系统梳理神经辐射场编辑方法，揭示文本生成与新表示的融合潜力。
Editing Implicit and Explicit Representations of Radiance Fields: A Survey
- 按编辑策略分类，构建统一方法论框架
- 涵盖从基础修改到跨模态编辑的多种技术路径
- 适合关注3D内容生成与交互的研究者
神经辐射场（NeRF）近年来通过体积化表示实现了高质量的新视角合成。然而，辐射场编辑方法的发展滞后于其他方面改进。随着受NeRF启发的新型辐射场表示及文生图模型的普及，辐射场编辑迎来了新机遇。本文全面综述了现有NeRF及其他类似辐射场表示的编辑方法，提出基于编辑策略的新分类体系，回顾开创性模型，探讨当前及潜在应用场景，并对前沿方法在编辑能力与性能上进行比较。
原文摘要 · Abstract (English)
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.
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