3D scene reconstruction is a foundational problem in computer vision. Despite recent advancements in Neural Implicit Representations (NIR), existing methods often lack editability and compositional flexibility, limiting their use in scenarios requiring high interactivity and object-level manipulation. In this paper, we introduce the Gaussian Object Carver (GOC), a novel, efficient, and scalable framework for object-compositional 3D scene reconstruction. GOC leverages 3D Gaussian Splatting (GS), enriched with monocular geometry priors and multi-view geometry regularization, to achieve high-quality and flexible reconstruction. Furthermore, we propose a zero-shot Object Surface Completion (OSC) model, which uses 3D priors from 3d object data to reconstruct unobserved surfaces, ensuring object completeness even in occluded areas. Experimental results demonstrate that GOC improves reconstruction efficiency and geometric fidelity. It holds promise for advancing the practical application of digital twins in embodied AI, AR/VR, and interactive simulation environments.
三维场景重建是计算机视觉中的一个基础问题。尽管神经隐式表示（Neural Implicit Representations, NIR）取得了显著进展，但现有方法通常缺乏可编辑性和组合灵活性，限制了其在需要高交互性和对象级操作的场景中的应用。 本文提出了高斯对象雕刻器（Gaussian Object Carver, GOC），这是一种新颖、高效且可扩展的框架，用于对象组成式三维场景重建。GOC结合了三维高斯喷溅（3D Gaussian Splatting, GS）技术，并通过单目几何先验和多视图几何正则化，提供高质量且灵活的重建能力。此外，我们提出了一种零样本对象表面补全（Object Surface Completion, OSC）模型，利用来自三维对象数据的几何先验重建未观测到的表面，从而确保在被遮挡区域中对象的完整性。 实验结果表明，GOC在重建效率和几何保真度上显著提升，为数字孪生技术在具身人工智能（Embodied AI）、增强现实/虚拟现实（AR/VR）和交互式模拟环境中的实际应用开辟了新的可能性。