用图割法实现3D高斯点云的交互式分割，单视图点击即可选中目标物体。
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting
- 将3D高斯点云构造成图，用图割算法优化能量函数进行分割
- 支持点击、涂鸦、文本等直观输入，无需额外训练即可达到顶尖性能
- 结合2D分割模型初筛，再通过图结构精调，适合快速交互式场景分割
我们提出GaussianCut，一种针对3D高斯点云表示场景的多视角交互式分割方法。用户仅需在单个视图上点击或涂鸦即可选择待分割对象。利用3D高斯喷溅（3DGS）作为场景表示，可直接从高斯集合中提取感兴趣物体。核心思想是将场景建模为图，通过图割算法最小化能量函数，实现高斯点的前景-背景分割。我们基于场景高斯构建图结构，并设计与分割对齐的能量函数，融合用户输入与场景属性。为获得初始粗分割，采用2D图像/视频分割模型，再通过图结构进一步优化。实验表明，GaussianCut在多样场景下表现稳健，性能媲美当前最优方法，且无需任何额外的分割训练。
原文摘要 · Abstract (English)
We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented by interacting with a single view. It accepts intuitive user input, such as point clicks, coarse scribbles, or text. Using 3D Gaussian Splatting (3DGS) as the underlying scene representation simplifies the extraction of objects of interest which are considered to be a subset of the scene's Gaussians. Our key idea is to represent the scene as a graph and use the graph-cut algorithm to minimize an energy function to effectively partition the Gaussians into foreground and background. To achieve this, we construct a graph based on scene Gaussians and devise a segmentation-aligned energy function on the graph to combine user inputs with scene properties. To obtain an initial coarse segmentation, we leverage 2D image/video segmentation models and further refine these coarse estimates using our graph construction. Our empirical evaluations show the adaptability of GaussianCut across a diverse set of scenes. GaussianCut achieves competitive performance with state-of-the-art approaches for 3D segmentation without requiring any additional segmentation-aware training.
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