An overview of Neural Radiance Fields

Xiaoju Liu

doi:10.54254/2755-2721/45/20241016

Research Article

Open access

Published on 15 March 2024

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Liu,X. (2024). An overview of Neural Radiance Fields. Applied and Computational Engineering,45,1-6.

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An overview of Neural Radiance Fields

Xiaoju Liu *^,1,

¹ School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611730, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/45/20241016

Abstract

Synthesizing controllable, photo-realistic images and videos is one of the fundamental goals of computer graphics. Neural rendering is a rapidly emerging field in image synthesis that allows a compact representation of scenes, and by utilizing neural networks, rendering can be learned from existing observations. Neural Radiance Fields (NeRF) implement an effective combination of Neural Fields and the graphics component Volume rendering. It achieves the first photo-level view synthesis effect using an implicit representation. Unlike previous approaches, NeRF chooses Volume as an intermediate representation to reconstruct an implicit Volume. Although the advantages of NeRF are apparent, there are many drawbacks in the original version of NeRF: it is slow to train and render, requires a large number of perspectives, can only represent static scenes, and the trained NeRF representation does not generalize to other scenes. This report focuses on optimizing the shortcomings mentioned above of NeRF by scholars in the last three years and analyzes the solutions to the problems of NeRF from several perspectives.

Keywords

Neural Rendering, Neural Radiance Fields, Deep Learning, 3D Reconstruction

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Cite this article

Liu,X. (2024). An overview of Neural Radiance Fields. Applied and Computational Engineering,45,1-6.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Conference website: https://www.confspml.org/

ISBN：978-1-83558-331-9(Print) / 978-1-83558-332-6(Online)

Conference date: 15 January 2024

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.45

ISSN：2755-2721(Print) / 2755-273X(Online)

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