  The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has
increased interest in 3D scene editing. An essential task in editing is
removing objects from a scene while ensuring visual reasonability and multiview
consistency. However, current methods face challenges such as time-consuming
object labeling, limited capability to remove specific targets, and compromised
rendering quality after removal. This paper proposes a novel object-removing
pipeline, named OR-NeRF, that can remove objects from 3D scenes with user-given
points or text prompts on a single view, achieving better performance in less
time than previous works. Our method spreads user annotations to all views
through 3D geometry and sparse correspondence, ensuring 3D consistency with
less processing burden. Then recent 2D segmentation model Segment-Anything
(SAM) is applied to predict masks, and a 2D inpainting model is used to
generate color supervision. Finally, our algorithm applies depth supervision
and perceptual loss to maintain consistency in geometry and appearance after
object removal. Experimental results demonstrate that our method achieves
better editing quality with less time than previous works, considering both
quality and quantity.
