  We propose ExtraNeRF, a novel method for extrapolating the range of views
handled by a Neural Radiance Field (NeRF). Our main idea is to leverage NeRFs
to model scene-specific, fine-grained details, while capitalizing on diffusion
models to extrapolate beyond our observed data. A key ingredient is to track
visibility to determine what portions of the scene have not been observed, and
focus on reconstructing those regions consistently with diffusion models. Our
primary contributions include a visibility-aware diffusion-based inpainting
module that is fine-tuned on the input imagery, yielding an initial NeRF with
moderate quality (often blurry) inpainted regions, followed by a second
diffusion model trained on the input imagery to consistently enhance, notably
sharpen, the inpainted imagery from the first pass. We demonstrate high-quality
results, extrapolating beyond a small number of (typically six or fewer) input
views, effectively outpainting the NeRF as well as inpainting newly disoccluded
regions inside the original viewing volume. We compare with related work both
quantitatively and qualitatively and show significant gains over prior art.
