LEGO: Leveled Language Gaussian Splatting

We introduce LEGO for advanced open-vocabulary scene un-derstanding. Beyond basic concept recognition, its core innovation lies incapturing the intrinsic semantic hierarchies within the scene, such as the"flowerpot → bouquet → bud → petal" lineage. While foundation mod-els like SAM can identify multi-granular structures in 2D, their partitionsare 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 struc-turally coherent, multi-level segmentation of 3D scenes. By groundingthese segments with CLIP embeddings, LEGO recovers open-vocabularysemantic logic across hierarchical levels. Furthermore, by incorporatingspatial relationships, we elevate these segments into level-wise languagescene graphs, effectively empowering Large Language Models to performcomplex, 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 3Dsegmentation benchmarks, exhibiting advanced hierarchical scene de-composition and context-aware spatial reasoning. Project page: https://pz0826.github.io/LEGO-Webpage/