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We propose a method for 3D shape reconstruction from unoriented point clouds.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Kunihiko Fukushima · 1980
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Marching cubes: A high resolution 3d surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
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A survey of the statistical theory of shape
David G Kendall · 1989
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Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Computing and rendering point set surfaces
M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin, and C.T. Silva · 2003
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Lie groups, Lie algebras, and representations: an elementary introduction , volume 10
Brian C Hall · 2003
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Representation theory: a first course , volume 129
William Fulton and Joe Harris · 2013
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Pointnet: Deep learning on point sets for 3d classification and segmentation, 2016
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2016
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A survey of surface reconstruction from point clouds
Matthew Berger, Andrea Tagliasacchi, Lee M Seversky, Pierre Alliez, Gael Guennebaud, Joshua A Levine, Andrei Sharf, and Claudio T Silva · 2017
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J. Guibas · 2017
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Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Learning so(3) equivariant representations with spherical cnns
Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, and Kostas Daniilidis · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Learning steerable filters for rotation equivariant cnns
Maurice Weiler, Fred A. Hamprecht, and Martin Storath · 2018
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Gauge equivariant convolutional networks and the icosahedral CNN
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling · 2019
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Spiralnet++: A fast and highly efficient mesh convolution operator
Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou · 2019
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Neural-pull: Learning signed distance functions from point clouds by learning to pull space onto surfaces
Ma Baorui, Han Zhizhong, Liu Yu-Shen, and Zwicker Matthias · 2021
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Equivariant point network for 3d point cloud analysis
Haiwei Chen, Shichen Liu, Weikai Chen, Hao Li, and Randall Hill · 2021
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Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs
Pim de Haan, Maurice Weiler, Taco Cohen, and Max Welling · 2021
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Vector neurons: A general framework for so(3)-equivariant networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J. Guibas · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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What do single-view 3d reconstruction networks learn?
Maxim Tatarchenko, Stephan R. Richter, Rene Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
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Pointconv: Deep convolutional networks on 3d point clouds
Wenxuan Wu, Zhongang Qi, and Li Fuxin · 2019
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Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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Dynamic plane convolutional occupancy networks
Stefan Lionar, Daniil Emtsev, Dusan Svilarkovic, and Songyou Peng · 2021
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Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
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3d object detection with pointformer
Xuran Pan, Zhuofan Xia, Shiji Song, Li Erran Li, and Gao Huang · 2021
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A functional approach to rotation equivariant non-linearities for tensor field networks
Adrien Poulenard and Leonidas J. Guibas · 2021
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Group equivariant stand-alone self-attention for vision
David W. Romero and Jean-Baptiste Cordonnier · 2021
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Sa-convonet: Sign-agnostic optimization of convolutional occupancy networks
Jiapeng Tang, Jiabao Lei, Dan Xu, Feiying Ma, Kui Jia, and Lei Zhang · 2021
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Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling · 2021
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Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
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Pointr: Diverse point cloud completion with geometry-aware transformers
Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou · 2021
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Correspondence-free point cloud registration with SO(3)-equivariant implicit shape representations
Minghan Zhu, Maani Ghaffari, and Huei Peng · 2021
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Poco: Point convolution for surface reconstruction
Alexandre Boulch and Renaud Marlet · 2022
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Geometric and physical quantities improve e(3) equivariant message passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, and Max Welling · 2022
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3d equivariant graph implicit functions, 2022
Yunlu Chen, Basura Fernando, Hakan Bilen, Matthias Nießner, and Efstratios Gavves · 2022
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A survey of deep learning-based mesh processing
He Wang and Juyong Zhang · 2022
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Neural fields as learnable kernels for 3d reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, and Or Litany · 2022
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