	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}
}