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Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
A newton-raphson method for the solution of systems of equations
Adi Ben-Israel · 1966
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Physically-based methods for polygonization of implicit surfaces
Luiz Henrique de Figueiredo, Jonas de Miranda Gomes, Demetri Terzopoulos, and Luiz Velho · 1992
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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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Using particles to sample and control implicit surfaces
Andrew P Witkin and Paul S Heckbert · 1994
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A volumetric method for building complex models from range images
Brian Curless and Marc Levoy · 1996
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Sphere tracing: A geometric method for the antialiased ray tracing of implicit surfaces
John C Hart · 1996
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The approximation power of moving least-squares
David Levin · 1998
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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
Earlier work this paper cites.
Poisson surface reconstruction
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
Earlier work this paper cites.
Fast sampling of implicit surfaces by particle systems
Florian Levet, Xavier Granier, and Christophe Schlick · 2006
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Sampling point-set implicits
João Proença, Joaquim A Jorge, and Mario Costa Sousa · 2007
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Accurate, dense, and robust multiview stereopsis
Yasutaka Furukawa and Jean Ponce · 2009
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Blender Foundations: The Essential Guide to Learning Blender 2.6
Roland Hess · 2010
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Edge-aware point set resampling
Hui Huang, Shihao Wu, Minglun Gong, Daniel Cohen-Or, Uri Ascher, and Hao Zhang · 2013
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Large scale multi-view stereopsis evaluation
Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs · 2014
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Large-scale data for multiple-view stereopsis
Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl · 2016
Earlier work this paper cites.
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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Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Earlier work this paper cites.
Physically based rendering: From theory to implementation
Matt Pharr, Wenzel Jakob, and Greg Humphreys · 2016
Cited alongside, same era.
Learning efficient point cloud generation for dense 3D object reconstruction
Chen-Hsuan Lin, Chen Kong, and Simon Lucey · 2017
Cited alongside, same era.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik · 2017
Cited alongside, same era.
A papier-mâché approach to learning 3D surface generation
Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry · 2018
Cited alongside, same era.
Unsupervised learning of shape and pose with differentiable point clouds
Eldar Insafutdinov and Alexey Dosovitskiy · 2018
Cited alongside, same era.
Neural 3D mesh renderer
Differentiable surface splatting for point-based geometry processing
Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine-Hornung · 2019
Later among the works it cites.
Patch-based progressive 3d point set upsampling
Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, and Olga Sorkine-Hornung · 2019
Later among the works it cites.
Sal: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 2020
Closest in time.
Sald: Sign agnostic learning with derivatives, 2020
Matan Atzmon and Yaron Lipman · 2020
Closest in time.
Points2surf learning implicit surfaces from point clouds
Philipp Erler, Paul Guerrero, Stefan Ohrhallinger, Niloy J Mitra, and Michael Wimmer · 2020
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Implicit geometric regularization for learning shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
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Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Paparazzi: surface editing by way of multi-view image processing
Hsueh-Ti Derek Liu, Michael Tao, and Alec Jacobson · 2018
Cited alongside, same era.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Cited alongside, same era.
Controlling neural level sets
Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, and Yaron Lipman · 2019
Cited alongside, same era.
Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
Cited alongside, same era.
Soft rasterizer: A differentiable renderer for image-based 3D reasoning
Shichen Liu, Tianye Li, Weikai Chen, and Hao Li · 2019
Cited alongside, same era.
Learning to infer implicit surfaces without supervision
Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li · 2019
Cited alongside, same era.
Closest in time.
SDFDiff: Differentiable rendering of signed distance fields for 3D shape optimization
Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker · 2020
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Differentiable rendering: A survey
Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, and Adrien Gaidon · 2020
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SDF-SRN: Learning signed distance 3D object reconstruction from static images
Chen-Hsuan Lin, Chaoyang Wang, and Simon Lucey · 2020
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DIST: Rendering deep implicit signed distance function with differentiable sphere tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui · 2020
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NeRF: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Differentiable volumetric rendering: Learning implicit 3D representations without 3D supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Closest in time.
Coupling explicit and implicit surface representations for generative 3D modeling
Omid Poursaeed, Matthew Fisher, Noam Aigerman, and Vladimir G Kim · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien NP Martel, Alexander W Bergman, David B Lindell, and Gordon Wetzstein · 2020
Closest in time.
State of the art on neural rendering
Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al · 2020
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Ladybird: Quasi-Monte Carlo sampling for deep implicit field based 3D reconstruction with symmetry
Yifan Xu, Tianqi Fan, Yi Yuan, and Gurprit Singh · 2020
Closest in time.
Universal differentiable renderer for implicit neural representations, 2020
Lior Yariv, Matan Atzmon, and Yaron Lipman · 2020
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Multiview neural surface reconstruction by disentangling geometry and appearance
Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, and Yaron Lipman · 2020
Closest in time.