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With the development of the neural field, reconstructing the 3D model of atarget object from multi-view inputs has recently attracted increasingattention from the community. Existing methods normally learn a neural fieldfor the whole scene, while it is still under-explored how to reconstruct acertain object indicated by users on-the-fly. Considering the Segment AnythingModel (SAM) has shown effectiveness in segmenting any 2D images, in this paper,we propose Neural Object Cloning (NOC), a novel high-quality 3D objectreconstruction method, which leverages the benefits of both neural field andSAM from two aspects. Firstly, to separate the target object from the scene, wepropose a novel strategy to lift the multi-view 2D segmentation masks of SAMinto a unified 3D variation field. The 3D variation field is then projectedinto 2D space and generates the new prompts for SAM. This process is iterativeuntil convergence to separate the target object from the scene. Then, apartfrom 2D masks, we further lift the 2D features of the SAM encoder into a 3D SAMfield in order to improve the reconstruction quality of the target object. NOClifts the 2D masks and features of SAM into the 3D neural field forhigh-quality target object reconstruction. We conduct detailed experiments onseveral benchmark datasets to demonstrate the advantages of our method. Thecode will be released.

NOC: High-Quality Neural Object Cloning with 3D Lifting of Segment Anything