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Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs

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arxiv 2309.15940 v1 pith:USGXBGTP submitted 2023-09-27 cs.RO cs.CV

Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs

classification cs.RO cs.CV
keywords localizationopen-vocabularyovsgscenecontext-awaredatasetentityexperiments
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-based queries. Unlike conventional semantic-based object localization approaches, our system facilitates context-aware entity localization, allowing for queries such as ``pick up a cup on a kitchen table" or ``navigate to a sofa on which someone is sitting". In contrast to existing research on 3D scene graphs, OVSG supports free-form text input and open-vocabulary querying. Through a series of comparative experiments using the ScanNet dataset and a self-collected dataset, we demonstrate that our proposed approach significantly surpasses the performance of previous semantic-based localization techniques. Moreover, we highlight the practical application of OVSG in real-world robot navigation and manipulation experiments.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Pixels to Concepts: Growing Rich 3D Semantic Scene Graph Forests utilizing Foundation Models

    cs.RO 2026-06 unverdicted novelty 6.0

    Uses VLMs to detect instance concepts and LLMs to infer abstract relationships, assembling them into 3D scene graph forests that are evaluated on uHumans2 and ScanNet and tested in open-vocabulary retrieval on a Spot robot.

  2. Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs

    cs.RO 2026-05 unverdicted novelty 6.0

    Dynamic scene graphs serve as explicit memory to improve imitation learning policies for spatial-temporal reasoning under partial observability in mobile and tabletop manipulation.