Matching deformable objects in clutter

Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016

Volumn , Issue , 2016, Pages 1-10

  Cosmo, Luca ^a   Rodola, Emanuele ^b   Masci, Jonathan ^b   Torsello, Andrea ^a   Bronstein, Michael M ^c  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

^b UNIVERSITY OF LUGANO   (Switzerland)

^c TEL AVIV UNIVERSITY   (Israel)

Author keywords

correspondence; deep learning; functional maps; matching; shape analysis; spectral geometry

Indexed keywords

COMBINATORIAL OPTIMIZATION; DEEP LEARNING;

CORRESPONDENCE; CORRESPONDENCE ERRORS; DEFORMABLE OBJECT; DENSE CORRESPONDENCES; MATCHING; QUADRATIC ASSIGNMENT PROBLEMS; SHAPE ANALYSIS; SPECTRAL DOMAINS;

DEFORMATION;

EID: 85011309727     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/3DV.2016.10     Document Type: Conference Paper

References (48)

1. M. Abadi, A. Agarwal, P. Barham, et al. TensorFlow: Largescale machine learning on heterogeneous systems. Software available from tensorflow.org, 2015
2. D. Aiger, N. J. Mitra, and D. Cohen-Or. 4-points congruent sets for robust pairwise surface registration. TOG, 27(3): 85, 2008.
3. L. Alexandre. 3d descriptors for object and category recognition: A comparative evaluation. In Proc. IROS Workshops, pages 1-6, October 2012. 2, 4
4. M. Aubry, U. Schlickewei, and D. Cremers. The wave kernel signature: A quantum mechanical approach to shape analysis. In ICCV Workshops, pages 1626-1633, 2011
5. D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein, and D. Cremers. Anisotropic diffusion descriptors. Computer Graphics Forum, 35(2), 2016
6. J. Bromley et al. Signature verification using a "Siamese" time delay neural network. In Proc. NIPS. 1994. 4
7. A. Bronstein, M. Bronstein, and R. Kimmel. Numerical Geometry of Non-Rigid Shapes. Springer, 2008
8. T. S. Caetano, J. J. McAuley, L. Cheng, Q. V. Le, and A. J. Smola. Learning graph matching. TPAMI, 31(6): 1048-1058, June 2009. 2
9. H. Chui, and A. Rangarajan. A new algorithm for non-rigid point matching. In Proc. CVPR, volume 2, pages 44-51, 2000
10. L. Cosmo, E. Rodolà, M. M. Bronstein, et al. SHREC'16: Partial matching of deformable shapes. In Proc. 3DOR, 2016
11. M. Garland, and P. S. Heckbert. Surface simplification using quadric error metrics. In Proc. SIGGRAPH, pages 209-216, 1997
12. Y. Guo, M. Bennamoun, F. Sohel, M. Lu, and J. Wan. 3d object recognition in cluttered scenes with local surface features: A survey. TPAMI, 36(11): 2270-2287, Nov 2014. 2
13. R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In Proc. CVPR, 2006
14. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv: 1512.03385, 2015
15. B. Jian, and B. C. Vemuri. Robust point set registration using gaussian mixture models. TPAMI, 33(8): 1633-1645, Aug 2011
16. A. Kanezaki, E. Rodolà, D. Cremers, and T. Harada. Learning similarities for rigid and non-rigid object detection. In Proc. 3DV, volume 1, pages 720-727, Dec 2014. 2
17. V. G. Kim, Y. Lipman, and T. A. Funkhouser. Blended intrinsic maps. TOG, 30(4): 79, 2011
18. A. Kovnatsky, M. Bronstein, A. Bronstein, K. Glashoff, and R. Kimmel. Coupled quasi-harmonic bases. Comput. Graph. Forum, 32(2pt4): 439-448, 2013
19. A. Kovnatsky, M. M. Bronstein, X. Bresson, and P. Vandergheynst. Functional correspondence by matrix completion. In Proc. CVPR, 2015
20. B. Kumar, G. Carneiro, and I. Reid. Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions. arXiv preprint arXiv: 1512.09272, 2015
21. W. Lian, L. Zhang, and D. Zhang. Rotation-invariant nonrigid point set matching in cluttered scenes. TIP, 21(5): 2786-2797, May 2012. 2
22. O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, and D. Cremers. Non-rigid puzzles. Computer Graphics Forum, 35(5), 2016
23. R. Litman, and A. M. Bronstein. Learning spectral descriptors for deformable shape correspondence. TPAMI, (99): 1-1, 2014
24. J. Ma, J. Zhao, and A. L. Yuille. Non-rigid point set registration by preserving global and local structures. TIP, 25(1): 53-64, Jan 2016. 2
25. J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst. Geodesic convolutional neural networks on riemannian manifolds. arXiv: 1501.06297, 2015
26. J. Masci, M. M. Bronstein, A. Bronstein, and J. Schmidhuber. Multimodal similarity-preserving hashing. PAMI, 36(4): 824-830, 2014
27. M. Meyer, M. Desbrun, P. Schröder, and A. H. Barr. Discrete differential-geometry operators for triangulated 2-manifolds. Visualization&Mathematics, pages 35-57, 2003
28. D. Mumford, and J. Shah. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure and Applied Math., 42(5): 577-685, 1989
29. A. Myronenko, and X. Song. Point set registration: Coherent point drift. TPAMI, 32(12): 2262-2275, Dec 2010. 2, 7, 8
30. M. Ovsjanikov, M. Ben-Chen, J. Solomon, A. Butscher, and L. Guibas. Functional maps: A flexible representation of maps between shapes. ACM Trans. Graph., 31(4): 30: 1-30: 11, July 2012.
31. J. Pokrass, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, and G. Sapiro. Sparse modeling of intrinsic correspondences. Computer Graphics Forum, 32(2pt4): 459-468, 2013
32. E. Rodolà, A. Albarelli, F. Bergamasco, and A. Torsello. A scale independent selection process for 3d object recognition in cluttered scenes. International Journal of Computer Vision, 102(1-3): 129-145, 2013
33. E. Rodolà, A. Bronstein, A. Albarelli, F. Bergamasco, and A. Torsello. A game-Theoretic approach to deformable shape matching. In Proc. CVPR, pages 182-189, June 2012. 4
34. E. Rodolà, L. Cosmo, M. M. Bronstein, A. Torsello, and D. Cremers. Partial functional correspondence. Computer Graphics Forum, 2016.
35. E. Rodolà, M. Moeller, and D. Cremers. Point-wise map recovery and refinement from functional correspondence. In Proc. VMV, 2015
36. E. Rodolà, A. Torsello, T. Harada, Y. Kuniyoshi, and D. Cremers. Elastic net constraints for shape matching. In Proc. ICCV, pages 1169-1176, December 2013. 6
37. S. Rota Bulò, and I. M. Bomze. Infection and immunization: A new class of evolutionary game dynamics. Games and Economic Behavior, 71(1): 193-211, January 2011. 4, 6
38. R. K. Srivastava, K. Greff, and J. Schmidhuber. Training very deep networks. In C. Cortes, N. D. Lawrence, D. D Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2377-2385. Curran Associates, Inc., 2015
39. J. Sun, M. Ovsjanikov, and L. Guibas. A concise and provably informative multi-scale signature based on heat diffusion. In Proc. SGP, 2009
40. J. Svoboda, J. Masci, and M. M. Bronstein. Palmprint identification via discriminative index learning. In Proc. ICPR, December 2016. 4
41. G. W. Taylor, I. Spiro, C. Bregler, and R. Fergus. Learning invariance through imitation. In Proc. CVPR, pages 2729-2736. IEEE, 2011
42. T. Tieleman, and G. Hinton. Lecture 6.5-RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 2012
43. F. Tombari, S. Salti, and L. Di Stefano. Unique signatures of histograms for local surface description. In Proc. ECCV, pages 356-369, 2010
44. O. Van Kaick, H. Zhang, G. Hamarneh, and D. Cohen-Or. A survey on shape correspondence. Computer Graphics Forum, 30(6): 1681-1707, 2011
45. M. Vestner, R. Litman, A. Bronstein, E. Rodolà, and D. Cremers. Bayesian inference of bijective non-rigid shape correspondence. arXiv: 1607.03425, 2016
46. F. Wang, Q. Huang, M. Ovsjanikov, and L. J. Guibas. Unsupervised multi-class joint image segmentation. In Proc. CVPR, pages 3142-3149, June 2014. 3
47. T. Windheuser, M. Vestner, E. Rodolà, R. Triebel, and D. Cremers. Optimal intrinsic descriptors for non-rigid shape analysis. In Proc. BMVC, 2014
48. A. Zeng, S. Song, M. Niener, M. Fisher, and J. Xiao. 3dmatch: Learning the matching of local 3d geometry in range scans. arXiv preprint arXiv: 1603.08182, 2016