Progressive conditional GAN-based augmentation for 3D object recognition

Neurocomputing - Tập 460 - Trang 20-30 - 2021
A.A.M. Muzahid1,2, Wan Wanggen1,2, Ferdous Sohel3, Mohammed Bennamoun4, Li Hou5, Hidayat Ullah1,2
1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
2Institute of Smart City, Shanghai University, Shanghai 200444, China
3Information Technology, Centre for Crop and Food Innovation, Murdoch University, WA 6150 Murdoch, Australia
4Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
5School of Information Engineering, Huangshan University, Huangshan, 245041, China

Tài liệu tham khảo

Goodfellow, 2014, Generative adversarial networks, Adv. Neural Inf. Process. Syst., 2672 Yi, 2017, Unsupervised dual learning for image-to-image translation, 2868 Wu, 2016, Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling, Adv. Neural Inf. Process. Syst., 82 Xie, 2018, Learning descriptor networks for 3D shape synthesis and analysis, 8629 Hong, 2019, How generative adversarial networks and their variants work: an overview, ACM Comput. Surv., 52, 1 Xu, 2020, 1.2 Watt classification of 3D Voxel Based Point-clouds using a CNN on a Neural Compute Stick, Neurocomputing, 393, 165, 10.1016/j.neucom.2018.10.114 Shah, 2016, A novel feature representation for automatic 3D object recognition in cluttered scenes, Neurocomputing, 205, 1, 10.1016/j.neucom.2015.11.019 Sharma A, Grau O, Fritz M. VConv-DAE: Deep Volumetric Shape Learning Without Object Labels. In: ECCV 2016 Workshops Lecture Notes in Computer Science 2016;9915:236–50. Kingma, 2014, Semi-supervised learning with deep generative models, 3581 Frid-Adar, 2018, GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification, Neurocomputing, 321, 321, 10.1016/j.neucom.2018.09.013 Rezaei, 2019, Deep learning-based 3D local feature descriptor from Mercator projections, Comput. Aided Geom. Des., 74, 101771, 10.1016/j.cagd.2019.101771 Muzahid, 2020, 3D object classification using a volumetric deep neural network: an efficient octree guided auxiliary learning approach, IEEE Access, 8, 23802, 10.1109/ACCESS.2020.2968506 Zhirong, 2015, 3D ShapeNets: a deep representation for volumetric shapes, 1912 Guo, 2016, A comprehensive performance evaluation of 3D local feature descriptors, Int J Comput Vis, 116, 66, 10.1007/s11263-015-0824-y Han, 2019, Image-based 3D object reconstruction: state-of-the-art and trends in the deep learning era, IEEE Trans. Pattern Anal. Mach. Intell., 1 Maturana, 2015, VoxNet: A 3D Convolutional Neural Network for real-time object recognition, 922 Wang, 2019, NormalNet: a voxel-based CNN for 3D object classification and retrieval, Neurocomputing, 323, 139, 10.1016/j.neucom.2018.09.075 Qi, 2017, PointNet++: deep hierarchical feature learning on point sets in a metric space, 1 Ma, 2019, Learning multi-view representation with LSTM for 3-D shape recognition and retrieval, IEEE Trans. Multimedia, 21, 1169, 10.1109/TMM.2018.2875512 Cheraghian, 2019, 3DCapsule: extending the capsule architecture to classify 3D point clouds, 1194 Yoon, 2017, Sketch-based 3D object recognition from locally optimized sparse features, Neurocomputing, 267, 556, 10.1016/j.neucom.2017.06.034 Khan, 2019, Unsupervised primitive discovery for improved 3D generative modeling, 9731 Yang, 2018, FoldingNet: point cloud auto-encoder via deep grid deformation, 206 Q. Kong, B. Tong, M. Klinkigt, Y. Watanabe, N. Akira, T. Murakami. Active Generative Adversarial Network for Image Classification. ArXiv:190607133 [Cs, Stat] 2019. Luo, 2019, GAN-based augmentation for improving CNN performance of classification of defective photovoltaic module cells in electroluminescence images, IOP Conf Ser: Earth Environ Sci, 354 M. Mirza, S. Osindero, Conditional Generative Adversarial Nets. ArXiv:14111784 [Cs, Stat] 2014. Odena, 2017, Conditional image synthesis with auxiliary classifier GANs, 2642 Han, 2019, Unsupervised learning of 3-D local features from raw voxels based on a novel permutation voxelization strategy, IEEE Trans. Cybern., 49, 481, 10.1109/TCYB.2017.2778764 Muzahid, 2021, CurveNet: Curvature-based multitask learning deep networks for 3D object recognition, IEEE/CAA J. Autom. Sinica, 8, 1177, 10.1109/JAS.2020.1003324 Guo, 2014, 3D object recognition in cluttered scenes with local surface features: a survey, IEEE Trans. Pattern Anal. Mach. Intell., 36, 2270, 10.1109/TPAMI.2014.2316828 Brock, 2016, Generative and discriminative voxel modeling with convolutional neural networks, 1 A.X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, et al., ShapeNet: an Information-Rich 3D Model Repository. CoRR 2015; abs/1512.03012. Huang, 2019, 3D Volumetric modeling with introspective neural networks, AAAI, 33, 8481, 10.1609/aaai.v33i01.33018481 Han, 2019, View inter-prediction GAN: unsupervised representation learning for 3D shapes by learning global shape memories to support local view predictions, AAAI, 33, 8376, 10.1609/aaai.v33i01.33018376 Jiang, 2019, MLVCNN: multi-loop-view convolutional neural network for 3D shape retrieval, AAAI, 33, 8513, 10.1609/aaai.v33i01.33018513 E. Denton, S. Gross, R. Fergus, Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks. ArXiv:161106430 [Cs], 2016. A. Odena, Semi-Supervised Learning with Generative Adversarial Networks. ArXiv:160601583 [Cs, Stat], 2016. Zhi, 2018, Toward real-time 3D object recognition: a lightweight volumetric CNN framework using multitask learning, Comput. Graphics, 71, 199, 10.1016/j.cag.2017.10.007 Riegler, 2017, Learning deep 3D representations at high resolutions, 6620 Kanezaki, 2018, RotationNet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints, 5010 Liu, 2020, Relation-shape convolutional neural network for point cloud analysis, 8887 Liu, 2019, Multi-view hierarchical fusion network for 3D object retrieval and classification, IEEE Access, 7, 153021, 10.1109/ACCESS.2019.2947245 Li, 2018, SO-Net: self-organizing network for point cloud analysis, 9397 Radford, 2016, Unsupervised representation learning with deep convolutional generative adversarial networks Shrivastava, 2017, Learning from simulated and unsupervised images through adversarial training, 2242 T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation. ArXiv:171010196 [Cs, Stat], 2018. Muzahid, 2020, A new volumetric CNN for 3D object classification based on joint multiscale feature and subvolume supervised learning approaches, Comput. Intell. Neurosci., 1, 10.1155/2020/5851465 D. Zhang, A. Khoreva, Progressive Augmentation of GANs. ArXiv:190110422 [Cs], 2019. He, 2016, Deep residual learning for image recognition, 770 Diederik, 2015, Adam: a method for stochastic optimization Sedaghat, 2017, Orientation-boosted Voxel Nets for 3D Object Recognition, 1