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BiMoSG introduces a bimodal 3D scene graph approach that dynamically switches between coarse and fine representations to optimize efficiency for open-set robotic tasks. By prioritizing a fast, coarse mode by default and refining only when necessary, the method achieves real-time deployment speeds significantly faster than current open-source state-of-the-art solutions. This adaptive strategy offers a practical pathway for integrating complex scene understanding directly into real-time robot execution pipelines.
Seeing Fast and Slow: Bimodal 3D Scene Graphs for Open-set Tasks
arXiv:2605.31067v2 Announce Type: replace Abstract: Open-set task execution can significantly benefit from seamlessly switching between coarse and fine scene representations depending on the context and the evolving information as the robot explores the environment. For example, it is often sufficient to start with a coarse scene representation initially and only employ a finer, more granular scene representation when the robot encounters regions which are likely to contain the task relevant objects. Hence, in this work, we propose BiMoSG, a bimodal 3D scene graph generation approach for open-set tasks. BiMoSG employs a "fast" mode by default to efficiently generate a coarse 3D scene graph and can switch to a "slow" mode for generating a finer open vocabulary 3D scene graph of task relevant objects. We demonstrate that our proposed 3D scene graph generation approach is significantly faster than the open-source state-of-the-art approaches. This allows us to integrate the scene graph generation process with task execution for real-time deployment.
Abstract:Open-set task execution can significantly benefit from seamlessly switching between coarse and fine scene representations depending on the context and the evolving information as the robot explores the environment. For example, it is often sufficient to start with a coarse scene representation initially and only employ a finer, more granular scene representation when the robot encounters regions which are likely to contain the task relevant objects. Hence, in this work, we propose BiMoSG, a bimodal 3D scene graph generation approach for open-set tasks. BiMoSG employs a "fast" mode by default to efficiently generate a coarse 3D scene graph and can switch to a "slow" mode for generating a finer open vocabulary 3D scene graph of task relevant objects. We demonstrate that our proposed 3D scene graph generation approach is significantly faster than the open-source state-of-the-art approaches. This allows us to integrate the scene graph generation process with task execution for real-time deployment.
Robotics (cs.RO)
arXiv:2605.31067 [cs.RO]
(or arXiv:2605.31067v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.31067 Focus to learn more arXiv-issued DOI via DataCite
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