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

Learning Naturally Aggregated Appearance for Efficient 3D Editing