用高斯点云实现可编辑的3D物体组合重建
Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion
- 基于单目几何先验与多视角正则化，提升高斯点云重建精度
- 零样本完成遮挡区域表面，确保物体完整性
- 适合数字孪生、AR/VR等需交互式操作的场景
3D场景重建是计算机视觉的基础问题。尽管神经隐式表示（NIR）近期取得进展，但现有方法普遍存在可编辑性差、组合灵活性不足的问题，限制了在高交互性及物体级操作场景中的应用。本文提出一种新型、高效且可扩展的物体组合式3D场景重建框架——高斯物体雕刻器（Gaussian Object Carver, GOC）。GOC采用3D高斯点云（GS），结合单目几何先验与多视图几何正则化，实现高质量、灵活的重建。此外，我们提出零样本物体表面补全（OSC）模型，利用3D物体数据中的先验信息重建未观测表面，确保遮挡区域的物体完整性。实验表明，GOC显著提升了重建效率与几何保真度，有望推动数字孪生在具身智能、AR/VR及交互式仿真环境中的实际应用。
原文摘要 · Abstract (English)
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.
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