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Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces

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arxiv 2503.19199 v1 pith:NPHBUBDJ submitted 2025-03-24 cs.CV

Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces

Chenyangguang Zhang , Alexandros Delitzas , Fangjinhua Wang , Ruida Zhang , Xiangyang Ji , Marc Pollefeys , Francis Engelmann This is my paper
classification cs.CV
keywords functionalscenegraphsmodelsdatasetincludingindoorlanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial relationships of objects, functional 3D scene graphs capture objects, interactive elements, and their functional relationships. Due to the lack of training data, we leverage foundation models, including visual language models (VLMs) and large language models (LLMs), to encode functional knowledge. We evaluate our approach on an extended SceneFun3D dataset and a newly collected dataset, FunGraph3D, both annotated with functional 3D scene graphs. Our method significantly outperforms adapted baselines, including Open3DSG and ConceptGraph, demonstrating its effectiveness in modeling complex scene functionalities. We also demonstrate downstream applications such as 3D question answering and robotic manipulation using functional 3D scene graphs. See our project page at https://openfungraph.github.io

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Cited by 4 Pith papers

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

  1. RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses

    cs.CV 2026-05 unverdicted novelty 7.0

    RelWitness introduces relation witnesses from visual and geometric cues to learn open-vocabulary 3D scene graphs under incomplete supervision using a positive-unlabeled objective.

  2. 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.

  3. RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses

    cs.CV 2026-05 unverdicted novelty 6.0

    RelWitness uses concrete visual-geometric cues to verify and learn from missing relation labels in open-vocabulary 3D scene graph generation.

  4. RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses

    cs.CV 2026-05 unverdicted novelty 6.0

    RelWitness introduces relation witnesses as observable visual-geometric cues to classify unannotated relations and enable positive-unlabeled learning for open-vocabulary 3D scene graph generation.