This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang and J. Xiao. 3D ShapeNets: A Deep Representation for Volumetric Shapes. CVPR2015.WuZ.SongS.KhoslaA.YuF.ZhangL.TangX. and XiaoJ.. 3D ShapeNets: A Deep Representation for Volumetric Shapes. CVPR2015.Search in Google Scholar
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097–1105.KrizhevskyASutskeverIHintonG E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc.2012:1097–1105.Search in Google Scholar
D. Maturana and S. Scherer. VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. IROS2015.MaturanaD. and SchererS.. VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. IROS2015.10.1109/IROS.2015.7353481Search in Google Scholar
H. Su, S. Maji, E. Kalogerakis, E. Learned-Miller. Multi-view Convolutional Neural Networks for 3D Shape Recognition. ICCV2015.SuH.MajiS.KalogerakisE.Learned-MillerE.. Multi-view Convolutional Neural Networks for 3D Shape Recognition. ICCV2015.10.1109/ICCV.2015.114Search in Google Scholar
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CVPR 2017.QiCharles R.SuHaoMoKaichun, and GuibasLeonidas J.. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CVPR 2017.10.1109/CVPR.2017.16Search in Google Scholar
Z. Xie, K. Xu, W. Shan, L. Liu, Y. Xiong, H. Huang, Projective feature learning for 3D shapes with multiview depth images, in: Computer Graphics Forum, vol. 34, Wiley Online Library, 2015, pp. 1–11.XieZ.XuK.ShanW.LiuL.XiongY.HuangH., Projective feature learning for 3D shapes with multiview depth images, in: Computer Graphics Forum, vol. 34, Wiley Online Library, 2015, pp. 1–11.10.1111/cgf.12740Search in Google Scholar
Z. Zhu, X. Wang, S. Bai, C. Yao, X. Bai, Deep learning representation using autoencoder for 3D shape retrieval, in: Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics(SPAC), IEEE, 2014, pp. 279–284.ZhuZ.WangX.BaiS.YaoC.BaiX., Deep learning representation using autoencoder for 3D shape retrieval, in: Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics(SPAC), IEEE, 2014, pp. 279–284.10.1109/SPAC.2014.6982699Search in Google Scholar
Holz D, Holzer S, Rusu R B, et al. Real-Time Plane Segmentation Using RGB-D Cameras[C]// Robot Soccer World Cup XV. Springer-Verlag, 2012:306–317.HolzDHolzerSRusuR B, Real-Time Plane Segmentation Using RGB-D Cameras[C]// Robot Soccer World Cup XV. Springer-Verlag, 2012:306–317.10.1007/978-3-642-32060-6_26Search in Google Scholar
Salih Y, Malik A S. Comparison of stochastic filtering methods for 3D tracking[J]. Pattern Recognition, 2011, 44(10–11):2711–2737.SalihYMalikA S. Comparison of stochastic filtering methods for 3D tracking[J]. Pattern Recognition, 2011, 44(10–11):2711–2737.10.1016/j.patcog.2011.03.027Search in Google Scholar
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas. Volumetric and multi-view cnns for object classification on 3d data. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2016.QiC. R.SuH.NießnerM.DaiA.YanM., and GuibasL.. Volumetric and multi-view cnns for object classification on 3d data. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2016.Search in Google Scholar
http://www.cnblogs.com/graphics/archive/2010/08/05/1793393.html The Princeton ModelNet. http://modelnet.cs.http://www.cnblogs.com/graphics/archive/2010/08/05/1793393.htmlThe Princeton ModelNet. http://modelnet.cs.Search in Google Scholar
The Princeton ModelNet. http://modelnet.cs.The Princeton ModelNet. http://modelnet.cs.Search in Google Scholar
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014.GirshickR. B.DonahueJ.DarrellT., and MalikJ.. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014.10.1109/CVPR.2014.81Search in Google Scholar
