Due to the omnipresence of Neural Radiance Fields (NeRFs), the interest
towards editable implicit 3D representations has surged over the last years.
However, editing implicit or hybrid representations as used for NeRFs is
difficult due to the entanglement of appearance and geometry encoded in the
model parameters. Despite these challenges, recent research has shown first
promising steps towards photorealistic and non-photorealistic appearance edits.
The main open issues of related work include limited interactivity, a lack of
support for local edits and large memory requirements, rendering them less
useful in practice. We address these limitations with LAENeRF, a unified
framework for photorealistic and non-photorealistic appearance editing of
NeRFs. To tackle local editing, we leverage a voxel grid as starting point for
region selection. We learn a mapping from expected ray terminations to final
output color, which can optionally be supervised by a style loss, resulting in
a framework which can perform photorealistic and non-photorealistic appearance
editing of selected regions. Relying on a single point per ray for our mapping,
we limit memory requirements and enable fast optimization. To guarantee
interactivity, we compose the output color using a set of learned, modifiable
base colors, composed with additive layer mixing. Compared to concurrent work,
LAENeRF enables recoloring and stylization while keeping processing time low.
Furthermore, we demonstrate that our approach surpasses baseline methods both
quantitatively and qualitatively.

用于编辑 NeRFs 的 LAENeRF 是一个统一框架，以实现对 NeRFs 的逼真和非逼真的外观编辑，采用区域选择的体素网格作为局部编辑的起点，并通过学习从射线末端到最终输出颜色的映射来限制内存需求和实现快速优化，最终实现了颜色重新着色和样式化，并在定量和定性方面超越了基线方法。