Obj-NeRF: Extract Object NeRFs from Multi-view Images
Zhiyi Li Lihe Ding Tianfan Xue
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.