[论文解读] NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)

Kyle Gao, Yina Gao+4 more|ArXiv.org|Oct 1, 2022

Robotics and Sensor-Based Localization被引用 105

一句话总结

所提供的文本是关于 IEEEtran LaTeX 模板和投稿实践的指南，而非实际的 NeRF 评审论文。

ABSTRACT

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.

研究动机与目标

• 解释 IEEEtran LaTeX 模板的目的与范围，以及它们在最终印刷与 IEEE Xplore 提交之间的局限性。
• 提供逐步指南，帮助在 IEEE 文章中创建常见的前置材料、章节和后置材料。
• 提供实用指南、示例和最终检查清单，以确保提交合规。

提出的方法

• 描述不同 IEEE 出版类型（期刊、会议、comp soc 等）的 documentclass 选项。
• 展示如何排布前言材料（题名、作者、摘要、关键词）和 running headers。
• 提供图、表、方程、引用、列表和传记注记的模板与编码示例。
• 提供关于 LaTeX 包使用以及提交过程中应避免的常见陷阱的指导。
• 包含最终检查清单，以确保编号、交叉引用和格式正确。

实验结果

研究问题

• RQ1 对于各种 IEEE 出版类型，推荐的 documentclass 选项是什么？
• RQ2 作者在 IEEE 提交中应如何构建前言材料和 running heads？
• RQ3 基于 IEEEtran 的手稿中，图、表、方程和引用的最佳实践是什么？
• RQ4 作者在使用 LaTeX 进行 IEEE 提交时应避免哪些常见错误？

主要发现

• 提供对 IEEEtran 功能和命令的全面清单，涵盖常见出版要素。
• 概述 IEEE 风格下前言材料、章节、图、表、方程和参考文献的具体示例。
• 提供实用的检查清单，以最小化排版错误并确保与 IEEE 提交工作流程的兼容性。
• 强调草拟模板与最终 IEEE Xplore 就绪输出之间的区别，并参考 IEEE 模板选择器。
• 推荐用于获取模板和支持的软件发行版与在线资源（TUG、TeX Live）。