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Encoding 3D points is one of the primary steps in learning-based implicit scene representation.
Ray tracing with cones
John Amanatides · 1984
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Chiun-Hong Chien and Jake K Aggarwal · 1986
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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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Surface reconstruction from unorganized points
Hugues Hoppe, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle · 1992
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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 ball-pivoting algorithm for surface reconstruction
Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cláudio Silva, and Gabriel Taubin · 1999
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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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Stuart J. Russell and Peter Norvig · 2009
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Efficient sparse voxel octrees–analysis, extensions, and implementation
Samuli Laine and Tero Karras · 2010
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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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Diederik P. Kingma and Jimmy Ba · 2015
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Thingi10k: A dataset of 10, 000 3d-printing models
Qingnan Zhou and Alec Jacobson · 2016
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niebner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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Hierarchical surface prediction for 3d object reconstruction
Christian Häne, Shubham Tulsiani, and Jitendra Malik · 2017
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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
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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 Ruizhongtai 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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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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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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Adaptive o-cnn: A patch-based deep representation of 3d shapes
Peng-Shuai Wang, Chun-Yu Sun, Yang Liu, and Xin Tong · 2018
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Abc: A big cad model dataset for geometric deep learning
Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo · 2019
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Neural volumes: learning dynamic renderable volumes from images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 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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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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Acorn: adaptive coordinate networks for neural scene representation
Julien NP Martel, David B Lindell, Connor Z Lin, Eric R Chan, Marco Monteiro, and Gordon Wetzstein · 2021
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Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Michael Oechsle, Songyou Peng, and Andreas Geiger · 2021
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Shape as points: A differentiable poisson solver
Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, and Andreas Geiger · 2021
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Neural geometric level of detail: Real-time rendering with implicit 3d shapes
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler · 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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Matryodshka: Real-time 6dof video view synthesis using multi-sphere images
Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, and James Tompkin · 2020
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Immersive light field video with a layered mesh representation
Michael Broxton, John Flynn, Ryan Overbeck, Daniel Erickson, Peter Hedman, Matthew Duvall, Jason Dourgarian, Jay Busch, Matt Whalen, and Paul Debevec · 2020
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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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Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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Neural unsigned distance fields for implicit function learning
Julian Chibane, Aymen Mir, and Gerard Pons-Moll · 2020
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Neural point cloud rendering via multi-plane projection
Peng Dai, Yinda Zhang, Zhuwen Li, Shuaicheng Liu, and Bing Zeng · 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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Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang · 2021
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Unsupervised 3d learning for shape analysis via multiresolution instance discrimination
Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou, Zhirong Wu, Yang Liu, and Xin Tong · 2021
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Iso-points: Optimizing neural implicit surfaces with hybrid representations
Wang Yifan, Shihao Wu, Cengiz Oztireli, and Olga Sorkine-Hornung · 2021
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Plenoctrees for real-time rendering of neural radiance fields
Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa · 2021
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Poco: Point convolution for surface reconstruction
Alexandre Boulch and Renaud Marlet · 2022
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Tensorf: Tensorial radiance fields
Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su · 2022
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Plenoxels: Radiance fields without neural networks
Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa · 2022
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Meshudf: Fast and differentiable meshing of unsigned distance field networks
Benoit Guillard, Federico Stella, and Pascal Fua · 2022
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Bacon: Band-limited coordinate networks for multiscale scene representation
David B Lindell, Dave Van Veen, Jeong Joon Park, and Gordon Wetzstein · 2022
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Learning smooth neural functions via lipschitz regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, and Or Litany · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
Cheng Sun, Min Sun, and Hwann-Tzong Chen · 2022
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Point cloud utils, 2022
Francis Williams · 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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Neural kernel surface reconstruction
Jiahui Huang, Zan Gojcic, Matan Atzmon, Or Litany, Sanja Fidler, and Francis Williams · 2023
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Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies
Xiaoxiao Long, Cheng Lin, Lingjie Liu, Yuan Liu, Peng Wang, Christian Theobalt, Taku Komura, and Wenping Wang · 2023
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