This AI Paper Presents PaletteNeRF, a Novel Method for Photorealistic Appearance Editing of Neural Radiance Fields (NeRF) Based on 3D Color Decomposition

The capacity of Neural Radiance Fields (NeRF) and its derivatives to accurately recreate real-world 3D scenes from 2D photos and allow high-quality, photorealistic new view synthesis has garnered more and more interest in recent years. However, as scene appearance is implicitly recorded in neural characteristics and network weights that do not permit local manipulation or intuitive alteration, such volumetric representations are difficult to modify. Several methods have supported the editing of NeRF. One group of techniques recovers the scene’s material qualities so that they can be altered, such as surface roughness, or rendered again in new lighting circumstances.

Such techniques depend on a precise assessment of the scene reflectance, which is frequently difficult for complicated real-world images taken in an open environment. Another class of methods involves discovering a latent code that NeRF may be trained to use to achieve the desired look. These techniques do not, however, offer fine-grained editing and frequently have limited capacity and flexibility. Additionally, while some other methods can adapt NeRF’s look to fit a certain kind of image, they occasionally fall short of preserving the same amount of photorealism in the original scene. They suggest PaletteNeRF in this work as an innovative way to facilitate flexible and simple editing of NeRF.

Their approach is influenced by earlier techniques for image editing that employed color palettes, which use a condensed selection of hues to represent the complete spectrum of shades in a picture. They combine specular and diffuse components to describe each point’s brightness, and they further divide the diffuse component into a linear combination of common view-independent color bases. To reduce the disparity between the produced pictures and the ground truth images, they jointly optimize the per-point specular component, the global color bases, and the per-point linear weights during training.

To promote the sparseness and spatial coherence of the decomposition and create a more meaningful grouping, they also apply unique regularizers on the weights. By freely altering the taught color bases, students may intuitively adjust NeRF’s look with the suggested framework (Fig. 1). Additionally, they demonstrate how their system may be used in conjunction with semantic features to provide semantic editing. Their technique offers more globally coherent and 3D consistent recoloring outputs of the scene across arbitrary viewpoints than earlier palette-based picture or video editing techniques. They show that their approach outperforms baseline approaches numerically and subjectively, allowing for more precise local color modification while faithfully keeping the photorealism of the 3D scene.

In summary,

• They offer a unique framework to make altering NeRF easier by breaking down the radiance field into a weighted mixture of learned color bases.

• To produce intuitive decompositions, they devised a reliable optimization technique using unique regularizers.

• Their method allows for realistic palette-based appearance customization, allowing even inexperienced users to engage with NeRF in a straightforward and manageable way on common hardware.

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