Neural Radiance Fields or NeRFs have become the representation of choice for problems in view synthesis or image-based rendering, as well as in many other applications across computer graphics and vision, and beyond. At their core, NeRFs describe a new representation of 3D scenes or 3D geometry. Instead of meshes, disparity maps, multiplane images or even voxel grids, they represent the scene as a continuous volume, with volumetric parameters like view-dependent radiance and volume density obtained by querying a neural network. The NeRF representation has now been widely used, with thousands of papers extending or building on it every year, multiple authors and websites providing overviews and surveys, and numerous industrial applications and startup companies. In this article, we briefly review the NeRF representation, and describe the three decades-long quest to find the best 3D representation for view synthesis and related problems, culminating in the NeRF papers. We then describe new developments in terms of NeRF representations and make some observations and insights regarding the future of 3D representations.

翻译：神经辐射场（Neural Radiance Fields，简称NeRFs）已成为视角合成或基于图像的渲染问题中的首选表示方法，并在计算机图形学、计算机视觉及其他众多应用领域中得到广泛使用。其核心思想在于描述了一种全新的3D场景或3D几何表示方式。与网格、视差图、多平面图像乃至体素网格不同，NeRF将场景表示为连续体，通过查询神经网络获得诸如视角相关辐射度和体密度等体积参数。目前，NeRF表示已被广泛采用，每年有数千篇论文对其进行扩展或基于此开展研究，多位作者及网站提供了相关综述与概述，并催生了众多工业应用及初创公司。本文简要回顾了NeRF表示方法，描述了过去三十年来为寻找视角合成及相关问题的最佳3D表示所做的探索，其最终成果汇集于NeRF论文中。随后，我们介绍了NeRF表示方面的最新进展，并就3D表示的未来提出了一些观察与见解。