Résumé
Neural Radiance Fields (NeRFs) have revolutionized novel view synthesis for captured scenes, with recent methods allowing interactive free-viewpoint navigation and fast training for scene reconstruction. However, the implicit representations used by these methods — often including neural networks and complex encodings— make them difficult to edit. Some initial methods have been proposed, but they suffer from limited editing capabilities and/or from a lack of interactivity, and are thus unsuitable for interactive editing of captured scenes. We tackle both limitations and introduce NeRFshop, a novel end-to-end method that allows users to interactively select and deform objects through cage-based transformations. NeRFshop provides fine scribblebaseduser control for the selection of regions or objects to edit, semi-automatic cage creation, and interactive volumetric manipulation of scene content thanks to our GPU-friendly two-level interpolation scheme. Further, we introduce a preliminary approach that reduces potential resulting artifacts of these transformations with a volumetric membrane interpolation technique inspired by Poisson image editing and provide a process that “distills” the edits into a standalone NeRF representation.