MA3DSG: Multi-Agent 3D Scene Graph Generation for Large-Scale Indoor Environments

• URL: http://arxiv.org/abs/2602.04152v1
• Date: Wed, 04 Feb 2026 02:39:57 GMT
• Title: MA3DSG: Multi-Agent 3D Scene Graph Generation for Large-Scale Indoor Environments
• Authors: Yirum Kim, Jaewoo Kim, Ue-Hwan Kim,
• Abstract summary: We introduce Multi-Agent 3D Scene Graph Generation (MA3DSG) model, the first framework designed to tackle this scalability challenge using multiple agents.<n>We develop a training-free graph alignment algorithm that efficiently merges partial graphs from individual agents into a unified global scene graph.
• Score: 6.071490877668865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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