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Volume rendering using neural fields has shown great promise in capturing and synthesizing novel views of 3D scenes.
Surfels: Surface elements as rendering primitives
Hanspeter Pfister, Matthias Zwicker, Jeroen Van Baar, and Markus Gross · 2000
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 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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Autoint: Automatic integration for fast neural volume rendering
Julien NP Martel David B Lindell and Gordon Wetzstein · 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
Cited alongside, same era.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Cited alongside, same era.
Nerf in detail: Learning to sample for view synthesis
Relja Arandjelović and Andrew Zisserman · 2021
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Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su · 2021
Cited alongside, same era.
Depth-supervised nerf: Fewer views and faster training for free
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
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DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, and Markus Steinberger · 2021
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Derf: Decomposed radiance fields
Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi · 2021
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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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Mlp-mixer: An all-mlp architecture for vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, et al · 2021
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Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan · 2021
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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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How to represent part-whole hierarchies in a neural network
Geoffrey Hinton · 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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pixelnerf: Neural radiance fields from one or few images
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