The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 500 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.

翻译：在三维环境与场景的建模及解释等多个方面，人类对推进三维计算机视觉、计算机图形学与机器学习的研究始终抱有浓厚兴趣。Mildenhall等人在其关于神经辐射场（Neural Radiance Fields, NeRFs）的论文中进行的探索，引发了计算机图形学、机器人学、计算机视觉领域的蓬勃发展，且高分辨率、低存储的增强现实与虚拟现实三维模型的潜在应用前景备受关注，已有超过500篇与NeRFs相关的预印本论文发表。本文旨在为初涉这些领域的研究者搭建桥梁，从数学、几何学、计算机视觉与计算机图形学的基础知识出发，逐步深入到隐式表示（Implicit Representations）这一多学科交叉领域所面临的挑战。本综述梳理了渲染、隐式学习及NeRFs的发展历程，总结了NeRFs研究的主要进展及其在当今世界的潜在应用与影响。为此，本综述将所有NeRF相关研究按照所用数据集、目标函数、解决的应用问题及相应评估标准进行了分类。