  Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene
representation that allows for novel view synthesis. Increasingly, NeRFs will
be shareable with other people. Before sharing a NeRF, though, it might be
desirable to remove personal information or unsightly objects. Such removal is
not easily achieved with the current NeRF editing frameworks. We propose a
framework to remove objects from a NeRF representation created from an RGB-D
sequence. Our NeRF inpainting method leverages recent work in 2D image
inpainting and is guided by a user-provided mask. Our algorithm is underpinned
by a confidence based view selection procedure. It chooses which of the
individual 2D inpainted images to use in the creation of the NeRF, so that the
resulting inpainted NeRF is 3D consistent. We show that our method for NeRF
editing is effective for synthesizing plausible inpaintings in a multi-view
coherent manner. We validate our approach using a new and still-challenging
dataset for the task of NeRF inpainting.
