  Aliasing artifacts in renderings produced by Neural Radiance Field (NeRF) is
a long-standing but complex issue in the field of 3D implicit representation,
which arises from a multitude of intricate causes and was mitigated by
designing more advanced but complex scene parameterization methods before. In
this paper, we present a Diffusion-based restoration method for anti-aliasing
Neural Radiance Field (Drantal-NeRF). We consider the anti-aliasing issue from
a low-level restoration perspective by viewing aliasing artifacts as a kind of
degradation model added to clean ground truths. By leveraging the powerful
prior knowledge encapsulated in diffusion model, we could restore the
high-realism anti-aliasing renderings conditioned on aliased low-quality
counterparts. We further employ a feature-wrapping operation to ensure
multi-view restoration consistency and finetune the VAE decoder to better adapt
to the scene-specific data distribution. Our proposed method is easy to
implement and agnostic to various NeRF backbones. We conduct extensive
experiments on challenging large-scale urban scenes as well as unbounded
360-degree scenes and achieve substantial qualitative and quantitative
improvements.
