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Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation.
Marching cubes: A high resolution 3d surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
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Surface reconstruction from unorganized points
Hugues Hoppe, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle · 1992
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The ball-pivoting algorithm for surface reconstruction
Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cláudio Silva, and Gabriel Taubin · 1999
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Reconstruction and representation of 3d objects with radial basis functions
Jonathan C Carr, Richard K Beatson, Jon B Cherrie, Tim J Mitchell, W Richard Fright, Bruce C McCallum, and Tim R Evans · 2001
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Fast surface reconstruction using the level set method
Hong-Kai Zhao, Stanley Osher, and Ronald Fedkiw · 2001
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Scattered data approximation
Holger Wendland · 2004
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Estimating differential quantities using polynomial fitting of osculating jets
Frédéric Cazals and Marc Pouget · 2005
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Implicit surface modelling as an eigenvalue problem
Christian Walder, Olivier Chapelle, and Bernhard Schölkopf · 2005
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Poisson surface reconstruction
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
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Implicit surfaces with globally regularised and compactly supported basis functions
Christian Walder, Olivier Chapelle, and Bernhard Schölkopf · 2007
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Signing the unsigned: Robust surface reconstruction from raw pointsets
Patrick Mullen, Fernando De Goes, Mathieu Desbrun, David Cohen-Steiner, and Pierre Alliez · 2010
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Kenshi Takayama, Alec Jacobson, Ladislav Kavan, and Olga Sorkine-Hornung · 2014
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Signed distance fields for polygon soup meshes
Hongyi Xu and Jernej Barbič · 2014
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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, et al · 2015
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Understanding deep neural networks with rectified linear units
Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee · 2016
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Differential Geometry of Curves and Surfaces: Revised and Updated Second Edition
Manfredo P Do Carmo · 2016
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Deep learning 3d shape surfaces using geometry images
Ayan Sinha, Jing Bai, and Karthik Ramani · 2016
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Modeling facial geometry using compositional vaes
Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, and Yaser Sheikh · 2018
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Multi-chart generative surface modeling
Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, and Yaron Lipman · 2018
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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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Deformable shape completion with graph convolutional autoencoders
Or Litany, Alex Bronstein, Michael Bronstein, and Ameesh Makadia · 2018
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Learning 3d shape completion from laser scan data with weak supervision
David Stutz and Andreas Geiger · 2018
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Point set processing
Pierre Alliez, Simon Giraudot, Clément Jamin, Florent Lafarge, Quentin Mérigot, Jocelyn Meyron, Laurent Saboret, Nader Salman, and Shihao Wu · 2019
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Dynamic FAUST: Registering human bodies in motion
Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J. Black · 2017
Cited alongside, same era.
Optimizing the latent space of generative networks
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, and Arthur Szlam · 2017
Cited alongside, same era.
Shape completion using 3d-encoder-predictor cnns and shape synthesis
Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
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Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, and Yaron Lipman · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Cvxnets: Learnable convex decomposition
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, and Andrea Tagliasacchi · 2019
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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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Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, and Thomas Funkhouser · 2019
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Variational implicit point set surfaces
Zhiyang Huang, Nathan Carr, and Tao Ju · 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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Deep geometric prior for surface reconstruction
Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo · 2019
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