@article{Gao2026, 
author = {Kyle Gao and Yina Gao and Hongjie He and Dening Lu and Linlin Xu and Jonathan Li},
title = {Neural radiance fields in 3D vision: A comprehensive review},
year = {2026},
journal = {Computational Visual Media},
volume = {12},
number = {4},
pages = {851-905},
keywords = {neural radiance fields (NeRFs), novel view synthesis, volume rendering, Gaussian splatting},
url = {https://www.sciopen.com/article/10.26599/CVM.2026.9450535},
doi = {10.26599/CVM.2026.9450535},
abstract = {Presented in March 2020, neural radiance fields (NeRFs) have 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 and augmented reality, and more. In August 2023, Gaussian splatting, a direct competitor to NeRF-based volume rendering frameworks, was proposed. Gaussian splatting has gained tremendous momentum, overtaking NeRF-based methods 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 NeRFs and neural field rendering dominated the field of novel view synthesis and applications thereof. We also include works from the post-Gaussian splatting era, where NeRFs and implicit/hybrid neural fields have found niche applications.    Our survey is organized into architecture and application-based taxonomies in the pre-Gaussian splatting era, as well as providing a categorization of active research areas for NeRFs, neural fields, and implicit/hybrid neural representation methods. In the post-Gaussian splatting era, we focus on relevant developments and applications. We provide an introduction to the theory of NeRFs and their training via differentiable volume rendering. We also present a benchmark comparison of the performance and speed of classical NeRFs, implicit and hybrid neural representations, and neural field models, as well as an overview of key datasets.}
}