无需训练和相机视角，快速实现3D高斯点云的交互式分割。
B$^3$-Seg: Camera-Free, Training-Free 3DGS Segmentation via Analytic EIG and Beta-Bernoulli Bayesian Updates
- 基于贝叶斯更新与信息增益选择最优观测视角。
- 端到端分割仅需数秒，性能媲美昂贵监督方法。
- 适合影视游戏资产实时编辑，理论保证高效性。
交互式3D高斯溅射（3DGS）分割对影视与游戏制作中预重建资产的实时编辑至关重要。然而，现有方法依赖预设相机视角、真值标签或高昂重训练，难以满足低延迟需求。本文提出B³-Seg（Beta-Bernoulli贝叶斯3DGS分割），一种在无相机、免训练条件下实现开放词汇3DGS分割的快速且理论严谨的方法。通过将分割建模为序列贝塔-伯努利贝叶斯更新，并利用解析期望信息增益（EIG）主动选择下一视角，该方法保证了EIG的自适应单调性与子模性，从而以贪心策略获得最优视角采样策略的(1−1/e)近似解。多数据集实验表明，B³-Seg在保持竞争性能的同时，实现了端到端分割仅需数秒，验证了其在实际交互场景中的可行性与可证明的信息效率。
原文摘要 · Abstract (English)
Interactive 3D Gaussian Splatting (3DGS) segmentation is essential for real-time editing of pre-reconstructed assets in film and game production. However, existing methods rely on predefined camera viewpoints, ground-truth labels, or costly retraining, making them impractical for low-latency use. We propose B$^3$-Seg (Beta-Bernoulli Bayesian Segmentation for 3DGS), a fast and theoretically grounded method for open-vocabulary 3DGS segmentation under camera-free and training-free conditions. Our approach reformulates segmentation as sequential Beta-Bernoulli Bayesian updates and actively selects the next view via analytic Expected Information Gain (EIG). This Bayesian formulation guarantees the adaptive monotonicity and submodularity of EIG, which produces a greedy $(1{-}1/e)$ approximation to the optimal view sampling policy. Experiments on multiple datasets show that B$^3$-Seg achieves competitive results to high-cost supervised methods while operating end-to-end segmentation within a few seconds. The results demonstrate that B$^3$-Seg enables practical, interactive 3DGS segmentation with provable information efficiency.
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