用锚点图结构让3D高斯点更精准区分物体，支持交互式编辑。
AG$^2$aussian: Anchor-Graph Structured Gaussian Splatting for Instance-Level 3D Scene Understanding and Editing
- 构建锚点图结构组织语义特征，规范高斯点分布
- 实现清晰的实例级高斯点选择，提升分割准确性
- 适用于点击查询、文本驱动编辑等多类场景
3D高斯点绘（3DGS）在众多应用中快速普及，亟需具备语义感知能力的3D高斯表示以支持场景理解与编辑。现有方法通常将语义特征附加到自由高斯点上，并通过可微渲染进行特征蒸馏，导致分割噪声大、高斯点选择混乱。本文提出AG²aussian，一种基于锚点图结构的新框架，用于组织语义特征并调控高斯原始体。该结构不仅促进紧凑且具有实例感知能力的高斯分布，还支持基于图的传播机制，实现干净准确的实例级高斯点选择。在四个应用场景（交互式点击查询、开放词汇文本驱动查询、物体移除编辑、物理模拟）中的广泛验证表明，本方法具有显著优势，且对各类应用均有增益。实验与消融研究进一步评估了关键设计的有效性。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has witnessed exponential adoption across diverse applications, driving a critical need for semantic-aware 3D Gaussian representations to enable scene understanding and editing tasks. Existing approaches typically attach semantic features to a collection of free Gaussians and distill the features via differentiable rendering, leading to noisy segmentation and a messy selection of Gaussians. In this paper, we introduce AG$^2$aussian, a novel framework that leverages an anchor-graph structure to organize semantic features and regulate Gaussian primitives. Our anchor-graph structure not only promotes compact and instance-aware Gaussian distributions, but also facilitates graph-based propagation, achieving a clean and accurate instance-level Gaussian selection. Extensive validation across four applications, i.e. interactive click-based query, open-vocabulary text-driven query, object removal editing, and physics simulation, demonstrates the advantages of our approach and its benefits to various applications. The experiments and ablation studies further evaluate the effectiveness of the key designs of our approach.
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