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. Codes will be released soon.

翻译：3D高斯泼溅（3D Gaussian Splatting）作为神经辐射场（NeRFs）的替代3D表示方法，凭借其高质量渲染结果与实时渲染速度脱颖而出。考虑到3D高斯表示尚未得到解析，首先需要在该领域内执行目标分割，进而可实现场景编辑与碰撞检测，这对虚拟现实（VR）、增强现实（AR）、游戏/电影制作等众多应用至关重要。本文提出一种新颖方法，通过无需训练过程和学习参数的交互式流程，实现3D高斯中的目标分割。我们将该方法称为SA-GS（Segment Anything in 3D Gaussians）。给定单个输入视图中的一组点击点，SA-GS可通过所提出的多视图掩码生成与视图级标签分配方法，泛化SAM模型以实现3D一致性分割。我们进一步提出跨视图标签投票方法，以分配来自不同视图的标签。此外，针对位于边界处的3D高斯因不可忽略的空间尺寸导致分割对象边界粗糙的问题，SA-GS嵌入了简单而有效的高斯分解方案。大量实验表明，SA-GS能够生成高质量的3D分割结果，并可轻松应用于场景编辑和碰撞检测任务。代码将很快开源。