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.
@article{arxiv.2412.17628,
title = {Editing Implicit and Explicit Representations of Radiance Fields: A Survey},
author = {Arthur Hubert and Gamal Elghazaly and Raphael Frank},
journal= {arXiv preprint arXiv:2412.17628},
year = {2024}
}