Beyond Isolated Objects: Relationship-aware Open Vocabulary Scene Understanding via 3D Scene Graph Analysis

Open-vocabulary 3D scene understanding aims to segment3D scenes beyond predefined categories by transferring semantic knowl-edge from vision-language models. Existing methods have advanced thistask by lifting language-aligned 2D features into 3D, yet they oftenrely on context-independent semantic representations, leaving object re-lationships underexplored for contextual refinement. We propose Rel-GraphOV, a relationship-aware framework that uses 3D scene graphs toenhance open-vocabulary 3D understanding. Our method constructs re-lational scene graphs from multi-view observations by leveraging vision-language reasoning to infer object relationships and prune geometricallyimplausible connections, without manual relationship annotations. Toaggregate relational context while avoiding feature interference, we in-troduce an Adaptive Gated Dual-Stream Contextual GAT that separatesdense geometric features and semantic CLIP embeddings, performs edge-guided message passing, and adaptively fuses complementary semantics.A hierarchical contrastive objective further promotes instance-level con-sistency and category-level discrimination. Experiments on ScanNetV2,ScanNet200, ScanNet++, and Replica demonstrate strong performanceand generalization ability. Project Page: cxavireh.github.io/relgraphov-projectpage