Neural Impostor: Editing Neural Radiance Fields with Explicit Shape Manipulation
This addresses the problem of limited NeRF editing capabilities for 3D content creators, offering a practical solution that is incremental in improving existing methods.
The paper tackled the challenge of editing Neural Radiance Fields (NeRF) for geometry modification by introducing Neural Impostor, a hybrid representation combining explicit tetrahedral meshes with multigrid implicit fields, enabling efficient deformation, composition, and generation while maintaining volumetric appearance.
Neural Radiance Fields (NeRF) have significantly advanced the generation of highly realistic and expressive 3D scenes. However, the task of editing NeRF, particularly in terms of geometry modification, poses a significant challenge. This issue has obstructed NeRF's wider adoption across various applications. To tackle the problem of efficiently editing neural implicit fields, we introduce Neural Impostor, a hybrid representation incorporating an explicit tetrahedral mesh alongside a multigrid implicit field designated for each tetrahedron within the explicit mesh. Our framework bridges the explicit shape manipulation and the geometric editing of implicit fields by utilizing multigrid barycentric coordinate encoding, thus offering a pragmatic solution to deform, composite, and generate neural implicit fields while maintaining a complex volumetric appearance. Furthermore, we propose a comprehensive pipeline for editing neural implicit fields based on a set of explicit geometric editing operations. We show the robustness and adaptability of our system through diverse examples and experiments, including the editing of both synthetic objects and real captured data. Finally, we demonstrate the authoring process of a hybrid synthetic-captured object utilizing a variety of editing operations, underlining the transformative potential of Neural Impostor in the field of 3D content creation and manipulation.