Displets: Resolving stereo ambiguities using object knowledge

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

Volumn 07-12-June-2015, Issue , 2015, Pages 4165-4175

  Güney, Fatma ^a   Geiger, Andreas ^a  

^a University in Tübingen   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE SEGMENTATION; PATTERN RECOGNITION; SEMANTICS;

NONLOCAL; OBJECT CATEGORIES; OBJECT CLASS; REGULAR STRUCTURE; SEMANTIC SEGMENTATION; STEREO TECHNIQUES; SUPER PIXELS;

STEREO IMAGE PROCESSING;

EID: 84959218293     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299044     Document Type: Conference Paper

References (70)

1. S. Bao, M. Chandraker, Y. Lin, and S. Savarese. Dense object reconstruction with semantic priors. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
2. F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz. PMBP: patchmatch belief propagation for correspondence field estimation. International Journal of Computer Vision (IJCV), 110(1):2-13, 2014
3. M. Bleyer, C. Rhemann, and C. Rother. Extracting 3D sceneconsistent object proposals and depth from stereo images. In Proc. of the European Conf. on Computer Vision (ECCV), 2012
4. M. Bleyer, C. Rother, and P. Kohli. Surface stereo with soft segmentation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010
5. M. Bleyer, C. Rother, P. Kohli, D. Scharstein, and S. Sinha. Object stereo-joint stereo matching and object segmentation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011
6. J. Carreira and C. Sminchisescu. CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 34(7):1312-1328, 2012
7. A. Dame, V. Prisacariu, C. Ren, and I. Reid. Dense reconstruction using 3D object shape priors. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
8. D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in Neural Information Processing Systems (NIPS), 2014
9. S. Fuhrmann, J. Ackermann, T. Kalbe, and M. Goesele. Direct resampling for isotropic surface remeshing. In Vision, Modeling and Visualization (VMV), 2010
10. M. Garland and P. S. Heckbert. Surface simplification using quadric error metrics. In ACM Trans. on Graphics (SIGGRAPH), pages 209-216, 1997
11. S. K. Gehrig, F. Eberli, and T. Meyer. A real-time low-power stereo vision engine using semi-global matching. In Proc. of the International Conf. on Computer Vision Systems (ICVS), 2009
12. A. Geiger, M. Lauer, C. Wojek, C. Stiller, and R. Urtasun. 3D traffic scene understanding from movable platforms. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 36(5):1012-1025, May 2014
13. A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2012
14. A. Geiger, M. Roser, and R. Urtasun. Efficient large-scale stereo matching. In Proc. of the Asian Conf. on Computer Vision (ACCV), 2010
15. C. Haene, C. Zach, A. Cohen, R. Angst, and M. Pollefeys. Joint 3D scene reconstruction and class segmentation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
16. P. Heise, S. Klose, B. Jensen, and A. Knoll. PM-Huber: patchmatch with huber regularization for stereo matching. In Proc. IEEE International Conf. on Computer Vision (ICCV), December 2013
17. S. Hermann and R. Klette. Iterative semi-global matching for robust driver assistance systems. In Proc. of the Asian Conf. on Computer Vision (ACCV), 2013
18. H. Hirschmueller. Stereo processing by semiglobal matching and mutual information. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 30(2):328-341, 2008
19. D. Hoiem, A. A. Efros, and M. Hebert. Recovering surface layout from an image. International Journal of Computer Vision (IJCV), 75(1):151-172, October 2007
20. A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann. Local stereo matching using geodesic support weights. In Proc. IEEE International Conf. on Image Processing (ICIP), 2009
21. B. Jacquet, C. Hane, K. Koser, and M. Pollefeys. Real-world normal map capture for nearly flat reflective surfaces. In Proc. IEEE International Conf. on Computer Vision (ICCV), December 2013
22. B. Julesz. Binocular Depth Perception of Computer-Generated Patterns. Bell System Technical Journal, 39(5):1125-1161, 1960
23. T. Kanade and M. Okutomi. A stereo matching algorithm with an adaptive window: Theory and experiment. In Proc. IEEE International Conf. on Robotics and Automation (ICRA), 1994
24. A. Klaus, M. Sormann, and K. Karner. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In Proc. of the International Conf. on Pattern Recognition (ICPR), 2006
25. V. Kolmogorov. Convergent tree-reweighted message passing for energy minimization. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 28(10):1568-1583, 2006
26. N. Komodakis and N. Paragios. Beyond pairwise energies: Efficient optimization for higher-order MRFs. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009
27. K. Konolige. Small vision system. hardware and implementation. In Proc. of the International Symposium on Robotics Research (ISRR), 1997
28. P. Krähenbühl and V. Koltun. Geodesic object proposals. In Proc. of the European Conf. on Computer Vision (ECCV), 2014
29. A. Kundu, Y. Li, F. Dellaert, F. Li, and J. Rehg. Joint semantic segmentation and 3d reconstruction from monocular video. In Proc. of the European Conf. on Computer Vision (ECCV), 2014
30. G. Kuschk and D. Cremers. Fast and accurate large-scale stereo reconstruction using variational methods. In Proc. IEEE International Conf. on Computer Vision (ICCV) Workshops, 2013
31. L. Ladicky, C. Russell, P. Kohli, and P. H. S. Torr. Associative hierarchical random fields. IEEE Trans. on Pat-tern Analysis and Machine Intelligence (PAMI), 36(6):1056-1077, 2014
32. L. Ladicky, J. Shi, and M. Pollefeys. Pulling things out of perspective. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2014
33. L. Ladicky, P. Sturgess, C. Russell, S. Sengupta, Y. Bastanlar, W. Clocksin, and P. Torr. Joint optimisation for object class segmentation and dense stereo reconstruction. In Proc. of the British Machine Vision Conf. (BMVC), 2010
34. G. Li and S. W. Zucker. Differential geometric inference in surface stereo. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 32(1):72-86, 2010
35. B. Liu, S. Gould, and D. Koller. Single image depth estimation from predicted semantic labels. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010
36. C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. T. Freeman. SIFT flow: Dense correspondence across different scenes. In Proc. of the European Conf. on Computer Vision (ECCV), 2008
37. Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu. Constant time weighted median filtering for stereo matching and beyond. In Proc. IEEE International Conf. on Computer Vision (ICCV), December 2013
38. V. Mansinghka, T. Kulkarni, Y. Perov, and J. Tenenbaum. Approximate Bayesian image interpretation using generative probabilistic graphics programs. NIPS 2013, 2013
39. D. Marr. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Henry Holt and Company, 1983
40. X. Mei, X. Sun,W. Dong, H. Wang, and X. Zhang. Segmenttree based cost aggregation for stereo matching. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
41. X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang. On building an accurate stereo matching system on graphics hardware. In Proc. IEEE International Conf. on Computer Vision (ICCV) Workshops, 2011
42. P. MA rquez-Neila, P. Kohli, C. Rother, and L. Baumela. Non-parametric higher-order random fields for image segmentation. In Proc. of the European Conf. on Computer Vision (ECCV), 2014
43. C. Olsson, J. Ulen, and Y. Boykov. In defense of 3D-label stereo. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
44. J. Pacheco, S. Zuffi, M. J. Black, and E. Sudderth. Preserving modes and messages via diverse particle selection. In Proc. of the International Conf. on Machine learning (ICML), 2014
45. R. Ranftl, T. Pock, and H. Bischof. Minimizing TGV-based variational models with non-convex data terms. In Proc. of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2013
46. C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011
47. C. Rother, P. Kohli, W. Feng, and J. Jia. Minimizing sparse higher order energy functions of discrete variables. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009
48. A. Saxena, J. Schulte, and A. Y. Ng. Depth estimation using monocular and stereo cues. In Proc. of the International Joint Conf. on Artificial Intelligence (IJCAI), 2007
49. A. Saxena, M. Sun, and A. Y. Ng. Make3D: learning 3D scene structure from a single still image. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 31:824-840, 2009
50. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision (IJCV), 47:7-42, 2002
51. M. Schönbein and A. Geiger. Omnidirectional 3d reconstruction in augmented manhattan worlds. In Proc. IEEE International Conf. on Intelligent Robots and Systems (IROS), 2014
52. S. N. Sinha, D. Scharstein, and R. Szeliski. Efficient highresolution stereo matching using local plane sweeps. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014
53. R. Spangenberg, T. Langner, and R. Rojas. Weighted semiglobal matching and center-symmetric census transform for robust driver assistance. In Proc. of the International Conf. on Computer Analysis of Images and Patterns (CAIP), 2013
54. T. Taniai, Y. Matsushita, and T. Naemura. Graph cut based continuous stereo matching using locally shared labels. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014
55. H. Trinh and D. McAllester. Unsupervised learning of stereo vision with monocular cues. In Proc. of the British Machine Vision Conf. (BMVC), 2009
56. J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. International Journal of Computer Vision (IJCV), 104(2):154-171, 2013
57. C. Vogel, S. Roth, and K. Schindler. View-consistent 3D scene flow estimation over multiple frames. In Proc. of the European Conf. on Computer Vision (ECCV), 2014
58. C. Vogel, K. Schindler, and S. Roth. Piecewise rigid scene flow. In Proc. IEEE International Conf. on Computer Vision (ICCV), 2013
59. H. von Helmholtz. Handbuch der physiologischen Optik. L. Voss, 1867
60. L. Wang, H. Jin, and R. Yang. Search space reduction for MRF stereo. In Proc. of the European Conf. on Computer Vision (ECCV), 2008
61. D. Wei, C. Liu, and W. Freeman. A data-driven regularization model for stereo and flow. In Proc. of the International Conference on 3D Vision (3DV), 2014
62. M. Weinmann, A. Osep, R. Ruiters, and R. Klein. Multi-view normal field integration for 3D reconstruction of mirroring objects. In Proc. IEEE International Conf. on Computer Vision (ICCV), 2013
63. O. Woodford, P. Torr, I. Reid, and A. Fitzgibbon. Global stereo reconstruction under second-order smoothness priors. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 31:2115-2128, 2009
64. K. Yamaguchi, T. Hazan, D. McAllester, and R. Urtasun. Continuous markov random fields for robust stereo estimation. In Proc. of the European Conf. on Computer Vision (ECCV), 2012
65. K. Yamaguchi, D. McAllester, and R. Urtasun. Robust monocular epipolar flow estimation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013
66. K. Yamaguchi, D. McAllester, and R. Urtasun. Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In Proc. of the European Conf. on Computer Vision (ECCV), 2014
67. K.-j. Yoon, S. Member, and I. S. Kweon. Adaptive supportweight approach for correspondence search. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 28:650-656, 2006
68. J. Zbontar and Y. LeCun. Computing the stereo matching cost with a convolutional neural network, 2014
69. C. Zhang, Z. Li, R. Cai, H. Chao, and Y. Rui. As-rigidas-possible stereo under second order smoothness priors. In Proc. of the European Conf. on Computer Vision (ECCV), 2014
70. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In Proc. of the European Conf. on Computer Vision (ECCV), 2014.