Dataset


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.

Evaluation

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: Our main ranking is computed at 3 pixels error threshold, evaluating all pixels. For methods which do not provide dense result we use background interpolation to fill in missing values. When publishing, please make sure that you provide this 'default' table in your publication.

Error threshold        Evaluation area

Optical Flow Evaluation

This table ranks general optical flow methods, performing a full 2D search, as compared to the motion stereo methods below.

Rank Method Setting Out-Noc Out-All Avg-Noc Avg-All Density Runtime Environment
1 PR-Sf+E 4.08 % 7.79 % 0.9 px 1.7 px 100.00 % 200 s 4 cores @ 3.0 Ghz (Matlab + C/C++)
Anonymous submission
2 PCBP-Flow ms 4.08 % 8.70 % 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.
3 MotionSLIC ms 4.36 % 10.91 % 1.0 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.
4 PR-Sceneflow 4.48 % 8.98 % 1.3 px 3.3 px 100.00 % 150 sec 4 core @ 3.0 Ghz (Matlab + C/C++)
Anonymous submission
5 TGV2ADCSIFT 6.55 % 15.35 % 1.6 px 4.5 px 100.00 % 12s GPU @ 2.4 Ghz (C/C++)
Anonymous submission
6 Data-Flow 8.22 % 15.78 % 2.3 px 5.7 px 100.00 % 3 min 2 cores @ 2.5 Ghz (Matlab + C/C++)
Anonymous submission
7 TVL1-HOG 8.31 % 19.21 % 2.0 px 6.1 px 100.00 % 180 s 2 cores @ 3.0 Ghz (Matlab)
Anonymous submission
8 MLDP-OF 8.91 % 18.95 % 2.5 px 6.7 px 100.00 % 160 s 2 cores @ 2.5 Ghz (Matlab)
Anonymous submission
9 CRTflow 9.71 % 18.88 % 2.7 px 6.5 px 100.00 % 18 s GPU @ 1.0 Ghz (C/C++)
Anonymous submission
10 C++ 10.16 % 20.29 % 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. 2013.
11 C+NL 10.60 % 20.66 % 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. 2013.
12 fSGM 11.03 % 22.90 % 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.
13 TGV2CENSUS 11.14 % 18.42 % 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.
14 C+NL-fast 12.42 % 22.27 % 3.2 px 7.8 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. 2013.
15 HS 14.77 % 24.08 % 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. 2013.
16 IQFlow 18.93 % 28.33 % 3.6 px 8.8 px 100.00 % 60 s 4 cores @ 3.5 Ghz (C/C++)
Anonymous submission
17 GC-BM-Bino ms 18.93 % 29.37 % 5.0 px 12.0 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.
18 C+NL-M 19.17 % 26.35 % 7.4 px 14.5 px 100.00 % 5 min 2 cores @ 2.5 Ghz (Matlab)
Anonymous submission
19 eFolki 19.34 % 28.79 % 5.2 px 10.8 px 100.00 % 0.026 s GPU @ 700 Mhz (C/C++)
Anonymous submission
20 GC-BM-Mono ms 19.49 % 29.88 % 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.
21 RSRS-Flow 20.74 % 29.68 % 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.
22 ALD 21.35 % 30.65 % 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.
23 LDOF 21.86 % 31.31 % 5.5 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.
24 HMM 24.76 % 34.16 % 7.2 px 15.0 px 100.00 % 10 min 1 core @ 2.5 Ghz (C/C++)
Anonymous submission
25 DB-TV-L1 30.75 % 39.13 % 7.8 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.
26 GCSF 33.23 % 41.74 % 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.
27 HAOF 35.76 % 43.36 % 11.1 px 18.2 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.
28 BERLOF 37.59 % 45.20 % 8.5 px 16.2 px 15.26 % 0.231 s GPU @ 700 Mhz (C/C++) GeForce GTX 680
Anonymous submission
29 RLOF 38.51 % 46.04 % 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.
30 PolyExpand 47.54 % 53.95 % 17.2 px 25.2 px 100.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion. SCIA 2003.
31 OCV-BM 63.46 % 68.16 % 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.
32 Pyramid-LK 65.74 % 70.09 % 21.7 px 33.1 px 99.90 % 1.5 min 1 core @ 2.5 Ghz (Matlab)
J. Bouguet: Pyramidal implementation of the Lucas Kanade feature tracker. Intel 2000.
This table as LaTeX

The settings column describes additional assumptions made / information used by the methods:

  • ms = motion stereo: Usage of the epipolar geometry to restrict the search problem to 1D

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 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.



eXTReMe Tracker