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arXiv · 2608.10057

LEGO: Leveled Language Gaussian Splatting

Yuning Peng·Haiping Wang·Yuan Liu·Yipeng Lu·Zhen Dong·Bisheng Yang
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Abstract

We introduce LEGO for advanced open-vocabulary scene understanding. Beyond basic concept recognition, its core innovation lies in capturing the intrinsic semantic hierarchies within the scene, such as the "flowerpot -> bouquet -> bud -> petal" lineage. While foundation models like SAM can identify multi-granular structures in 2D, their partitions are strictly perspective-bound and lack cross-view consensus. LEGO self-adaptively re-grades volatile multi-view SAM granularities into a unified, 3D-consistent hierarchy. This provides precise supervision for the structurally coherent, multi-level segmentation of 3D scenes. By grounding these segments with CLIP embeddings, LEGO recovers open-vocabulary semantic logic across hierarchical levels. Furthermore, by incorporating spatial relationships, we elevate these segments into level-wise language scene graphs, effectively empowering Large Language Models to perform complex, context-aware spatial reasoning and precise visual grounding. Experimental results demonstrate that LEGO establishes new state-of-the-art performance across both promptable and open-vocabulary 3D segmentation benchmarks, exhibiting advanced hierarchical scene decomposition and context-aware spatial reasoning.

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Yuning Peng, Haiping Wang, Yuan Liu, Yipeng Lu, Zhen Dong, Bisheng Yang. 2026-08-10. LEGO: Leveled Language Gaussian Splatting. https://arxiv.org/abs/2608.10057

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