Editing Implicit and Explicit Representations of Radiance Fields: A Survey

TL;DR

This survey reviews recent advances in editing neural radiance fields, categorizing methods, and discussing new applications, highlighting the gap between NeRF development and editing techniques.

Contribution

It provides a comprehensive taxonomy and comparison of existing radiance field editing methods, and discusses future directions and applications.

Findings

Classified editing methods into a new taxonomy.

Compared state-of-the-art approaches in editing options and performance.

Identified opportunities for future research and applications.

Abstract

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.