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Neural 3D reconstruction from multi-view images has recently attractedincreasing attention from the community. Existing methods normally learn aneural field for the whole scene, while it is still under-explored how toreconstruct a target object indicated by users. Considering the SegmentAnything Model (SAM) has shown effectiveness in segmenting any 2D images, inthis paper, we propose NTO3D, a novel high-quality Neural Target Object 3D(NTO3D) reconstruction method, which leverages the benefits of both neuralfield and SAM. We first propose a novel strategy to lift the multi-view 2Dsegmentation masks of SAM into a unified 3D occupancy field. The 3D occupancyfield is then projected into 2D space and generates the new prompts for SAM.This process is iterative until convergence to separate the target object fromthe scene. After this, we then lift the 2D features of the SAM encoder into a3D feature field in order to improve the reconstruction quality of the targetobject. NTO3D lifts the 2D masks and features of SAM into the 3D neural fieldfor high-quality neural target object 3D reconstruction. We conduct detailedexperiments on several benchmark datasets to demonstrate the advantages of ourmethod. The code will be available at: https://github.com/ucwxb/NTO3D.

NTO3D: Neural Target Object 3D Reconstruction with Segment Anything