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We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs).
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L. Ying, A. Hertzmann, H. Biermann, and D. Zorin, “Texture and shape synthesis on surfaces,” in Proceedings of the 12th Eurographics Workshop on Rendering Techniques , 2001, pp. 301–312
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X. Gu, S. J. Gortler, and H. Hoppe, “Geometry images,” ACM Transactions on Graphics (TOG) , vol. 21, no. 3, pp. 355–361, 2002
2002
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S. Lefebvre and H. Hoppe, “Perfect spatial hashing,” ACM Trans. Graph. , vol. 25, no. 3, pp. 579–588, Jul. 2006
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K. Chellapilla, S. Puri, and P. Simard, “High performance convolutional neural networks for document processing,” in Tenth International Workshop on Frontiers in Handwriting Recognition . Suvisoft, 2006
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B. Vallet and B. Lévy, “Spectral geometry processing with manifold harmonics,” in Computer Graphics Forum , vol. 27, no. 2. Wiley Online Library, 2008, pp. 251–260
2008
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D. A. Alcantara, A. Sharf, F. Abbasinejad, S. Sengupta, M. Mitzenmacher, J. D. Owens, and N. Amenta, “Real-time parallel hashing on the gpu,” ACM Transactions on Graphics (TOG) , vol. 28, no. 5, p. 154, 2009
2009
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L. Zhu, Y. Chen, and A. Yuille, “Learning a hierarchical deformable template for rapid deformable object parsing,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 6, pp. 1029–1043, 2010
2010
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
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2012
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M. Fisher, D. Ritchie, M. Savva, T. Funkhouser, and P. Hanrahan, “Example-based synthesis of 3d object arrangements,” ACM Transactions on Graphics (TOG) , vol. 31, no. 6, p. 135, 2012
2012
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A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” International Journal of Robotics Research (IJRR) , 2013
2013
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2014
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N. J. Mitra, M. Wand, H. Zhang, D. Cohen-Or, V. Kim, and Q.-X. Huang, “Structure-aware shape processing,” in ACM SIGGRAPH 2014 Courses , ser. SIGGRAPH ’14. New York, NY, USA: ACM, 2014, pp. 13:1–13:21. [Online]. Available: http://doi.acm.org/10.1145/2614028.2615401
2014
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A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: an astounding baseline for recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2014, pp. 806–813
2014
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M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision . Springer, 2014, pp. 818–833
2014
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Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 675–678
2014
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N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting.” Journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
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H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 945–953
2015
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B. Shi, S. Bai, Z. Zhou, and X. Bai, “Deeppano: Deep panoramic representation for 3-d shape recognition,” IEEE Signal Processing Letters , vol. 22, no. 12, pp. 2339–2343, 2015
2015
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 1, pp. 142–158, 2016
2016
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A. Sinha, J. Bai, and K. Ramani, “Deep learning 3d shape surfaces using geometry images,” in European Conference on Computer Vision . Springer, 2016, pp. 223–240
2016
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2016
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W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1874–1883
2016
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J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst, “Geodesic convolutional neural networks on riemannian manifolds,” in Proceedings of the IEEE international conference on computer vision workshops , 2015, pp. 37–45
2015
Cited alongside, same era.
D. Boscaini, J. Masci, S. Melzi, M. M. Bronstein, U. Castellani, and P. Vandergheynst, “Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks,” in Computer Graphics Forum , vol. 34, no. 5. Wiley Online Library, 2015, pp. 13–23
2015
Cited alongside, same era.
D. Maturana and S. Scherer, “Voxnet: A 3d convolutional neural network for real-time object recognition,” in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on . IEEE, 2015, pp. 922–928
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034
2015
Cited alongside, same era.
K. Guo, D. Zou, and X. Chen, “3d mesh labeling via deep convolutional neural networks,” ACM Transactions on Graphics (TOG) , vol. 35, no. 1, p. 3, 2015
2015
Cited alongside, same era.
D. Z. Wang and I. Posner, “Voting for voting in online point cloud object detection.” in Robotics: Science and Systems , 2015
2015
Cited alongside, same era.
H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1520–1528
2015
Cited alongside, same era.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox, “Flownet: Learning optical flow with convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 2758–2766
2015
Cited alongside, same era.
Later among the works it cites.
Y. Li, S. Pirk, H. Su, C. R. Qi, and L. J. Guibas, “Fpnn: Field probing neural networks for 3d data,” in Advances in Neural Information Processing Systems , 2016, pp. 307–315
2016
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2016
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M. Savva, F. Yu, H. Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, H. Su, S. Bai, X. Bai et al. , “Shrec¡¯16 track: large-scale 3d shape retrieval from shapenet core55,” in Proceedings of the Eurographics Workshop on 3D Object Retrieval , 2016
2016
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L. Yi, V. G. Kim, D. Ceylan, I. Shen, M. Yan, H. Su, A. Lu, Q. Huang, A. Sheffer, L. Guibas et al. , “A scalable active framework for region annotation in 3d shape collections,” ACM Transactions on Graphics (TOG) , vol. 35, no. 6, p. 210, 2016
2016
Later among the works it cites.
G. Riegler, A. O. Ulusoy, and A. Geiger, “Octnet: Learning deep 3d representations at high resolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017, pp. 6620–6629
2017
Later among the works it cites.
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong, “O-cnn: Octree-based convolutional neural networks for 3d shape analysis,” ACM Trans. Graph. , vol. 36, no. 4, pp. 72:1–72:11, Jul. 2017
2017
Later among the works it cites.
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine , vol. 34, no. 4, pp. 18–42, 2017
2017
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H. Maron, M. Galun, N. Aigerman, M. Trope, N. Dym, E. Yumer, V. G. KIM, and Y. Lipman, “Convolutional neural networks on surfaces via seamless toric covers.” SIGGRAPH, 2017
2017
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M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner, “Vote3deep: fast object detection in 3d point clouds using efficient convolutional neural networks,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 1355–1361
2017
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2017
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2017
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2017
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P. R. Lorenzo, J. Nalepa, L. S. Ramos, and J. R. Pastor, “Hyper-parameter selection in deep neural networks using parallel particle swarm optimization,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion . ACM, 2017, pp. 1864–1871
2017
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M. D. Zeiler, G. W. Taylor, and R. Fergus, “Adaptive deconvolutional networks for mid and high level feature learning,” in Computer Vision (ICCV), 2011 IEEE International Conference on . IEEE, 2011, pp. 2018–2025
2025
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