  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.
