NestSplat: Hierarchical Relational Reasoning Segmentation in 3D Gaussian Splatting

Language-guided segmentation in 3D Gaussian Splatting (3DGS) must resolve spatial and relational constraints, yet existing benchmarks emphasize single targets and shallow relations. We introduce Nest-LERF and Nest-ScanNet under zero-target, single-target, and grouped-multiple-target settings. Nest-LERF covers nesting, stacking, part–whole, and mixed relations, while room-scale Nest-ScanNet covers nesting, part–whole, spatial-directional, and mixed relations. We also propose NestSplat, a training-free framework that derives a scene vocabulary from training views, lifts 2D masks into language-anchored 3D supports, and selects query targets through bounded vision–language decisions with geometric context. Without per-scene training, NestSplat achieves 34.16% and 27.42% mIoU on Nest-LERF and Nest-ScanNet, respectively, and transfers strongly to Ref-LERF and Causal-LERF.