Principal views selection based on growing graph convolution network for multi-view 3D model recognition

[1]  Huaxiang Zhang,et al.  Group-pair deep feature learning for multi-view 3d model retrieval , 2021, Appl. Intell..

[2]  Anan Liu,et al.  Monocular Image-Based 3-D Model Retrieval: A Benchmark , 2021, IEEE Transactions on Cybernetics.

[3]  Xin Ning,et al.  Review of multi-view 3D object recognition methods based on deep learning , 2021, Displays.

[4]  Marcin Maleszka,et al.  Geometric modeling: Background for processing the 3d objects , 2021, Applied Intelligence.

[5]  Jiangshe Zhang,et al.  DRCNN: Dynamic Routing Convolutional Neural Network for Multi-View 3D Object Recognition , 2020, IEEE Transactions on Image Processing.

[6]  Xin Ning,et al.  Voxel-based three-view hybrid parallel network for 3D object classification , 2021, Displays.

[7]  Hui Zeng,et al.  Hierarchical Graph Attention Based Multi-View Convolutional Neural Network for 3D Object Recognition , 2021, IEEE Access.

[8]  Jian Sun,et al.  View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Anan Liu,et al.  Multi-View Saliency Guided Deep Neural Network for 3-D Object Retrieval and Classification , 2020, IEEE Transactions on Multimedia.

[10]  Hui Wei,et al.  Latent-MVCNN: 3D Shape Recognition Using Multiple Views from Pre-defined or Random Viewpoints , 2020, Neural Processing Letters.

[11]  Shaohua Wan,et al.  Exploring Deep Learning for View-Based 3D Model Retrieval , 2020, ACM Trans. Multim. Comput. Commun. Appl..

[12]  Qiang Huang,et al.  View-based weight network for 3D object recognition , 2020, Image Vis. Comput..

[13]  Dan Song,et al.  Multi-View Hierarchical Fusion Network for 3D Object Retrieval and Classification , 2019, IEEE Access.

[14]  Liwei Wang,et al.  Learning Relationships for Multi-View 3D Object Recognition , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[15]  Yue Gao,et al.  DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval , 2019, AAAI.

[16]  Wei An,et al.  Learning Multi-View Representation With LSTM for 3-D Shape Recognition and Retrieval , 2019, IEEE Transactions on Multimedia.

[17]  Shiming Xiang,et al.  Relation-Shape Convolutional Neural Network for Point Cloud Analysis , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Kostas Daniilidis,et al.  Equivariant Multi-View Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[19]  Junwei Han,et al.  3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation , 2019, IEEE Transactions on Image Processing.

[20]  Junwei Han,et al.  SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention , 2019, IEEE Transactions on Image Processing.

[21]  Chi-Man Vong,et al.  Unsupervised Learning of 3-D Local Features From Raw Voxels Based on a Novel Permutation Voxelization Strategy , 2019, IEEE Transactions on Cybernetics.

[22]  Yue Gao,et al.  MeshNet: Mesh Neural Network for 3D Shape Representation , 2018, AAAI.

[23]  Yue Gao,et al.  Hypergraph Neural Networks , 2018, AAAI.

[24]  Yan Zhang,et al.  VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification , 2018, IEEE Transactions on Visualization and Computer Graphics.

[25]  Yue Wang,et al.  Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..

[26]  Wei Wu,et al.  PointCNN: Convolution On X-Transformed Points , 2018, NeurIPS.

[27]  Yue Gao,et al.  Predicting Personalized Image Emotion Perceptions in Social Networks , 2018, IEEE Transactions on Affective Computing.

[28]  Subhransu Maji,et al.  A Deeper Look at 3D Shape Classifiers , 2018, ECCV Workshops.

[29]  Yue Gao,et al.  Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification , 2018, IEEE Transactions on Image Processing.

[30]  Yi Fang,et al.  Siamese CNN-BiLSTM Architecture for 3D Shape Representation Learning , 2018, IJCAI.

[31]  Yue Gao,et al.  GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[32]  Junsong Yuan,et al.  Multi-view Harmonized Bilinear Network for 3D Object Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[33]  Vincent Lepetit,et al.  3D Pose Estimation and 3D Model Retrieval for Objects in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[34]  Junwei Han,et al.  Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network With Coupled Softmax , 2018, IEEE Transactions on Image Processing.

[35]  Yu-Ting Su,et al.  View-Based 3-D Model Retrieval: A Benchmark , 2018, IEEE Transactions on Cybernetics.

[36]  Ioannis Pratikakis,et al.  Ensemble of PANORAMA-based convolutional neural networks for 3D model classification and retrieval , 2017, Comput. Graph..

[37]  Yasuyuki Matsushita,et al.  RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints , 2016, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[38]  Kaleem Siddiqi,et al.  Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition , 2019, BMVC.

[39]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[40]  Junwei Han,et al.  Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[41]  Hong Liu,et al.  View-based 3D object retrieval with discriminative views , 2017, Neurocomputing.

[42]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[43]  Victor S. Lempitsky,et al.  Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[44]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Ioannis Pratikakis,et al.  Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval , 2017, 3DOR@Eurographics.

[46]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[47]  Paulo E. Rauber,et al.  Visualizing Time-Dependent Data Using Dynamic t-SNE , 2016, EuroVis.

[48]  Bo Li,et al.  Large-Scale 3D Shape Retrieval from ShapeNet Core55 , 2016, 3DOR@Eurographics.

[49]  Leonidas J. Guibas,et al.  Volumetric and Multi-view CNNs for Object Classification on 3D Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Longin Jan Latecki,et al.  GIFT: A Real-Time and Scalable 3D Shape Search Engine , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[51]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Hongxun Yao,et al.  View-based 3D object retrieval via multi-modal graph learning , 2015, Signal Process..

[53]  Subhransu Maji,et al.  Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[54]  Jianxiong Xiao,et al.  3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Andrew Y. Ng,et al.  Convolutional-Recursive Deep Learning for 3D Object Classification , 2012, NIPS.

[56]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[57]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[58]  Deborah Estrin,et al.  VoxNet: An Interactive, Rapidly-Deployable Acoustic Monitoring Platform , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[59]  Ioannis Pratikakis,et al.  Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation , 2007, Pattern Recognit..

[60]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

[61]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.