PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields

Understand

Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis.

• However, it remains underexplored how the appearance of such representations can be efficiently edited while maintaining photorealism.
• In this work, we present PaletteNeRF, a novel method for photorealistic appearance editing of neural radiance fields (NeRF) based on 3D color decomposition.
• Our method decomposes the appearance of each 3D point into a linear combination of palette-based bases (i.e., 3D segmentations defined by a group of NeRF-type functions) that are shared across the scene.