  Despite the emergence of successful NeRF inpainting methods built upon
explicit RGB and depth 2D inpainting supervisions, these methods are inherently
constrained by the capabilities of their underlying 2D inpainters. This is due
to two key reasons: (i) independently inpainting constituent images results in
view-inconsistent imagery, and (ii) 2D inpainters struggle to ensure
high-quality geometry completion and alignment with inpainted RGB images.
  To overcome these limitations, we propose a novel approach called MVIP-NeRF
that harnesses the potential of diffusion priors for NeRF inpainting,
addressing both appearance and geometry aspects. MVIP-NeRF performs joint
inpainting across multiple views to reach a consistent solution, which is
achieved via an iterative optimization process based on Score Distillation
Sampling (SDS). Apart from recovering the rendered RGB images, we also extract
normal maps as a geometric representation and define a normal SDS loss that
motivates accurate geometry inpainting and alignment with the appearance.
Additionally, we formulate a multi-view SDS score function to distill
generative priors simultaneously from different view images, ensuring
consistent visual completion when dealing with large view variations. Our
experimental results show better appearance and geometry recovery than previous
NeRF inpainting methods.
