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.

本研究针对3D场景分割中交互性不足的问题，提出了一种新的方法GaussianCut，通过图切割算法进行有效的高斯分割。采用直观的用户输入（如点点击、粗略涂鸦或文本）来选择要分割的对象，显著提高了分割的准确性和便捷性。实验证明，GaussianCut在多样化场景中表现优越，与现有的三维分割技术竞争，且不需要额外的培训。

GaussianCut：基于图切割的3D高斯交互式分割