The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has
led to increased interest in 3D scene editing. One important 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 labelling, 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 either point or text prompts on a single view, achieving better
performance in less time than previous works. Our method uses a points
projection strategy to rapidly spread user annotations to all views,
significantly reducing the processing burden. This algorithm allows us to
leverage the recent 2D segmentation model Segment-Anything (SAM) to predict
masks with improved precision and efficiency. Additionally, we obtain colour
and depth priors through 2D inpainting methods. Finally, our algorithm employs
depth supervision and perceptual loss for scene reconstruction 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.

本文提出了一种名为 OR-NeRF 的新型对象移除流水线，它可以在单个视图上通过点或文本提示从 3D 场景中移除对象，并在更短的时间内实现更好的性能。

OR-NeRF：由多视角分割指导的神经辐射场，用于删除 3D 场景中的对象

OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation  with Neural Radiance Fields

Weakly supervised semantic segmentation with weak labels is a long-lived
ill-posed problem. Mainstream methods mainly focus on improving the quality of
pseudo labels. In this report, we attempt to explore the potential of 'prompt
to masks' from the powerful class-agnostic large segmentation model,
segment-anything. Specifically, different weak labels are used as prompts to
the segment-anything model, generating precise class masks. The class masks are
utilized to generate pseudo labels to train the segmentation networks. We have
conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments
demonstrate that segment-anything can serve as a good pseudo-label generator.
The code will be made publicly available.

本研究旨在探讨通过利用弱标签作为暗示来生成准确的分割掩码，并将其作为趋近于真实标签的伪标签来训练分割网络中的潜力。在 PASCAL VOC 2012 数据集上的实验证明了 segment-anything 可以作为良好的伪标签生成器。