编辑辐射场的隐式与显式表示：一项综述
Editing Implicit and Explicit Representations of Radiance Fields: A Survey
AI总结：
本文全面综述了NeRF及类似辐射场表示的编辑方法，提出基于编辑方法的新分类法，回顾开创性模型，并在编辑选项与性能上比较了前沿方法。
摘要：
近年来，Neural Radiance Fields (NeRF) 通过提供一种紧凑且能实现高质量图像渲染的新型体积表示，彻底改变了新视图合成。然而，针对这些辐射场的编辑方法的发展速度，慢于 NeRF 其他诸多方面的改进。随着近期受 NeRF 启发的替代性辐射场表示的发展，以及 text-to-image 模型在全球范围内的普及，为辐射场编辑提供了许多新机遇与策略。本文对文献中关于 NeRF 及其他类似辐射场表示的不同编辑方法进行了全面综述。我们提出了一种新的分类法，根据编辑方法对现有工作进行分类，回顾了开创性模型，反思了辐射场编辑的当前及潜在新应用，并在编辑选项和性能方面比较了 state-of-the-art 方法。
英文摘要：
Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering. However, the methods to edit those radiance fields developed slower than the many improvements to other aspects of NeRF. With the recent development of alternative radiance field-based representations inspired by NeRF as well as the worldwide rise in popularity of text-to-image models, many new opportunities and strategies have emerged to provide radiance field editing. In this paper, we deliver a comprehensive survey of the different editing methods present in the literature for NeRF and other similar radiance field representations. We propose a new taxonomy for classifying existing works based on their editing methodologies, review pioneering models, reflect on current and potential new applications of radiance field editing, and compare state-of-the-art approaches in terms of editing options and performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
