In March 2020, Neural Radiance Field (NeRF) revolutionized Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. In August 2023, Gaussian Splatting, a direct competitor to the NeRF-based framework, was proposed, gaining tremendous momentum and overtaking NeRF-based research in terms of interest as the dominant framework for novel view synthesis. We present a comprehensive survey of NeRF papers from the past five years (2020-2025). These include papers from the pre-Gaussian Splatting era, where NeRF dominated the field for novel view synthesis and 3D implicit and hybrid representation neural field learning. We also include works from the post-Gaussian Splatting era where NeRF and implicit/hybrid neural fields found more niche applications. Our survey is organized into architecture and application-based taxonomies in the pre-Gaussian Splatting era, as well as a categorization of active research areas for NeRF, neural field, and implicit/hybrid neural representation methods. We provide an introduction to the theory of NeRF and its training via differentiable volume rendering. We also present a benchmark comparison of the performance and speed of classical NeRF, implicit and hybrid neural representation, and neural field models, and an overview of key datasets.

翻译：2020年3月，神经辐射场（NeRF）彻底改变了计算机视觉领域，实现了基于神经网络的隐式场景表示与新视角合成。NeRF模型已在机器人学、城市测绘、自主导航、虚拟现实/增强现实等领域获得广泛应用。2023年8月，作为NeRF框架直接竞争者的高斯溅射技术被提出，该技术迅速获得巨大发展势头，在新视角合成领域取代NeRF成为主导框架。本文系统综述了过去五年（2020-2025年）的NeRF相关论文，涵盖高斯溅射前时代——彼时NeRF主导着新视角合成及三维隐式/混合表示神经场学习领域，同时包含高斯溅射后时代——该时期NeRF及隐式/混合神经场在更多细分领域找到应用场景。本综述按以下维度组织：高斯溅射前时代的架构与应用分类体系，以及NeRF、神经场与隐式/混合神经表示方法的活跃研究方向归类。我们阐述了NeRF的理论基础及其通过可微分体渲染的训练机制，并对经典NeRF、隐式/混合神经表示及神经场模型的性能与速度进行了基准比较，最后概述了关键数据集。