LAENeRF：面向神经辐射场的局部外观编辑
LAENeRF: Local Appearance Editing for Neural Radiance Fields
AI总结：
针对NeRF外观与几何纠缠导致的局部编辑难、内存大、交互差等问题，提出LAENeRF统一框架，基于体素网格区域选择与射线终止点颜色映射，实现低内存快速的照片级与非照片级局部外观编辑。
摘要：
由于神经辐射场（NeRFs）的普遍存在，近年来对可编辑隐式3D表示的兴趣激增。然而，由于模型参数中编码的外观与几何纠缠不清，编辑NeRFs所使用的隐式或混合表示十分困难。尽管存在这些挑战，近期研究已在照片级真实感与非照片级真实感外观编辑方面迈出了有前景的第一步。相关工作主要的未决问题包括交互性有限、缺乏对局部编辑的支持以及内存需求巨大，使其在实际中实用性较差。我们通过LAENeRF解决了这些局限性，这是一个用于NeRFs照片级真实感与非照片级真实感外观编辑的统一框架。为解决局部编辑问题，我们利用体素网格作为区域选择的起点。我们学习从预期射线终止点到最终输出颜色的映射，该映射可选择由风格损失监督，从而构建出一个能对选定区域执行照片级真实感与非照片级真实感外观编辑的框架。由于映射仅依赖每条射线上的单点，我们限制了内存需求并实现了快速优化。为保证交互性，我们使用一组学习到的、可修改的基础颜色，通过加法图层混合来合成输出颜色。与同期工作相比，LAENeRF在保持较低处理时间的同时实现了重新着色和风格化。此外，我们证明我们的方法在定量和定性上均超越了基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
