Neural radiance fields, which represent a 3D scene as a color field and a density field, have demonstrated great progress in novel view synthesis yet are unfavorable for editing due to the implicitness. In view of such a deficiency, we propose to replace the color field with an explicit 2D appearance aggregation, also called canonical image, with which users can easily customize their 3D editing via 2D image processing. To avoid the distortion effect and facilitate convenient editing, we complement the canonical image with a projection field that maps 3D points onto 2D pixels for texture lookup. This field is carefully initialized with a pseudo canonical camera model and optimized with offset regularity to ensure naturalness of the aggregated appearance. Extensive experimental results on three datasets suggest that our representation, dubbed AGAP, well supports various ways of 3D editing (e.g., stylization, interactive drawing, and content extraction) with no need of re-optimization for each case, demonstrating its generalizability and efficiency. Project page is available at https://felixcheng97.github.io/AGAP/.

翻译：神经辐射场将3D场景表示为颜色场和密度场，在新视角合成方面取得了显著进展，但由于其隐式特性，不便于编辑。针对这一缺陷，我们提出用显式的2D外观聚合（亦称规范图像）替代颜色场，使用户能够通过2D图像处理轻松定制3D编辑。为避免失真效应并促进便捷编辑，我们为该规范图像补充了一个投影场，用于将3D点映射到2D像素以进行纹理查询。该场通过伪规范相机模型精心初始化，并利用偏移正则化进行优化，以确保聚合外观的自然性。在三个数据集上的大量实验结果表明，我们的表示方法（命名为AGAP）能够有效支持多种3D编辑方式（如风格化、交互式绘制和内容提取），且无需针对每种情况进行重新优化，展现了其泛化能力和高效性。项目页面详见https://felixcheng97.github.io/AGAP/。