3D场景里点一下，就能把整件物体抠出来
在3D重建的场景里想选中一个完整物体，过去要么给每个物体重新训练一遍模型，要么让AI从几十个角度反复看，又慢又费算力。这篇提出一个不用训练的方法：把场景里密密麻麻的点先聚成一小块一小块的「超点」，再根据颜色和位置连成一张网；你随手画几笔，系统就把这几笔的提示沿着网扩散开，像切蛋糕一样把整个物体切出来。它支持你从新角度再补几笔来修正，越改越准。效果和需要大量视角的旧方法差不多，但用的视角和算力都少得多。它不是你明天就能用上的产品，但指向一个更实在的方向：以后修3D场景、抠3D素材，可能就像在照片里圈个物体一样简单。
📄 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.