BluNF: Blueprint Neural Field
Robin Courant Xi Wang Marc Christie Vicky Kalogeiton
Neural Radiance Fields (NeRFs) have revolutionized scene novel view
synthesis, offering visually realistic, precise, and robust implicit
reconstructions. While recent approaches enable NeRF editing, such as object
removal, 3D shape modification, or material property manipulation, the manual
annotation prior to such edits makes the process tedious. Additionally,
traditional 2D interaction tools lack an accurate sense of 3D space, preventing
precise manipulation and editing of scenes. In this paper, we introduce a novel
approach, called Blueprint Neural Field (BluNF), to address these editing
issues. BluNF provides a robust and user-friendly 2D blueprint, enabling
intuitive scene editing. By leveraging implicit neural representation, BluNF
constructs a blueprint of a scene using prior semantic and depth information.
The generated blueprint allows effortless editing and manipulation of NeRF
representations. We demonstrate BluNF's editability through an intuitive
click-and-change mechanism, enabling 3D manipulations, such as masking,
appearance modification, and object removal. Our approach significantly
contributes to visual content creation, paving the way for further research in
this area.