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arXiv · 2605.21788

SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching

Xuefei Sun·Xujia Zhang·Brendan Crowe·Doncey Albin·Christoffer Heckman
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Abstract

Zero-shot 3D visual grounding requires localizing objects in unstructured environments from free-form natural language. Recent vision-language model (VLM) approaches achieve promising results but rely on view-dependent reasoning or implicit representations, limiting spatial consistency and interpretability for compositional queries. We propose SceneGraphGrounder, a framework that reformulates 3D grounding as structured graph matching over a reconstructed 3D scene graph. To enable this formulation, we introduce a visual marker prompting strategy that enables a VLM to infer object-object relationships from 2D views, which are subsequently lifted into a persistent 3D scene graph encoding both spatial and semantic relations. Given a query, we construct a query graph and perform constrained alignment with the scene graph, ensuring multi-view consistency and interpretable reasoning. Experiments on the ScanRefer benchmark demonstrate that our method achieves competitive performance among zero-shot approaches, using only RGB-D inputs. We further validate our framework through real-world deployment on a mobile robot, demonstrating robust spatial reasoning in long-horizon physical environments. We will make our code publicly available upon acceptance.

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Xuefei Sun, Xujia Zhang, Brendan Crowe, Doncey Albin, Christoffer Heckman. 2026-05-20. SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching. https://arxiv.org/abs/2605.21788

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