  We propose InNeRF360, an automatic system that accurately removes
text-specified objects from 360-degree Neural Radiance Fields (NeRF). The
challenge is to effectively remove objects while inpainting perceptually
consistent content for the missing regions, which is particularly demanding for
existing NeRF models due to their implicit volumetric representation. Moreover,
unbounded scenes are more prone to floater artifacts in the inpainted region
than frontal-facing scenes, as the change of object appearance and background
across views is more sensitive to inaccurate segmentations and inconsistent
inpainting. With a trained NeRF and a text description, our method efficiently
removes specified objects and inpaints visually consistent content without
artifacts. We apply depth-space warping to enforce consistency across multiview
text-encoded segmentations, and then refine the inpainted NeRF model using
perceptual priors and 3D diffusion-based geometric priors to ensure visual
plausibility. Through extensive experiments in segmentation and inpainting on
360-degree and frontal-facing NeRFs, we show that our approach is effective and
enhances NeRF's editability. Project page: https://ivrl.github.io/InNeRF360.
