Editing Implicit and Explicit Representations of Radiance Fields: A Survey

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.