论文标题
PALETTENERF：基于调色板的NERFS的颜色编辑
PaletteNeRF: Palette-based Color Editing for NeRFs
论文作者
论文摘要
神经辐射场（NERF）是一种强大的工具，可以忠实地为只有稀疏捕获图像的场景创造新颖的景观。尽管具有代表3D场景及其外观的强大能力，但其编辑能力仍非常有限。在本文中，我们提出了一个名为Palettenerf的Vanilla Nerf的简单但有效的扩展，以在NERF代表的场景上进行有效的颜色编辑。由最近基于调色板的图像分解作用的动机，我们将每个像素颜色近似为通过添加剂重量调制的调色板颜色的总和。我们的方法没有像香草nerf中那样预测像素颜色，而是预测加法权重。基础的NERF主链也可以用最近的NERF模型（例如KilOnerf）取代，以实现实时编辑。实验结果表明，我们的方法可以在各种NERF代表的场景上实现高效，一致和无伪影的颜色编辑。
Neural Radiance Field (NeRF) is a powerful tool to faithfully generate novel views for scenes with only sparse captured images. Despite its strong capability for representing 3D scenes and their appearance, its editing ability is very limited. In this paper, we propose a simple but effective extension of vanilla NeRF, named PaletteNeRF, to enable efficient color editing on NeRF-represented scenes. Motivated by recent palette-based image decomposition works, we approximate each pixel color as a sum of palette colors modulated by additive weights. Instead of predicting pixel colors as in vanilla NeRFs, our method predicts additive weights. The underlying NeRF backbone could also be replaced with more recent NeRF models such as KiloNeRF to achieve real-time editing. Experimental results demonstrate that our method achieves efficient, view-consistent, and artifact-free color editing on a wide range of NeRF-represented scenes.
