  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.
