aiqbench analysis
This research introduces BiMoSG, a bimodal 3D scene graph generation method that dynamically switches between coarse and fine representations to optimize efficiency for open-set robotic tasks. By prioritizing fast, coarse graphs by default and refining them only when necessary, the approach significantly accelerates real-time deployment compared to existing state-of-the-art models. The work advances machine learning in robotics by demonstrating how adaptive representation switching can balance computational cost with detailed environmental understanding.
Seeing Fast and Slow: Bimodal 3D Scene Graphs for Open-set Tasks
arXiv:2605.31067v3 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:2605.31067v3 [cs.RO] for this version)
From: Malika Meghjani Dr. [view email] [v1] Fri, 29 May 2026 09:37:19 UTC (4,615 KB) [v2] Tue, 2 Jun 2026 08:08:50 UTC (1 KB) (withdrawn) [v3] Mon, 5 Oct 2026 10:48:34 UTC (1,988 KB)
Full text under the publisher's open licence · 196 words
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