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.

翻译：从多视角图像进行神经三维重建近期受到学术界广泛关注。现有方法通常学习整个场景的神经场，而如何重建用户指定的目标物体仍处于探索阶段。鉴于Segment Anything Model（SAM）在二维图像分割中展现的有效性，本文提出NTO3D——一种高质量神经目标物体三维重建方法，融合神经场与SAM的优势。我们首先提出新型策略，将SAM生成的多视角二维分割掩码提升至统一的三维占据场。该三维占据场随后投影至二维空间，为SAM生成新提示，通过迭代优化直至目标物体与场景分离。接着，我们将SAM编码器的二维特征提升至三维特征场，以提升目标物体重建质量。NTO3D通过将SAM的二维掩码与特征提升至三维神经场，实现高质量神经目标物体三维重建。我们在多个基准数据集上进行了详细实验，验证了本方法的优越性。代码将发布于：https://github.com/ucwxb/NTO3D。