APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 1

Deep Review and Analysis of Recent NeRFs

Fang Zhu, State Key Laboratory of Mobile Network and Mobile Multimedia Technology and Sanechips Technology Co., LTD., China, zhu.fang@sanechips.com.cn , Shuai Guo, Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, China, Li Song, Institute of Image Communication and Network Engineering and Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, China, Ke Xu, State Key Laboratory of Mobile Network and Mobile Multimedia Technology and Sanechips Technology Co., LTD., China, Jiayu Hu, University of California, Los Angeles, USA
 
Suggested Citation
Fang Zhu, Shuai Guo, Li Song, Ke Xu and Jiayu Hu (2023), "Deep Review and Analysis of Recent NeRFs", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 1, e6. http://dx.doi.org/10.1561/116.00000162

Publication Date: 07 Mar 2023
© 2023 F. Zhu, S. Guo, L. Song, K. Xu and J. Hu
 
Subjects
 
Keywords
NeRFreviewvolumetric renderingfactorizable embeddingfuture innovations
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
Neural Radiance Fields 
Volumetric Rendering 
Novel View Synthesis 
Factorizable Embedded Space 
Multi-view Consistent 
Weighted Importance Sampling 
Application Innovations of NeRFs 
Future 
Conclusion 
References 

Abstract

Neural radiance fields (NeRFs) refer to a suit of deep neural networks that are used to learn and represent objects or scenes. Generally speaking, NeRFs have five main characters: volumetric rendering, novel view synthesis, factorizable embedded space, multi-view consistency and weighted importance sampling. Recently, NeRFs have drawn great attention and are now important cornerstones of metaverse and augmented reality research, as {is} their stronger efficiency and more imaginative rendering performance. There have been many reviews of NeRFs, most of them focus on different applications of NeRFs. In this paper, we provide a deep review and analysis of recent NeRF related works, according to the main characters of NeRFs they make further progress in. Then we introduce some new application innovations of NeRFs, and illustrate future opportunities of them. We hope this paper can provide an insightful organization of current developments in NeRFs, identify their limitations, and give suggestions for further research.

DOI:10.1561/116.00000162