In recent years, Neural Radiance Fields (NeRF) has made remarkable progress in the field of computer vision and graphics, providing strong technical support for solving key tasks including 3D scene understanding, new perspective synthesis, human body reconstruction, robotics, and so on, the attention of academics to this research result is growing. As a revolutionary neural implicit field representation, NeRF has caused a continuous research boom in the academic community. Therefore, the purpose of this review is to provide an in-depth analysis of the research literature on NeRF within the past two years, to provide a comprehensive academic perspective for budding researchers. In this paper, the core architecture of NeRF is first elaborated in detail, followed by a discussion of various improvement strategies for NeRF, and case studies of NeRF in diverse application scenarios, demonstrating its practical utility in different domains. In terms of datasets and evaluation metrics, This paper details the key resources needed for NeRF model training. Finally, this paper provides a prospective discussion on the future development trends and potential challenges of NeRF, aiming to provide research inspiration for researchers in the field and to promote the further development of related technologies.

近年来，神经辐射场（NeRF）在计算机视觉和图形领域取得了显着进展，为解决3D场景理解、新视角合成、人体重建、机器人等关键任务提供了强有力的技术支持。本文对过去两年内关于NeRF的研究文献进行了深入分析，在详细阐述NeRF核心架构的基础上，讨论了NeRF的各种改进策略，以及在不同应用场景中的案例研究，展示了其在不同领域的实际应用价值。同时，本文还详细介绍了NeRF模型训练所需的关键资源，最后提出了关于NeRF未来发展趋势和潜在挑战的前瞻性讨论，旨在为该领域的研究人员提供研究灵感，促进相关技术的进一步发展。

基于神经辐射场的视觉渲染方法综述