PaletteNeRF: Palette-based Color Editing for NeRFs
This addresses the problem of color editing in 3D scene representations for users of NeRF technology, though it is an incremental extension of existing methods.
The paper tackles the limited editing ability of Neural Radiance Fields (NeRFs) by proposing PaletteNeRF, a method that enables efficient color editing on NeRF-represented scenes, achieving view-consistent and artifact-free results.
Neural Radiance Field (NeRF) is a powerful tool to faithfully generate novel views for scenes with only sparse captured images. Despite its strong capability for representing 3D scenes and their appearance, its editing ability is very limited. In this paper, we propose a simple but effective extension of vanilla NeRF, named PaletteNeRF, to enable efficient color editing on NeRF-represented scenes. Motivated by recent palette-based image decomposition works, we approximate each pixel color as a sum of palette colors modulated by additive weights. Instead of predicting pixel colors as in vanilla NeRFs, our method predicts additive weights. The underlying NeRF backbone could also be replaced with more recent NeRF models such as KiloNeRF to achieve real-time editing. Experimental results demonstrate that our method achieves efficient, view-consistent, and artifact-free color editing on a wide range of NeRF-represented scenes.