VConv-DAE: Deep volumetric shape learning without object labels

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9915 LNCS, Issue , 2016, Pages 236-250

  Sharma, Abhishek ^a   Grau, Oliver ^b   Fritz, Mario ^c  

^a Intel Visual Computing Institute   (Germany)

^b INTEL CORPORATION   (United States)

^c MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

3D deep learning; Denoising auto encoder; Shape blending; Shape completion

Indexed keywords

BLENDING; COMPUTER AIDED DESIGN; COMPUTER VISION; DEEP LEARNING; SIGNAL ENCODING;

3D DEEP LEARNING; AUTO ENCODERS; DE-NOISING; DENOISING AUTO-ENCODER; LEARN+; SHAPE BLENDING; SHAPE COMPLETIONS; SHAPE LEARNING; VOLUMETRIC REPRESENTATION; VOLUMETRICS;

TOPOLOGY;

EID: 85005943809     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-49409-8_20     Document Type: Conference Paper

References (32)

1. Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (2011)
2. Chaudhuri, S., Kalogerakis, E., Guibas, L., Koltun, V.: Probabilistic reasoning for assembly-based 3D modeling. In: SIGGRAPH (2011)
3. Kalogerakis, E., Chaudhuri, S., Koller, D., Koltun, V.: A Probabilistic model of component-based shape synthesis. In: SIGGRAPH (2012)
4. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D shapenets: a deep representation for volumetric shapes. In: CVPR (2015)
5. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527-1554 (2006)
6. Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10), 95-103 (2011)
7. Lai, K., Bo, L., Fox, D.: Unsupervised feature learning for 3D scene labeling. In: ICRA (2014)
8. Mitra, N.J., Pauly, M., Wand, M., Ceylan, D.: Symmetry in 3D geometry: extraction and applications. In: Computer Graphics Forum, vol. 32, pp. 1-23. Wiley Online Library (2013)
9. Thanh Nguyen, D., Hua, B.S., Tran, K., Pham, Q.H., Yeung, S.K.: A field model for repairing 3D shapes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
10. Maturana, D., Scherer, S.: 3D convolutional neural networks for landing zone detection from lidar. In: ICRA (2015)
11. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML (2008)
12. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. JMLR 11, 3371-3408 (2010)
13. Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional autoencoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52-59. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21735-7_7
14. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818-833. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_53
15. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
16. Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction (2015). arXiv:1604.00449
17. Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.M.: Learning shape correspondence with anisotropic convolutional neural networks (2016). arXiv:1605.06437
18. Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.M., Cremers, D.: Anisotropic diffusion descriptors. In: Computer Graphics Forum (2016)
19. Huang, H., Kalogerakis, E., Yumer, M. E., Mech, R.: Shape synthesis from sketches via procedural models, convolutional networks. In: IEEE Transactions on Visualization and Computer Graphics (2016)
20. Wei, L., Huang, Q., Ceylan, D., Etienne, V., Li, H.: Dense human body correspondences using convolutional networks. In: CVPR (2016)
21. Dosovitskiy, A., Springenberg, J.T., Brox, T.: Learning to generate chairs with convolutional neural networks. In: CVPR (2015)
22. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
23. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15, 1929-1958 (2014)
24. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746-760. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33715-4_54
25. Chen, D.Y., Tian, X.P., Shen, Y.T., Ouhyoung, M.: On visual similarity based 3D model retrieval. In: Computer Graphics Forum, vol. 22, pp. 223-232. Wiley Online Library (2003)
26. Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. In: Symposium on geometry processing, vol. 6, pp. 156-164 (2003)
27. Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of ICCV (2015)
28. Yumer, M.E., Mitra, N.J.: Learning semantic deformation flows with 3D convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 294-311. Springer, Heidelberg (2016). doi:10.1007/978-3-319-46466-4_18
29. Georgoulis, S., Rematas, K., Ritschel, T., Fritz, M., Gool, L.V., Tuytelaars, T.: Deep reflectance maps. In: CVPR (2016)
30. Rematas, K., Ritschel, T., Fritz, M., Gavves, E., Tuytelaars, T.: DeLight-Net: decomposing reflectance maps into specular materials and natural illumination (2016). arXiv:1602.00328
31. Rezende, D.J., Eslami, S.M., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: Unsupervised learning of 3D structure from images (2016). arXiv:1607.00662
32. Nalbach, O., Arabadzhiyska, E., Mehta, D., Seidel, H.P., Ritschel, T.: Deep shading: convolutional neural networks for screen-space shading (2016). arXiv:1603.06078