A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 4040-4048

  Mayer, Nikolaus ^a   Ilg, Eddy ^a   Hausser, Philip ^b   Fischer, Philipp ^a   Cremers, Daniel ^b   Dosovitskiy, Alexey ^a   Brox, Thomas ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; OPTICAL FLOWS; PATTERN RECOGNITION; STEREO IMAGE PROCESSING;

CONVOLUTIONAL NETWORKS; DISPARITY ESTIMATIONS; LARGE DATASET; LARGE NETWORKS; LARGE-SCALE DATASETS; OPTICAL FLOW ESTIMATION; STATE OF THE ART; STEREO VIDEO;

CONVOLUTION;

EID: 84986301062     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.438     Document Type: Conference Paper

References (28)

1. S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black, and R. Szeliski. A database and evaluation methodology for optical flow. Technical Report MSR-TR-2009-179, December 2009.
2. D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, Part IV, LNCS 7577, pages 611-625, Oct. 2012.
3. J. Cech, J. Sanchez-Riera, and R. P. Horaud. Scene flow estimation by growing correspondence seeds. In CVPR, 2011.
4. A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Haz?rbas?, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. FlowNet: Learning optical flow with convolutional networks. In ICCV, 2015.
5. A. Dosovitskiy, J. T. Springenberg, and T. Brox. Learning to generate chairs with convolutional neural networks. In CVPR, 2015.
6. D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a multi-scale deep network. NIPS, 2014.
7. N. Einecke and J. Eggert. A multi-block-matching approach for stereo. In Intelligent Vehicles Symposium, pages 585-592, 2015.
8. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The KITTI dataset. International Journal of Robotics Research (IJRR), 2013.
9. J. Hays and A. A. Efros. im2gps: estimating geographic information from a single image. In CVPR, 2008.
10. H. Hirschmüller. Stereo processing by semiglobal matching and mutual information. PAMI, 30(2):328-341, 2008.
11. F. Huguet and F. Deverney. A variational method for scene flow estimation from stereo sequences. In ICCV, 2007.
12. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.
13. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
14. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1106-1114, 2012.
15. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541-551, 1989.
16. M. Menze and A. Geiger. Object scene flow for autonomous vehicles. In Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
17. J. Quiroga, F. Devernay, and J. Crowley. Scene flow by tracking in intensity and depth data. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 50-57. IEEE, 2012.
18. M. Savva, A. X. Chang, and P. Hanrahan. Semantically-Enriched 3D Models for Common-sense Knowledge. CVPR 2015 Workshop on Functionality, Physics, Intentionality and Causality, 2015.
19. D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Ne?síc, X. Wang, and P. Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In Pattern Recognition, pages 31-42. Springer, 2014.
20. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision, 47(1-3):7-42, 2002.
21. N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012.
22. S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade. Three-dimensional scene flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3):475-480, 2005.
23. S. Vedula, S. Baker, P. Rander, R. T. Collins, and T. Kanade. Three-dimensional scene flow. In ICCV, pages 722-729, 1999.
24. C. Vogel, K. Schindler, and S. Roth. 3d scene flow estimation with a piecewise rigid scene model. International Journal of Computer Vision, 115(1):1-28, 2015.
25. A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke, and D. Cremers. Stereoscopic scene flow computation for 3d motion understanding. International Journal of Computer Vision, 95(1):29-51, 2010.
26. J. Xiao, A. Owens, and A. Torralba. Sun3d: A database of big spaces reconstructed using sfm and object labels. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 1625-1632, Dec 2013.
27. J. ? Zbontar and Y. LeCun. Stereo matching by training a convolutional neural network to compare image patches. arXiv preprint arXiv:1510.05970, 2015.
28. K. Zhang, J. Lu, and G. Lafruit. Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Techn., 19(7):1073-1079, 2009.