Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs

• URL: http://arxiv.org/abs/2504.13153v1
• Date: Thu, 17 Apr 2025 17:56:07 GMT
• Title: Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs
• Authors: Shaohui Dai, Yansong Qu, Zheyan Li, Xinyang Li, Shengchuan Zhang, Liujuan Cao,
• Abstract summary: We introduce a training-free framework that constructs a superpoint graph directly from Gaussian primitives.<n>The superpoint graph partitions the scene into spatially compact and semantically coherent regions, forming view-consistent 3D entities.<n>Our method achieves state-of-the-art open-vocabulary segmentation performance, with semantic field reconstruction completed over $30times$ faster.
• Score: 16.153129392697885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bridging natural language and 3D geometry is a crucial step toward flexible, language-driven scene understanding. While recent advances in 3D Gaussian Splatting (3DGS) have enabled fast and high-quality scene reconstruction, research has also explored incorporating open-vocabulary understanding into 3DGS. However, most existing methods require iterative optimization over per-view 2D semantic feature maps, which not only results in inefficiencies but also leads to inconsistent 3D semantics across views. To address these limitations, we introduce a training-free framework that constructs a superpoint graph directly from Gaussian primitives. The superpoint graph partitions the scene into spatially compact and semantically coherent regions, forming view-consistent 3D entities and providing a structured foundation for open-vocabulary understanding. Based on the graph structure, we design an efficient reprojection strategy that lifts 2D semantic features onto the superpoints, avoiding costly multi-view iterative training. The resulting representation ensures strong 3D semantic coherence and naturally supports hierarchical understanding, enabling both coarse- and fine-grained open-vocabulary perception within a unified semantic field. Extensive experiments demonstrate that our method achieves state-of-the-art open-vocabulary segmentation performance, with semantic field reconstruction completed over $30\times$ faster. Our code will be available at https://github.com/Atrovast/THGS.

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