GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
暂无分享,去创建一个
David Meger | Edward Smith | Scott Fujimoto | Adriana Romero | Adriana Romero | D. Meger | Scott Fujimoto | Edward James Smith
[1] Jonathan Masci,et al. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[3] Hao Su,et al. A Point Set Generation Network for 3D Object Reconstruction from a Single Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Jiajun Wu,et al. MarrNet: 3D Shape Reconstruction via 2.5D Sketches , 2017, NIPS.
[5] Jiajun Wu,et al. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.
[6] Wei Liu,et al. Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images , 2018, ECCV.
[7] Xiao-Ming Wu,et al. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.
[8] Andrea Vedaldi,et al. Learning 3D Object Categories by Looking Around Them , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[10] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[11] Silvio Savarese,et al. 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction , 2016, ECCV.
[12] Jiajun Wu,et al. Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] David Meger,et al. Improved Adversarial Systems for 3D Object Generation and Reconstruction , 2017, CoRL.
[14] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[15] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[16] Thomas Brox,et al. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[17] Alessandro Sperduti,et al. A general framework for adaptive processing of data structures , 1998, IEEE Trans. Neural Networks.
[18] Alexei A. Efros,et al. Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Robert C. Bolles,et al. Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.
[20] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[21] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[22] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[23] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[24] Xavier Bresson,et al. CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters , 2017, IEEE Transactions on Signal Processing.
[25] 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).
[26] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[27] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Frédéric Maire,et al. Learning Free-Form Deformations for 3D Object Reconstruction , 2018, ACCV.
[29] Tatsuya Harada,et al. Neural 3D Mesh Renderer , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] David Meger,et al. Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation , 2018, NeurIPS.
[31] Yoshua Bengio,et al. On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[32] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[33] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[34] Mathieu Aubry,et al. 3D-CODED: 3D Correspondences by Deep Deformation , 2018, ECCV.
[35] Jiajun Wu,et al. Learning Shape Priors for Single-View 3D Completion and Reconstruction , 2018, ECCV.
[36] David Eberly,et al. Distance Between Point and Triangle in 3D , 2008 .
[37] Jitendra Malik,et al. Category-specific object reconstruction from a single image , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Jitendra Malik,et al. Hierarchical Surface Prediction for 3D Object Reconstruction , 2017, 2017 International Conference on 3D Vision (3DV).
[39] Gernot Riegler,et al. OctNet: Learning Deep 3D Representations at High Resolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[41] Alexey Dosovitskiy,et al. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds , 2018, NeurIPS.
[42] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[43] Leon A. Gatys,et al. Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[45] Jitendra Malik,et al. Learning Category-Specific Mesh Reconstruction from Image Collections , 2018, ECCV.
[46] Jiajun Wu,et al. Learning to Reconstruct Shapes from Unseen Classes , 2018, NeurIPS.
[47] Vittorio Ferrari,et al. Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision , 2018, BMVC.
[48] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[49] Yoshua Bengio,et al. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[50] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[51] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[52] Anders P. Eriksson,et al. Image2Mesh: A Learning Framework for Single Image 3D Reconstruction , 2017, ACCV.
[53] Mathieu Aubry,et al. A Papier-Mache Approach to Learning 3D Surface Generation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[54] Yoshua Bengio,et al. Convolutional neural networks for mesh-based parcellation of the cerebral cortex , 2018 .
[55] Bernard Chazelle,et al. Shape distributions , 2002, TOGS.
[56] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[57] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.