  Neural 3D reconstruction from multi-view images has recently attracted
increasing attention from the community. Existing methods normally learn a
neural field for the whole scene, while it is still under-explored how to
reconstruct a target object indicated by users. Considering the Segment
Anything Model (SAM) has shown effectiveness in segmenting any 2D images, in
this paper, we propose NTO3D, a novel high-quality Neural Target Object 3D
(NTO3D) reconstruction method, which leverages the benefits of both neural
field and SAM. We first propose a novel strategy to lift the multi-view 2D
segmentation masks of SAM into a unified 3D occupancy field. The 3D occupancy
field is then projected into 2D space and generates the new prompts for SAM.
This process is iterative until convergence to separate the target object from
the scene. After this, we then lift the 2D features of the SAM encoder into a
3D feature field in order to improve the reconstruction quality of the target
object. NTO3D lifts the 2D masks and features of SAM into the 3D neural field
for high-quality neural target object 3D reconstruction. We conduct detailed
experiments on several benchmark datasets to demonstrate the advantages of our
method. The code will be available at: https://github.com/ucwxb/NTO3D.
