Neural Radiance Fields (NeRFs) have demonstrated remarkable effectiveness in
novel view synthesis within 3D environments. However, extracting a radiance
field of one specific object from multi-view images encounters substantial
challenges due to occlusion and background complexity, thereby presenting
difficulties in downstream applications such as NeRF editing and 3D mesh
extraction. To solve this problem, in this paper, we propose Obj-NeRF, a
comprehensive pipeline that recovers the 3D geometry of a specific object from
multi-view images using a single prompt. This method combines the 2D
segmentation capabilities of the Segment Anything Model (SAM) in conjunction
with the 3D reconstruction ability of NeRF. Specifically, we first obtain
multi-view segmentation for the indicated object using SAM with a single
prompt. Then, we use the segmentation images to supervise NeRF construction,
integrating several effective techniques. Additionally, we construct a large
object-level NeRF dataset containing diverse objects, which can be useful in
various downstream tasks. To demonstrate the practicality of our method, we
also apply Obj-NeRF to various applications, including object removal,
rotation, replacement, and recoloring.

本研究提出了 Obj-NeRF，一种综合管道，通过使用一个单一的提示从多视图图像中恢复特定对象的 3D 几何形状。该方法结合了 Segment Anything Model（SAM）的 2D 分割能力和 NeRF 的 3D 重建能力，并应用了几种有效技术。此外，研究还构建了一个包含多样化对象的大规模对象级 NeRF 数据集，可在各种下游任务中发挥作用。为了证明我们的方法的实用性，我们还将 Obj-NeRF 应用于包括对象去除、旋转、替换和重新上色在内的各种应用。