Fetching the paper…
Reading the bibliography…
Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints.
Efficient ray tracing of volume data
Marc Levoy · 1980
Earlier work this paper cites.
The triangle processor and normal vector shader: A VLSI system for high performance graphics
Michael Deering, Stephanie Winner, Bic Schediwy, Chris Duffy, and Neil Hunt · 1988
Earlier work this paper cites.
Splatting: A parallel, feed-forward volume rendering algorithm
Lee A Westover · 1991
Earlier work this paper cites.
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
Paul Debevec, C. J. Taylor, and Jitendra Malik · 1992
Earlier work this paper cites.
Fast volume rendering using a shear-warp factorization of the viewing transformation
Philippe Lacroute and Marc Levoy · 1994
Earlier work this paper cites.
Optical models for direct volume rendering
Nelson Max · 1995
Earlier work this paper cites.
The lumigraph
Steven J. Gortler, Radek Grzeszczuk, Richard Szeliski, and Michael F. Cohen · 1996
Earlier work this paper cites.
Light field rendering
Marc Levoy and Pat Hanrahan · 1996
Earlier work this paper cites.
Photorealistic scene reconstruction by voxel coloring
Steven M. Seitz and Charles R. Dyer · 1999
Earlier work this paper cites.
Surface light fields for 3D photography
Daniel Wood, Daniel Azuma, Wyvern Aldinger, Brian Curless, Tom Duchamp, David Salesin, and Werner Stuetzle · 2000
Earlier work this paper cites.
Unstructured lumigraph rendering
Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen · 2001
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Perfect spatial hashing
Sylvain Lefebvre and Hugues Hoppe · 2006
Earlier work this paper cites.
GigaVoxels: Ray-guided streaming for efficient and detailed voxel rendering
Cyril Crassin, Fabrice Neyret, Sylvain Lefebvre, and Elmar Eisemann · 2009
Earlier work this paper cites.
Efficient sparse voxel octrees
Samuli Laine and Tero Karras · 2010
Earlier work this paper cites.
Performance per what?
Tomas Akenine-Möller and Björn Johnsson · 2012
Earlier work this paper cites.
Unstructured light fields
Abe Davis, Marc Levoy, and Fredo Durand · 2012
Cited alongside, same era.
Real-time 3D reconstruction at scale using voxel hashing
Matthias Nießner, Michael Zollhofer, Shahram Izadi, and Marc Stamminger · 2013
Cited alongside, same era.
Let there be color! Large-scale texturing of 3D reconstructions
Michael Goesele Michael Waechter, Nils Moehrle · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Soft 3D reconstruction for view synthesis
Eric Penner and Li Zhang · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
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
Later among the works it cites.
JaxNeRF: an efficient JAX implementation of NeRF, 2020
Boyang Deng, Jonathan T. Barron, and Pratul P. Srinivasan · 2020
Later among the works it cites.
Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
Later among the works it cites.
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
Later among the works it cites.
Deformable neural radiance fields
Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualla · 2020
Later among the works it cites.
GRAF: Generative radiance fields for 3D-aware image synthesis
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep blending for free-viewpoint image-based rendering
Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Stereo magnification: Learning view synthesis using multiplane images
Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Neural volumes: Learning dynamic renderable volumes from images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 2019
Cited alongside, same era.
Local light field fusion: Practical view synthesis with prescriptive sampling guidelines
Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima K. Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar · 2019
Cited alongside, same era.
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
Later among the works it cites.
Lighthouse: Predicting lighting volumes for spatially-coherent illumination
Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, and Noah Snavely · 2020
Later among the works it cites.
Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
Later among the works it cites.
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
Closest in time.
Dynamic neural radiance fields for monocular 4D facial avatar reconstruction
Guy Gafni, Justus Thies, Michael Zollhöfer, and Matthias Nießner · 2021
Closest in time.
Neural scene flow fields for space-time view synthesis of dynamic scenes
Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang · 2021
Closest in time.
Autoint: Automatic integration for fast neural rendering
David B. Lindell, Julien N.P. Martel, and Gordon Wetzstein · 2021
Closest in time.
Neural scene graphs for dynamic scenes
Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide · 2021
Closest in time.
DeRF: Decomposed radiance fields
Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi · 2021
Closest in time.
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
Closest in time.
IBRNet: Learning multi-view image-based rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P. Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser · 2021
Closest in time.