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Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images).
Fourier transform of a polygonal shape function and its application in electromagnetics
Shung-Wu Lee and Raj Mittra · 1983
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On the calculation of the fourier transform of a polygonal shape function
Fu-Lai Chu and Chi-Fang Huang · 1989
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Efficient feature extraction for 2d/3d objects in mesh representation
Cha Zhang and Tsuhan Chen · 2001
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Learning occupancy grid maps with forward sensor models
Sebastian Thrun · 2003
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Accelerating the nonuniform fast fourier transform
Leslie Greengard and June-Yub Lee · 2004
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
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Multivariate fourier transform methods over simplex and super-simplex domains
Jiachang Sun · 2006
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Discrete fourier analysis on a dodecahedron and a tetrahedron
Huiyuan Li and Yuan Xu · 2009
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A geometric nonuniform fast fourier transform
Ian Sammis and John Strain · 2009
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Voxelpipe: a programmable pipeline for 3d voxelization
Jacopo Pantaleoni · 2011
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Nonlinear shape manifolds as shape priors in level set segmentation and tracking
Victor Adrian Prisacariu and Ian Reid · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Ross Girshick · 2015
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Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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U-net: Convolutional networks for biomedical image segmentation
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
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Grass: Generative recursive autoencoders for shape structures
Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, and Leonidas Guibas · 2017
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Deep learning of directional truncated signed distance function for robust 3d object recognition
Hongsen Liu, Yang Cong, Shuai Wang, Huijie Fan, Dongying Tian, and Yandong Tang · 2017
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Convolutional neural networks on surfaces via seamless toric covers
Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G Kim, and Yaron Lipman · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deeppano: Deep panoramic representation for 3-d shape recognition
Baoguang Shi, Song Bai, Zhichao Zhou, and Xiang Bai · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Cited alongside, same era.
Generative and discriminative voxel modeling with convolutional neural networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Shrec’16 track large-scale 3d shape retrieval from shapenet core55
Manolis Savva, Fisher Yu, Hao Su, M Aono, B Chen, D Cohen-Or, W Deng, Hang Su, Song Bai, Xiang Bai, et al · 2016
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6-dof object pose from semantic keypoints
Georgios Pavlakos, Xiaowei Zhou, Aaron Chan, Konstantinos G Derpanis, and Kostas Daniilidis · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
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Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
Li Yi, Hao Su, Xingwen Guo, and Leonidas Guibas · 2017
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3d-prnn: Generating shape primitives with recurrent neural networks
Chuhang Zou, Ersin Yumer, Jimei Yang, Duygu Ceylan, and Derek Hoiem · 2017
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3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
Angela Dai and Matthias Nießner · 2018
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Neural 3d mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Deep marching cubes: Learning explicit surface representations
Yiyi Liao, Simon Donne, and Andreas Geiger · 2018
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Multi-view consistency as supervisory signal for learning shape and pose prediction
Shubham Tulsiani, Alexei A Efros, and Jitendra Malik · 2018
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Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell · 2018
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