| title | GaussianSelector: Lightweight Human-Guided Object Selection in 3D Gaussian Splatting with Graph Optimization |
|---|---|
| authors | Baihan Yang, Tiexin Li, Yuheng Liu, Xin Lin, Xinke Li, Xiaohui Xie, Truong Nguyen |
| date | 2026-08-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.01492 |
| affiliation | LL Lab |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | 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. |
cite
@misc{yang2026gaussianselector,
title={GaussianSelector: Lightweight Human-Guided Object Selection in 3D Gaussian Splatting with Graph Optimization},
author={Baihan Yang and Tiexin Li and Yuheng Liu and Xin Lin and Xinke Li and Xiaohui Xie and Truong Nguyen},
year={2026},
eprint={2608.01492},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.01492}
}