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Ralph R. Martin | Shi-Min Hu | Tai-Jiang Mu | Jiahui Huang | Jun-Xiong Cai | Zheng-Ning Liu | Meng-Hao Guo
[1] Yue Gao,et al. MeshNet: Mesh Neural Network for 3D Shape Representation , 2018, AAAI.
[2] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[3] Wei Liu,et al. Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images , 2018, ECCV.
[4] 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).
[5] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[6] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Alexander M. Bronstein,et al. Deep Functional Maps: Structured Prediction for Dense Shape Correspondence , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[8] 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).
[9] Malcolm A. Sabin,et al. Behaviour of recursive division surfaces near extraordinary points , 1998 .
[10] Vladlen Koltun,et al. Tangent Convolutions for Dense Prediction in 3D , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[11] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[12] Ersin Yumer,et al. Convolutional neural networks on surfaces via seamless toric covers , 2017, ACM Trans. Graph..
[13] Xianzhi Li,et al. DNF-Net: A Deep Normal Filtering Network for Mesh Denoising , 2020, IEEE Transactions on Visualization and Computer Graphics.
[14] Andrei Khodakovsky,et al. Hybrid meshes: multiresolution using regular and irregular refinement , 2002, SCG '02.
[15] Leif Kobbelt,et al. √3-subdivision , 2000, SIGGRAPH.
[16] Dragomir Anguelov,et al. SCAPE: shape completion and animation of people , 2005, ACM Trans. Graph..
[17] Michael J. Black,et al. Generating 3D faces using Convolutional Mesh Autoencoders , 2018, ECCV.
[18] Maks Ovsjanikov,et al. Multi-directional geodesic neural networks via equivariant convolution , 2018, ACM Trans. Graph..
[19] N. Dyn,et al. A butterfly subdivision scheme for surface interpolation with tension control , 1990, TOGS.
[20] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[21] Subhransu Maji,et al. Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[22] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[23] Davide Scaramuzza,et al. Primal-Dual Mesh Convolutional Neural Networks , 2020, NeurIPS.
[24] Wei Wu,et al. PointCNN: Convolution On X-Transformed Points , 2018, NeurIPS.
[25] Abhishek Sharma,et al. Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Gershon Elber,et al. Orientation analysis of 3D objects toward minimal support volume in 3D-printing , 2015, Comput. Graph..
[27] 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).
[28] Lin Gao,et al. A survey on deep geometry learning: From a representation perspective , 2020, Computational Visual Media.
[29] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Vladimir G. Kim,et al. Neural subdivision , 2020, ACM Trans. Graph..
[31] Zhichao Zhou,et al. DeepPano: Deep Panoramic Representation for 3-D Shape Recognition , 2015, IEEE Signal Processing Letters.
[32] Wojciech Matusik,et al. Articulated mesh animation from multi-view silhouettes , 2008, ACM Trans. Graph..
[33] Ming Dong,et al. Directionally Convolutional Networks for 3D Shape Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Stephan Günnemann,et al. Dual-Primal Graph Convolutional Networks , 2018, ArXiv.
[35] Andrei Khodakovsky,et al. Globally smooth parameterizations with low distortion , 2003, ACM Trans. Graph..
[36] Ralph R. Martin,et al. PCT: Point cloud transformer , 2020, Computational Visual Media.
[37] Shi-Min Hu,et al. Jittor: a novel deep learning framework with meta-operators and unified graph execution , 2020, Science China Information Sciences.
[38] Charles T. Loop,et al. Smooth Subdivision Surfaces Based on Triangles , 1987 .
[39] Michael Garland,et al. Surface simplification using quadric error metrics , 1997, SIGGRAPH.
[40] Ron Goldman,et al. Nonlinear subdivision through nonlinear averaging , 2008, Comput. Aided Geom. Des..
[41] Vladimir G. Kim,et al. GWCNN: A Metric Alignment Layer for Deep Shape Analysis , 2017, Comput. Graph. Forum.
[42] Yue Wang,et al. Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..
[43] Maks Ovsjanikov,et al. Unsupervised Deep Learning for Structured Shape Matching , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[44] Ilya Kostrikov,et al. Surface Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] Stefanos Zafeiriou,et al. SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[46] Pierre Vandergheynst,et al. Geodesic Convolutional Neural Networks on Riemannian Manifolds , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).
[47] E. Catmull,et al. Recursively generated B-spline surfaces on arbitrary topological meshes , 1978 .
[48] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[49] Leonidas J. Guibas,et al. SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Jing Ren,et al. Continuous and orientation-preserving correspondences via functional maps , 2018, ACM Trans. Graph..
[51] Bastian Leibe,et al. DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Hans-Peter Seidel,et al. VNect , 2017, ACM Trans. Graph..
[53] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Paul Suetens,et al. SHREC '11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes , 2011, 3DOR@Eurographics.
[55] Jing Ren,et al. ZoomOut: Spectral Upsampling for Efficient Shape Correspondence , 2019, ACM Trans. Graph..
[56] Johannes Wallner,et al. Geometric Modeling with Conical Meshes and Developable Surfaces , 2006, ACM Trans. Graph..
[57] Jieyu Zhao,et al. Mesh Convolution: A Novel Feature Extraction Method for 3D Nonrigid Object Classification , 2021, IEEE Transactions on Multimedia.
[58] Yaron Lipman,et al. Surface Networks via General Covers , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[59] Justin Solomon,et al. HodgeNet , 2021, ACM Trans. Graph..
[60] Paolo Cignoni,et al. MeshLab: an Open-Source Mesh Processing Tool , 2008, Eurographics Italian Chapter Conference.
[61] Michael J. Black,et al. FAUST: Dataset and Evaluation for 3D Mesh Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[62] Ayellet Tal,et al. MeshWalker: Deep Mesh Understanding by Random Walks , 2020, ACM Trans. Graph..
[63] Karthik Ramani,et al. Deep Learning 3D Shape Surfaces Using Geometry Images , 2016, ECCV.
[64] 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).
[65] Keenan Crane,et al. Navigating intrinsic triangulations , 2019, ACM Trans. Graph..
[66] Leonidas J. Guibas,et al. TextureNet: Consistent Local Parametrizations for Learning From High-Resolution Signals on Meshes , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Marcel Campen,et al. A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes , 2018, ECCV Workshops.
[68] Li Wang,et al. MeshSNet: Deep Multi-scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces , 2019, MICCAI.
[69] Leonidas J. Guibas,et al. Robust Watertight Manifold Surface Generation Method for ShapeNet Models , 2018, ArXiv.
[70] Xin Tong,et al. PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames , 2018, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[71] Shi-Min Hu,et al. High-Quality Textured 3D Shape Reconstruction with Cascaded Fully Convolutional Networks , 2021, IEEE Transactions on Visualization and Computer Graphics.
[72] Matthias Nießner,et al. Scan2Mesh: From Unstructured Range Scans to 3D Meshes , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[73] Sebastian Scherer,et al. VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[74] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[75] Hans-Peter Seidel,et al. A Shrink Wrapping Approach to Remeshing Polygonal Surfaces , 1999, Comput. Graph. Forum.
[76] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[77] Mathieu Aubry,et al. 3D-CODED: 3D Correspondences by Deep Deformation , 2018, ECCV.
[78] Peter Schröder,et al. Normal meshes , 2000, SIGGRAPH.
[79] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[80] Daniel Cohen-Or,et al. Active co-analysis of a set of shapes , 2012, ACM Trans. Graph..
[81] Jonathan Masci,et al. Learning shape correspondence with anisotropic convolutional neural networks , 2016, NIPS.
[82] Daniela Giorgi,et al. SHape REtrieval Contest 2007: Watertight Models Track , 2007 .
[83] David P. Dobkin,et al. MAPS: multiresolution adaptive parameterization of surfaces , 1998, SIGGRAPH.