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.

翻译：由于神经辐射场（NeRF）的广泛应用，近年来对可编辑隐式三维表示的兴趣急剧增加。然而，由于模型参数中外观与几何信息的纠缠，编辑NeRF中使用的隐式或混合表示十分困难。尽管存在这些挑战，近期研究已在真实感与非真实感外观编辑方面迈出了初步且具有前景的步伐。现有工作的主要问题包括交互性有限、缺乏对局部编辑的支持以及高内存需求，这使其在实际应用中实用性不足。我们通过LAENeRF解决了这些局限性——这是一种用于NeRF真实感与非真实感外观编辑的统一框架。为实现局部编辑，我们利用体素网格作为区域选择的起点。我们学习从期望光线终止点到最终输出颜色的映射，该映射可选地受风格损失监督，从而形成能够对选定区域进行真实感与非真实感外观编辑的框架。通过为每条光线仅依赖单个点进行映射，我们限制了内存需求并实现了快速优化。为保证交互性，我们使用一组可学习的、可修改的基础颜色，并通过加法层混合来合成输出颜色。与现有工作相比，LAENeRF在保持低处理时间的同时实现了重新着色和风格化。此外，我们证明该方法在定量和定性评估上均超越了基线方法。