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.

近年来，神经辐射场（NeRF）在计算机视觉和图形领域取得了显着进展，为解决 3D 场景理解、新视角合成、人体重建、机器人等关键任务提供了强有力的技术支持。本文对过去两年内关于 NeRF 的研究文献进行了深入分析，在详细阐述 NeRF 核心架构的基础上，讨论了 NeRF 的各种改进策略，以及在不同应用场景中的案例研究，展示了其在不同领域的实际应用价值。同时，本文还详细介绍了 NeRF 模型训练所需的关键资源，最后提出了关于 NeRF 未来发展趋势和潜在挑战的前瞻性讨论，旨在为该领域的研究人员提供研究灵感，促进相关技术的进一步发展。

基于神经辐射场的视觉渲染方法综述

Neural Radiance Field-based Visual Rendering: A Comprehensive Review

We introduce anchored radial observations (ARO), a novel shape encoding for
learning implicit field representation of 3D shapes that is category-agnostic
and generalizable amid significant shape variations. The main idea behind our
work is to reason about shapes through partial observations from a set of
viewpoints, called anchors. We develop a general and unified shape
representation by employing a fixed set of anchors, via Fibonacci sampling, and
designing a coordinate-based deep neural network to predict the occupancy value
of a query point in space. Differently from prior neural implicit models that
use global shape feature, our shape encoder operates on contextual,
query-specific features. To predict point occupancy, locally observed shape
information from the perspective of the anchors surrounding the input query
point are encoded and aggregated through an attention module, before implicit
decoding is performed. We demonstrate the quality and generality of our
network, coined ARO-Net, on surface reconstruction from sparse point clouds,
with tests on novel and unseen object categories, "one-shape" training, and
comparisons to state-of-the-art neural and classical methods for reconstruction
and tessellation.

本文提出了一种基于锚定径向观察 (ARO) 的新型形状编码，通过固定集锚定视点和利用基于注意力机制的深度神经网络来预测三维空间中查询点的包含值，实现了对三维形状的学习和隐式表示。模型在短点云的表面重建和各种物体的比较测试中表现出了可靠性和泛化性，是当前重建和三角剖分领域的最优方法之一。