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Coordinate-based networks have emerged as a powerful tool for 3D representation and scene reconstruction.
Pyramidal parametrics
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Ntire 2017 challenge on single image super-resolution: Dataset and study
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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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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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Neural scene representation and rendering
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Deep blending for free-viewpoint image-based rendering
Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow · 2018
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Stereo magnification: Learning view synthesis using multiplane images
Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely · 2018
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Deepview: View synthesis with learned gradient descent
John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, and Richard Tucker · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 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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Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
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Local light field fusion: Practical view synthesis with prescriptive sampling guidelines
Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar · 2019
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Occupancy flow: 4D reconstruction by learning particle dynamics
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2019
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Texture fields: Learning texture representations in function space
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, 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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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, et al · 2019
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PIFu: Pixel-aligned implicit function for high-resolution clothed human digitization
Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li · 2019
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DeepVoxels: Learning persistent 3D feature embeddings
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer · 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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Deferred neural rendering: Image synthesis using neural textures
Justus Thies, Michael Zollhöfer, and Matthias Nießner · 2019
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Dynamic graph CNN for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 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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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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SAL: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 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 Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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On the effectiveness of weight-encoded neural implicit 3D shapes
Thomas Davies, Derek Nowrouzezahrai, and Alec Jacobson · 2020
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FastNeRF: High-fidelity neural rendering at 200fps
Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin · 2021
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Baking neural radiance fields for real-time view synthesis
Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, and Paul Debevec · 2021
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SAPE: Spatially-adaptive progressive encoding for neural optimization
Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or · 2021
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Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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Neural lumigraph rendering
Petr Kellnhofer, Lars Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, and Gordon Wetzstein · 2021
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AutoInt: Automatic integration for fast neural volume rendering
David B. Lindell, Julien N. P. Martel, and Gordon Wetzstein · 2021
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Multiplicative filter networks
Rizal Fathony, Anit Kumar Sahu, Devin Willmott, and J Zico Kolter · 2020
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Local deep implicit functions for 3D shape
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser · 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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Local implicit grid representations for 3D scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser · 2020
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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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Semantic implicit neural scene representations with semi-supervised training
Amit Kohli, Vincent Sitzmann, and Gordon Wetzstein · 2020
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Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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ACORN: Adaptive coordinate networks for neural scene representation
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NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
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Modulated periodic activations for generalizable local functional representations
Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, and Manmohan Chandraker · 2021
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DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
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Giraffe: Representing scenes as compositional generative neural feature fields
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UNISURF: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
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Shape as points: A differentiable Poisson solver
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D-NeRF: Neural radiance fields for dynamic scenes
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KiloNeRF: Speeding up neural radiance fields with thousands of tiny mlps
Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger · 2021
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NeRV: Neural reflectance and visibility fields for relighting and view synthesis
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Neural geometric level of detail: Real-time rendering with implicit 3D shapes
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Volume rendering of neural implicit surfaces
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PlenOctrees for real-time rendering of neural radiance fields
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Efficient geometry-aware 3D generative adversarial networks
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GRAM: Generative radiance manifolds for 3d-aware image generation
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StyleSDF: High-resolution 3D-consistent image and geometry generation
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Geometry-consistent neural shape representation with implicit displacement fields
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