3D Gaussian Splatting has emerged as an alternative 3D representation of Neural Radiance Fields (NeRFs), benefiting from its high-quality rendering results and real-time rendering speed. Considering the 3D Gaussian representation remains unparsed, it is necessary first to execute object segmentation within this domain. Subsequently, scene editing and collision detection can be performed, proving vital to a multitude of applications, such as virtual reality (VR), augmented reality (AR), game/movie production, etc. In this paper, we propose a novel approach to achieve object segmentation in 3D Gaussian via an interactive procedure without any training process and learned parameters. We refer to the proposed method as SA-GS, for Segment Anything in 3D Gaussians. Given a set of clicked points in a single input view, SA-GS can generalize SAM to achieve 3D consistent segmentation via the proposed multi-view mask generation and view-wise label assignment methods. We also propose a cross-view label-voting approach to assign labels from different views. In addition, in order to address the boundary roughness issue of segmented objects resulting from the non-negligible spatial sizes of 3D Gaussian located at the boundary, SA-GS incorporates the simple but effective Gaussian Decomposition scheme. Extensive experiments demonstrate that SA-GS achieves high-quality 3D segmentation results, which can also be easily applied for scene editing and collision detection tasks.
3D 高斯散射已经成为神经辐射场（NeRFs）的一种替代3D表示方法，它因高质量的渲染结果和实时渲染速度而受益。考虑到3D高斯表示仍未被解析，首先需要在此域内执行对象分割。随后，可以进行场景编辑和碰撞检测，这对于许多应用至关重要，如虚拟现实（VR）、增强现实（AR）、游戏/电影制作等。在本文中，我们提出了一种在3D高斯中实现对象分割的新方法，该方法通过一个无需任何训练过程和学习参数的交互式程序来实现。我们将这种方法称为SA-GS，即在3D高斯中分割任何东西。通过在单个输入视图中点击一组点，SA-GS可以利用所提出的多视图掩码生成和逐视图标签分配方法，实现3D一致性分割的SAM推广。我们还提出了一种跨视图标签投票方法，用于从不同视图分配标签。此外，为了解决由于位于边界的3D高斯的非微不足道的空间大小导致的分割对象边界粗糙问题，SA-GS采用了简单但有效的高斯分解方案。广泛的实验表明，SA-GS实现了高质量的3D分割结果，这也可以轻松应用于场景编辑和碰撞检测任务。