Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene representation has taken the field of Computer Vision by storm. As a novel view synthesis and 3D reconstruction method, NeRF models find applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Since the original paper by Mildenhall et al., more than 250 preprints were published, with more than 100 eventually being accepted in tier one Computer Vision Conferences. Given NeRF popularity and the current interest in this research area, we believe it necessary to compile a comprehensive survey of NeRF papers from the past two years, which we organized into both architecture, and application based taxonomies. We also provide an introduction to the theory of NeRF based novel view synthesis, and a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section.

翻译：神经辐射场（NeRF）作为一种基于隐式场景表示的新颖视图合成技术，已在计算机视觉领域掀起热潮。作为一种新颖视图合成与三维重建方法，NeRF模型被应用于机器人技术、城市场景建模、自主导航、虚拟现实/增强现实等多个领域。自Mildenhall等人提出原始论文以来，已有超过250篇预印本发表，其中100余篇最终被计算机视觉顶级会议收录。鉴于NeRF的广泛影响力及该研究方向当前的热度，我们认为有必要对过去两年间的NeRF相关论文进行全面综述，并按照架构与应用两种分类体系进行组织。本文还介绍了基于NeRF的新颖视图合成理论基础，并对关键NeRF模型的性能与速度进行了基准比较。通过本综述，我们期望为新手研究者提供NeRF入门指引，为该领域的重要工作提供实用参考，并通过讨论章节激励未来研究方向。