MA3DSG: Multi-Agent 3D Scene Graph Generation for Large-Scale Indoor Environments
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915773028499456 |
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| author | Kim, Yirum Kim, Jaewoo Kim, Ue-Hwan |
| author_facet | Kim, Yirum Kim, Jaewoo Kim, Ue-Hwan |
| contents | Current 3D scene graph generation (3DSGG) approaches heavily rely on a single-agent assumption and small-scale environments, exhibiting limited scalability to real-world scenarios. In this work, we introduce Multi-Agent 3D Scene Graph Generation (MA3DSG) model, the first framework designed to tackle this scalability challenge using multiple agents. We develop a training-free graph alignment algorithm that efficiently merges partial query graphs from individual agents into a unified global scene graph. Leveraging extensive analysis and empirical insights, our approach enables conventional single-agent systems to operate collaboratively without requiring any learnable parameters. To rigorously evaluate 3DSGG performance, we propose MA3DSG-Bench-a benchmark that supports diverse agent configurations, domain sizes, and environmental conditions-providing a more general and extensible evaluation framework. This work lays a solid foundation for scalable, multi-agent 3DSGG research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04152 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MA3DSG: Multi-Agent 3D Scene Graph Generation for Large-Scale Indoor Environments Kim, Yirum Kim, Jaewoo Kim, Ue-Hwan Robotics Artificial Intelligence Current 3D scene graph generation (3DSGG) approaches heavily rely on a single-agent assumption and small-scale environments, exhibiting limited scalability to real-world scenarios. In this work, we introduce Multi-Agent 3D Scene Graph Generation (MA3DSG) model, the first framework designed to tackle this scalability challenge using multiple agents. We develop a training-free graph alignment algorithm that efficiently merges partial query graphs from individual agents into a unified global scene graph. Leveraging extensive analysis and empirical insights, our approach enables conventional single-agent systems to operate collaboratively without requiring any learnable parameters. To rigorously evaluate 3DSGG performance, we propose MA3DSG-Bench-a benchmark that supports diverse agent configurations, domain sizes, and environmental conditions-providing a more general and extensible evaluation framework. This work lays a solid foundation for scalable, multi-agent 3DSGG research. |
| title | MA3DSG: Multi-Agent 3D Scene Graph Generation for Large-Scale Indoor Environments |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2602.04152 |