Neural rendering combines ideas from classical computer graphics and machine learning to synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is a recent innovation that uses AI algorithms to create 3D objects from 2D images. By leveraging an interpolation approach, NeRF can produce new 3D reconstructed views of complicated scenes. Rather than directly restoring the whole 3D scene geometry, NeRF generates a volumetric representation called a ``radiance field,'' which is capable of creating color and density for every point within the relevant 3D space. The broad appeal and notoriety of NeRF make it imperative to examine the existing research on the topic comprehensively. While previous surveys on 3D rendering have primarily focused on traditional computer vision-based or deep learning-based approaches, only a handful of them discuss the potential of NeRF. However, such surveys have predominantly focused on NeRF's early contributions and have not explored its full potential. NeRF is a relatively new technique continuously being investigated for its capabilities and limitations. This survey reviews recent advances in NeRF and categorizes them according to their architectural designs, especially in the field of novel view synthesis.

翻译：神经渲染结合了经典计算机图形学与机器学习的理念，通过真实世界观测合成图像。NeRF（神经辐射场）是一项近期创新，它利用人工智能算法从二维图像中创建三维物体。通过采用插值方法，NeRF能够生成复杂场景的新三维重建视角。该方法不直接恢复整个三维场景几何结构，而是生成一种称为"辐射场"的体积表示，能够为相关三维空间内的每个点创建颜色与密度。鉴于NeRF广泛的吸引力和知名度，全面审视该领域的现有研究显得尤为重要。尽管以往关于三维渲染的综述主要聚焦于传统计算机视觉方法或深度学习技术，仅有少数涉及NeRF的潜力，但这些综述大多集中于NeRF早期贡献，未能充分探索其全部潜能。作为一项相对新兴的技术，NeRF的功能与局限性仍在持续研究中。本综述回顾了NeRF的最新进展，并依据其架构设计进行分类，尤其关注新视角合成领域的研究。