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NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i.e., reconstructing real-world scenes and registering camera parameters simultaneously.
Ray tracing volume densities
James T Kajiya and Brian P Von Herzen · 1984
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Optical models for direct volume rendering
Nelson Max · 1995
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A stratified approach to metric self-calibration
Marc Pollefeys and Luc Van Gool · 1997
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Bundle adjustment—a modern synthesis
Bill Triggs, Philip F McLauchlan, Richard I Hartley, and Andrew W Fitzgibbon · 1999
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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A tutorial on quantitative trajectory evaluation for visual (-inertial) odometry
Zichao Zhang and Davide Scaramuzza · 2018
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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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Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 2019
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Pushing the boundaries of view extrapolation with multiplane images
Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely · 2019
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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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3d photography using context-aware layered depth inpainting
Meng-Li Shih, Shih-Yang Su, Johannes Kopf, and Jia-Bin Huang · 2020
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Self-calibrating neural radiance fields
Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Anima Anandkumar, Minsu Cho, and Jaesik Park · 2021
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Neural scene flow fields for space-time view synthesis of dynamic scenes
Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang · 2021
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Barf: Bundle-adjusting neural radiance fields
Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey · 2021
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Stable view synthesis
Gernot Riegler and Vladlen Koltun · 2021
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Nerv: Neural reflectance and visibility fields for relighting and view synthesis
Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron · 2021
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Nerf–: Neural radiance fields without known camera parameters
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Single-view view synthesis with multiplane images
Richard Tucker and Noah Snavely · 2020
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pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein · 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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Nerfies: Deformable neural radiance fields
Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla
Cited in the paper.
Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields
Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, and Steven M Seitz
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Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu · 2021
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inerf: Inverting neural radiance fields for pose estimation
Lin Yen-Chen, Pete Florence, Jonathan T Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin · 2021
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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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