Joint segmentation of images and scanned point cloud in large-scale street scenes with low-annotation cost

IEEE Transactions on Image Processing

Volumn 23, Issue 11, 2014, Pages 4763-4772

  Zhang, Honghui ^a   Wang, Jinglu ^a   Fang, Tian ^a   Quan, Long ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

image; point cloud; Segmentation; street scene

Indexed keywords

IMAGE; JOINT SEGMENTATION; POINT CLOUD;

IMAGE SEGMENTATION;

EID: 84907486604     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2348795     Document Type: Article

References (28)

1. G. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, "Segmentation and recognition using structure from motion point clouds," in Proc. Eur. Conf. Comput. Vis., 2008, pp. 44-57.
2. J. Xiao and L. Quan, "Multiple view semantic segmentation for street view images," in Proc. 12th IEEE Int. Conf. Comput. Vis., Sep./Oct. 2009, pp. 686-693.
3. C. Zhang, L. Wang, and R. Yang, "Semantic segmentation of urban scenes using dense depth maps," in Proc. Eur. Conf. Comput. Vis., 2010, pp. 708-721.
4. Q. Huang, M. Han, B. Wu, and S. Ioffe, "A hierarchical conditional random field model for labeling and segmenting images of street scenes," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 1953-1960.
5. G. Floros and B. Leibe, "Joint 2D-3D temporally consistent semantic segmentation of street scenes," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 2823-2830.
6. D. Anguelov et al., "Discriminative learning of Markov random fields for segmentation of 3D scan data," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 169-176.
7. A. Golovinskiy, V. G. Kim, and T. Funkhouser, "Shape-based recognition of 3D point clouds in urban environments," in Proc. 12th Int. Conf. Comput. Vis., Sep./Oct. 2009, pp. 2154-2161.
8. B. Douillard, D. Fox, and F. Ramos, "Laser and vision based outdoor object mapping," in Proc. Robot. Sci. Syst., 2008, pp. 9-16.
9. I. Posner, M. Cummins, and P. Newman, "Fast probabilistic labeling of city maps," in Proc. Robot. Sci. Syst., 2008, pp. 17-24.
10. D. Munoz, N. Vandapel, and M. Hebert, "Onboard contextual classification of 3D point clouds with learned high-order Markov random fields," in Proc. IEEE Int. Conf. Robot. Autom., May 2009, pp. 2009-2016.
11. D. Anguelov et al., "Google street view: Capturing the world at street level," Computer, vol. 43, no. 6, pp. 32-38, Jun. 2010.
12. S. Bileschi, "Streetscenes: Towards scene understanding in still images," Ph.D. dissertation, Dept. Elect. Eng. Comput. Sci., Massachusetts Institute of Technology, Cambridge, MA, USA, 2007.
13. H. Zhang, J. Xiao, and L. Quan, "Supervised label transfer for semantic segmentation of street scenes," in Proc. Eur. Conf. Comput. Vis., 2010, pp. 561-574.
14. S. Sengupta, E. Greveson, A. Shahrokni, and P. H. S. Torr, "Urban 3D semantic modelling using stereo vision," in Proc. IEEE Int. Conf. Robot. Autom., May 2013, pp. 580-585.
15. M. Lhuillier and L. Quan, "Quasi-dense reconstruction from image sequence," in Proc. Eur. Conf. Comput. Vis., 2002, pp. 125-139.
16. C. Haene, C. Zach, A. Cohen, R. Angst, and M. Pollefeys, "Joint 3D scene reconstruction and class segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 97-104.
17. G. Medioni, M. S. Lee, and C. K. Tang, A Computational Framework for Feature Extraction and Segmentation. Amsterdam, The Netherlands: Elsevier, 2000.
18. J.-F. Lalonde, N. Vandapel, D. Huber, and M. Hebert, "Natural terrain classification using three-dimensional ladar data for ground robot mobility," J. Field Robot., vol. 23, no. 10, pp. 839-861, 2006.
19. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "Slic superpixels," EPFL, Lausanne, Switzerland, EPFL Tech. Rep. 149300, 2010.
20. J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context," in Proc. Eur. Conf. Comput. Vis., 2006, pp. 2-23.
21. L. Ladický, C. Russell, P. Kohli, and P. H. S. Torr, "Associative hierarchical CRFs for object class image segmentation," in Proc. 12th IEEE Int. Conf. Comput. Vis., Sep./Oct. 2009, pp. 739-746.
22. C. E. Brodley and M. A. Friedl, "Identifying mislabeled training data," J. Artif. Intell. Res., vol. 11, pp. 131-167, Aug. 1999.
23. D. Nister and H. Stewenius, "Scalable recognition with a vocabulary tree," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2006, pp. 2161-2168.
24. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
25. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The Pascal visual object classes (VOC) challenge," Int. J. Comput. Vis., vol. 88, no. 2, pp. 303-338, 2010.
26. J. Shotton, M. Johnson, and R. Cipolla, "Semantic texton forests for image categorization and segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-8.
27. H. Zhang, T. Fang, X. Chen, Q. Zhao, and L. Quan, "Partial similarity based nonparametric scene parsing in certain environment," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 2241-2248.
28. D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert, "Contextual classification with functional max-margin Markov networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 975-982.