A coarse-to-fine model for 3D pose estimation and sub-category recognition

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

Volumn 07-12-June-2015, Issue , 2015, Pages 418-426

  Mottaghi, Roozbeh ^a   Xiang, Yu ^b,c   Savarese, Silvio ^c  

^a Allen Institute for Artificial Intelligence   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; HIERARCHICAL SYSTEMS; OBJECT DETECTION; OBJECT RECOGNITION;

3D POSE ESTIMATION; CATEGORY RECOGNITION; HIERARCHICAL MODEL; HIERARCHICAL REPRESENTATION; HIGHLY-CORRELATED; INDEPENDENT MODEL; PERFORMANCE LOSS; RESOLVING AMBIGUITIES;

PATTERN RECOGNITION;

EID: 84959235498     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298639     Document Type: Conference Paper

References (38)

1. M. Arie-Nachimson and R. Basri. Constructing implicit 3d shape models for pose estimation. In ICCV, 2009
2. M. Aubry, D. Maturana, A. A. Efros, B. C. Russell, and J. Sivic. Seeing 3d chairs: exemplar part-based 2d-3d align-ment using a large dataset of cad models. In CVPR, 2014
3. T. Berg and P. N. Belhumeur. Poof: Part-based one-vs-one features for fine-grained categorization, face verification, and attribute estimation. In CVPR, 2013
4. C. Desai, D. Ramanan, and C. C. Fowlkes. Discriminative models for multi-class object layout. IJCV, 2011
5. K. Duan, D. Parikh, D. Crandall, and K. Grauman. Dis-covering localized attributes for fine-grained recognition. In CVPR, 2012
6. R. Farrell, O. Oza, N. Zhang, V. I. Morariu, T. Darrell, and L. S. Davis. Birdlets: Subordinate categorization using volu-metric primitives and pose-normalized appearance. In ICCV, 2011
7. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ra-manan. Object detection with discriminatively trained part based models. PAMI, 2010
8. S. Fidler, S. Dickinson, and R. Urtasun. 3d object detec-tion and viewpoint estimation with a deformable 3d cuboid model. In NIPS, 2012
9. S. Fidler and A. Leonardis. Towards scalable representations of object categories: Learning a hierarchy of parts. In CVPR, 2007
10. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich fea-ture hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014
11. B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik. Semantic contours from inverse detectors. In ICCV, 2011
12. V. Hedau, D. Hoiem, and D. Forsyth. Thinking inside the box: Using appearance models and context based on room geometry. In ECCV, 2010
13. M. Hejrati and D. Ramanan. Analyzing 3d objects in clut-tered images. In NIPS, 2012
14. J. Krause, M. Stark, J. Deng, and L. Fei-Fei. 3d object rep-resentations for fine-grained categorization. In 3dRR Work-shop, 2013
15. J. Liebelt and C. Schmid. Multi-view object class detection with a 3d geometric model. In CVPR, 2010
16. J. Lim, H. Pirsiavash, and A. Torralba. Parsing ikea objects: Fine pose estimation. In ICCV, 2013
17. Y.-L. Lin, V. I. Morariu, W. Hsu, and L. S. Davis. Jointly optimizing 3d model fitting and fine-grained classification. In ECCV, 2014
18. O. M. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman. Cats and dogs. In CVPR, 2012
19. N. Payet and S. Todorovic. From contours to 3d object de-tection and pose estimation. In ICCV, 2011
20. J. Peng, T. Hazan, D. McAllester, and R. Urtasun. Convex max-product algorithms for continuous mrfs with applica-tions to protein folding. In ICML, 2011
21. B. Pepik, P. Gehler, M. Stark, and B. Schiele. 3d2pm-3d deformable part models. In ECCV, 2012
22. B. Pepik, M. Stark, P. Gehler, and B. Schiele. Teaching 3d geometry to deformable part models. In CVPR, 2012
23. B. Pepik, M. Stark, P. Gehler, and B. Schiele. Multi-view priors for learning detectors from sparse viewpoint data. In ICLR, 2014
24. R. Salakhutdinov, A. Torralba, and J. Tenenbaum. Learning to share visual appearance for multiclass object detection. In CVPR, 2011
25. S. Savarese and L. Fei-Fei. 3d generic object categorization, localization and pose estimation. In ICCV, 2007
26. J. Sivic, B. C. Russell, A. Zisserman, W. T. Freeman, and A. A. Efros. Unsupervised discovery of visual object class hierarchies. In CVPR, 2008
27. M. Stark, J. Krause, B. Pepik, D. Meger, J. J. Little, B. Schiele, and D. Koller. Fine-grained categorization for 3d scene understanding. In BMVC, 2012
28. H. Su, M. Sun, L. Fei-Fei, and S. Savarese. Learning a dense multiview representation for detection, viewpoint classifica-tion and synthesis. In ICCV, 2009
29. A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, B. Schiele, and L. V. Gool. Towards multi-view object class detection. In CVPR, 2006
30. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Al-tun. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004
31. J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulder. Selective search for object recognition. IJCV, 2013
32. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Mul-tiple kernels for object detection. In ICCV, 2009
33. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001
34. Y. Xiang, R. Mottaghi, and S. Savarese. Beyond pascal: A benchmark for 3d object detection in the wild. In WACV, 2014
35. Y. Xiang and S. Savarese. Estimating the aspect layout of object categories. In CVPR, 2012
36. B. Yao, G. Bradski, and L. Fei-Fei. A codebook-free and annotation-free approach for fine-grained image categoriza-tion. In CVPR, 2012
37. L. Zhu, C. Lin, H. Huang, Y. Chen, and A. Yuille. Unsuper-vised structure learning: Hierarchical recursive composition, suspicious coincidence and competitive exclusion. In ECCV, 2008
38. Z. Zia, M. Stark, B. Schiele, and K. Schindler. Detailed 3d representations for object recognition and modeling. PAMI, 2013.