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Point set is a flexible and lightweight representation widely used for 3D deep learning.
Marching cubes: A high resolution 3d surface construction algorithm
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The approximation power of moving least-squares
David Levin · 1998
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Point set surfaces
Marc Alexa, Johannes Behr, Daniel Cohen-Or, Shachar Fleishman, David Levin, and Claudio T. Silva · 2001
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Reconstruction and representation of 3D objects with radial basis functions
J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, and T. R. Evans · 2001
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Efficient simplification of point-sampled surfaces
Mark Pauly, Markus Gross, and Leif P. Kobbelt · 2002
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Multi-level partition of unity implicits
Yutaka Ohtake, Alexander Belyaev, Marc Alexa, Greg Turk, and Hans-Peter Seidel · 2003
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Poisson surface reconstruction
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
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Algebraic point set surfaces
Gaël Guennebaud and Markus Gross · 2007
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A survey of methods for moving least squares surfaces
Z.-Q. Cheng, Y.-Z. Wang, B. Li, K. Xu, G. Dang, and S.-Y. Jin · 2008
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Dynamic sampling and rendering of algebraic point set surfaces
Gaël Guennebaud, Marcel Germann, and Markus Gross · 2008
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Provably good moving least squares
Ravikrishna Kolluri · 2008
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Consolidation of unorganized point clouds for surface reconstruction
Hui Huang, Dan Li, Hao Zhang, Uri Ascher, and Daniel Cohen-Or · 2009
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Feature preserving point set surfaces based on non-linear kernel regression
Cengiz Oztireli, Gaël Guennebaud, and Markus Gross · 2009
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Screened Poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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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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Generative and discriminative voxel modeling with convolutional neural networks
Andrew Brock, Theodore Lim, J.M. Ritchie, and Nick Weston · 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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Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum · 2016
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A survey of surface reconstruction from point clouds
Matthew Berger, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Gaël Guennebaud, Joshua A. Levine, Andrei Sharf, and Claudio T. Silva · 2017
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Hierarchical surface prediction for 3D object reconstruction
Christian Häne, Shubham Tulsiani, 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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PointNet: Deep learning on point sets for 3D classification and segmentation
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Charles R Qi, Li Yi, 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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A point set generation network for 3D object reconstruction from a single image
Hao Su, Haoqiang Fan, and Leonidas Guibas · 2017
Learning elementary structures for 3D shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry · 2019
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Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 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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Learning Adaptive hierarchical cuboid abstractions of 3D shape collections
Chun-Yu Sun, Qian-Fang Zou, Xin Tong, and Yang Liu · 2019
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Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
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Learning shape abstractions by assembling volumetric primitives
Shubham Tulsiani, , Hao Su, Leonidas J. Guibas, Alexei A. Efros, and Jitendra Malik · 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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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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Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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AtlasNet: A Papier-Mâché approach to learning 3D surface generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry · 2018
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Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, and Xin Tong · 2019
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What do single-view 3D reconstruction networks learn?
Maxim Tatarchenko, Stephan R. Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
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Deep geometric prior for surface reconstruction
Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo · 2019
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PointFlow: 3D point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Differentiable surface splatting for point-based geometry processing
Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine-Hornung · 2019
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Patch-based progressive 3D point set upsampling
Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, and Olga Sorkine-Hornung · 2019
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SAL: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 2020
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Deep local shapes: Learning local SDF priors for detailed 3D reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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Implicit functions in feature space for 3D shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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Curriculum DeepSDF
Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, and Leonidas J. Guibas · 2020
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Deep learning for 3D point clouds: A survey
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun · 2020
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Local implicit grid representations for 3D scenes
Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Deep Octree-based CNNs with output-guided skip connections for 3D shape and scene completion
Peng-Shuai Wang, Yang Liu, and Xin Tong · 2020
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