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/.

我们提出了一种名为AGAP的新方法，通过使用显式的2D外观聚合（也称为规范图像），用户可以轻松地自定义他们的三维编辑。借助我们的方法，可以实现无需为每种情况重新优化的各种三维编辑方式（例如样式化、交互式绘画和内容提取），展示了其泛化性和高效性。

学习自然聚合的外观以提高高效3D编辑