Editing Implicit and Explicit Representations of Radiance Fields: A Survey
It addresses the problem of editing radiance fields for researchers and practitioners in computer vision and graphics, but it is incremental as it surveys existing work rather than introducing new methods.
This survey paper reviews methods for editing neural radiance fields (NeRF) and similar representations, which are used for novel view synthesis, by proposing a taxonomy, comparing state-of-the-art approaches, and discussing applications.
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.