3D 场景里点一下，就能把整件物体抠出来
在 3D 场景里选中一个完整物体，通常得把每个视角都看一遍、或者给模型打上标签，又慢又费算力。这篇提出一种免训练的方法：你只需在稀疏的几个视角上随手画几笔，它就能把散落的 3D 点聚成连贯的物体，像用剪刀沿着轮廓剪下来。它靠的是把密集点云粗化成「超点」、再用图优化把稀疏的笔迹扩散到整个物体，而且支持你多画几笔逐步修正。效果和需要密集视角的 SOTA 方法相当，但交互次数和计算量都大幅下降。做 3D 场景编辑、游戏资产提取的人，这是能直接上手用的。
📄 原文摘要(英文)
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.