Open-World 3D Scene Graph Generation for Retrieval-Augmented Reasoning

• URL: http://arxiv.org/abs/2511.05894v1
• Date: Sat, 08 Nov 2025 07:37:29 GMT
• Title: Open-World 3D Scene Graph Generation for Retrieval-Augmented Reasoning
• Authors: Fei Yu, Quan Deng, Shengeng Tang, Yuehua Li, Lechao Cheng,
• Abstract summary: We propose a unified framework for Open-World 3D Scene Graph Generation with Retrieval-Augmented Reasoning.<n>Our method integrates Vision-Language Models (VLMs) with retrieval-based reasoning to support multimodal exploration and language-guided interaction.<n>We evaluate our method on 3DSSG and Replica benchmarks across four tasks-scene question answering, visual grounding, instance retrieval, and task planning-demonstrating robust generalization and superior performance in diverse environments.
• Score: 24.17324180628543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding 3D scenes in open-world settings poses fundamental challenges for vision and robotics, particularly due to the limitations of closed-vocabulary supervision and static annotations. To address this, we propose a unified framework for Open-World 3D Scene Graph Generation with Retrieval-Augmented Reasoning, which enables generalizable and interactive 3D scene understanding. Our method integrates Vision-Language Models (VLMs) with retrieval-based reasoning to support multimodal exploration and language-guided interaction. The framework comprises two key components: (1) a dynamic scene graph generation module that detects objects and infers semantic relationships without fixed label sets, and (2) a retrieval-augmented reasoning pipeline that encodes scene graphs into a vector database to support text/image-conditioned queries. We evaluate our method on 3DSSG and Replica benchmarks across four tasks-scene question answering, visual grounding, instance retrieval, and task planning-demonstrating robust generalization and superior performance in diverse environments. Our results highlight the effectiveness of combining open-vocabulary perception with retrieval-based reasoning for scalable 3D scene understanding.

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