Exploiting the power of stereo confidences

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

Volumn , Issue , 2013, Pages 297-304

  Pfeiffer, David ^a   Gehrig, Stefan ^a   Schneider, Nicolai ^b  

^a DAIMLER AG   (Germany)

^b IT Designers GmbH   (Germany)

Author keywords

Evaluation; Ground Truth; Image Processing; Machine Vision; MAP Estimation; Segmentation; Stereo; Stereo Confidences; Stereo vision; Stixel World

Indexed keywords

EVALUATION; GROUND TRUTH; MAP ESTIMATION; STEREO; STEREO CONFIDENCES; STIXEL WORLD;

AUTOMOBILE DRIVERS; COMPUTER VISION; IMAGE PROCESSING; IMAGE SEGMENTATION; SENSORS;

STEREO VISION;

EID: 84887352541     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.45     Document Type: Conference Paper

References (26)

1. H. Badino, U. Franke, and R. Mester. Free space computation using stochastic occupancy grids and dynamic pro-gramming. In Workshop on Dynamical Vision, ICCV, Rio de Janeiro, Brazil, October 2007. 1, 2, 7
2. H. Badino, U. Franke, and D. Pfeiffer. The Stixel World-A compact medium level representation of the 3D-world. In DAGM, pages 51-60, Jena, Germany, September 2009. 1, 2
3. R. Benenson, R. Timofte, and L. Van Gool. Stixels estimation without depth map computation. In IEEE CVVT:E2M at ICCV, November 2011. 2
4. G. Egnal, M. Mintz, and R. P. Wildes. A stereo confidence metric using single view imagery with comparison to five alternative approaches. Image Vision Computing, 22(12):943-957, 2004. 2
5. A. E. Elfes and L. Matthies. Sensor integration for robot navigation: Combining sonar and stereo range data in a gridbased representation. In 28th IEEE Conference on Decision and Control, 1987. 1, 2
6. D. Gallup, M. Pollefeys, and J.-M. Frahm. 3d reconstruction using an n-layer heightmap. In DAGM, pages 1-10, Darmstadt, Germany, September 2010. 2
7. S. Gehrig, F. Eberli, and T. Meyer. A real-time low-power stereo vision engine using semi-global matching. In ICVS, Liège, Belgium, October 2009. Springer-Verlag. 3
8. A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE CVPR, pages 3354-3361, Providence, USA, June 2012. 1
9. R. Haeusler and R. Klette. Analysis of KITTI data for stereo analysis with stereo confidence measures. In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages 158-167, Florence, Italy, 2012. 2, 3
10. H. Hirschm?uller. Accurate and efficient stereo processing by semi-global matching and mutual information. In IEEE CVPR, pages 807-814, San Diego, USA, June 2005. 1, 3
11. H. Hirschm?uller and D. Scharstein. Evaluation of cost functions for stereo matching. In IEEE CVPR, Minneapolis, Minnesota, USA, June 2007. IEEE Computer Society. 3
12. X. Hu and P. Mordohai. Evaluation of stereo confidence indoors and outdoors. In IEEE CVPR, pages 1466-1473, San Francisco, CA, USA, June 2010. 1, 2, 3, 3
13. D. Kondermann, S. Abraham, G. J. Brostow, W. F?orstner, S. Gehrig, A. Imiya, B. J?ahne, F. Klose, M. A. Magnor, H. Mayer, R. Mester, T. Pajdla, R. Reulke, and H. Zimmer. On performance analysis of optical flow algorithms. In Theoretical Foundations of Computer Vision, 2012. 4
14. O. Mac Aodha, G. J. Brostow, and M. Pollefeys. Segmenting video into classes of algorithm-suitability. In IEEE CVPR, pages 1054-1061, San Francisco, CA, USA, June 2010. 4
15. A. Milella and R. Siegwart. Stereo-based ego-motion estimation using pixel tracking and iterative closest point. In ICVS, pages 21-30, Bari, Italy, January 2006. 2, 7
16. K. M?uhlmann, D. Maier, J. Hesser, and R. M?anner. Calculating dense disparity maps from color stereo images, an efficient implementation. IJCV, 47(1-3):79-88, 2002. 3
17. D. Pfeiffer and U. Franke. Towards a global optimal multilayer Stixel representation of dense 3D data. In BMVC, Dundee, Scotland, August 2011. 1, 2, 5, 5, 8
18. S. Rusinkiewicz and M. Levoy. Efficient variants of the icp algorithm. In 3D Digital Imaging and Modeling, 2001. 7
19. D. Scharstein and R. Szeliski. Middlebury online stereo evaluation, 2002. http://vision. middlebury. edu/stereo. 1, 4
20. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1-3):7-42, 2002. 1, 3, 4
21. F. Stein. The challenge of putting vision algorithms into a car. In The Eighth IEEE Workshop on Embedded Vision, CVPR, pages 89-94, Providence, RI, USA, June 2012. 5
22. P. Steingrube, S. Gehrig, and U. Franke. Performance evaluation of stereo algorithms for automotive applications. In ICVS, pages 285-294, Liège, Belgium, October 2009. 2
23. S. Thrun. Learning occupancy grid maps with forward sensor models. Autonomous Robots, 15:111-127, 2003. 2, 5
24. A. Wedel, A. Meissner, C. Rabe, U. Franke, and D. Cremers. Detection and segmentation of independently moving objects from dense scene flow. In EMMCVPR, 2009. 3
25. Y. Zhang, A. S. Dhua, S. J. Kiselewich, and W. A. Bauson. Challenges of embedded computer vision in automotive safety systems. In B. Kisa?canin, S. S. Bhattacharyya, and S. Chai, editors, Embedded Computer Vision, Adv. in Pattern Recognition, pages 257-279. Springer London, 2009. 5
26. Z. Zhang. A stereovision system for a planetary rover: Calibration, correlation, registration, and fusion. In Proc. IEEE Workshop on Planetary Rover Technology and Systems, Minneapolis, USA, April 1996. 2