GaussianSelector：基于图优化的3D高斯泼溅中的轻量级人类引导物体选择

摘要

从重建场景中以最少用户操作选择完整3D物体，对于实际场景编辑和具身交互至关重要。现有的基于3DGS的方法要么重新训练高斯表示以嵌入逐对象标签，要么构建密集的多视图SAM观测，这两种方式都需要沉重的计算开销和密集的视角覆盖，而后者在实际中很少可用。我们提出GaussianSelector，一种无需训练的框架，可在稀疏视图和稀疏涂鸦引导下进行交互式3D物体选择。该方法直接操作原生高斯基元，将密集高斯粗化为几何连贯的超点，并利用外观与空间线索构建连续性加权图。稀疏用户涂鸦通过可见性感知的透射率覆盖被提升至3D，选择问题则被建模为全局图割能量最小化，从而将稀疏证据传播至完整3D物体。这一设计天然支持多轮细化，用户可从额外视角迭代修正选择，逐步改进结果。实验表明，GaussianSelector在达到与最先进的多视图SAM方法相当的选择质量的同时，所需的交互视图数量显著更少，计算开销大幅降低。这些特性使其非常适合在实际部署场景中用于人在回路的3D场景编辑和3D资产提取。

English

Selecting a complete 3D object from a reconstructed scene with minimal user effort is essential for practical scene editing and embodied interaction. Existing 3DGS-based methods either retrain the Gaussian representation to embed per-object labels, or build dense multi-view SAM observations, both requiring heavy computation and dense viewpoint coverage that is rarely available in practice. We present GaussianSelector, a training-free framework for interactive 3D object selection from sparse views and sparse scribble guidance. Operating directly on native Gaussian primitives, we coarsen dense Gaussians into geometrically coherent superpoints and construct a continuity-weighted graph using appearance and spatial cues. Sparse user scribbles are lifted into 3D via visibility-aware transmittance coverage, and selection is solved as a global graph-cut energy minimization that propagates sparse evidence to a complete 3D object. This design naturally supports multi-round refinement, where users iteratively correct the selection from additional viewpoints to progressively improve the result. Experiments demonstrate that GaussianSelector achieves competitive selection quality against state-of-the-art multi-view SAM-based methods, while requiring significantly fewer interaction views and substantially lower computational overhead. These properties make it well suited for human-in-the-loop 3D scene editing and 3D asset extraction in real-world deployment scenarios.