RieMind：基于几何的场景理解空间智能体
RieMind: Geometry-Grounded Spatial Agent for Scene Understanding
AI总结：
本文提出RieMind，通过显式3D场景图实现空间推理，证明解耦感知与推理能显著提升空间推理能力，实验结果显示比现有方法提升16%-50%。
摘要：
视觉语言模型（VLMs）在理解室内场景中日益成为主流范式，但其在度量和空间推理方面仍存在挑战。当前方法依赖端到端视频理解和大规模空间问答微调，本质上将感知与推理耦合在一起。本文探讨解耦感知与推理是否能提升空间推理能力。我们提出了一种静态3D室内场景推理的智能体框架，将LLM显式地 grounding 在一个3D场景图（3DSG）中。而不是直接摄入视频，每个场景被表示为一个持久的3DSG，由专门的感知模块构建。为了隔离推理性能，我们从地面真实标注中实例化3DSG。智能体通过结构化的几何工具与场景交互，这些工具暴露了诸如物体尺寸、距离、姿态和空间关系等基本属性。我们在VSI-Bench静态分割上获得的结果在理想感知条件下提供了空间推理性能的上界，并发现其显著高于先前工作，最高提升达16%。与基础VLMs相比，我们的智能体变体在平均上提升了33%至50%。这些发现表明，显式几何 grounding 显著提高了空间推理性能，并表明结构化表示为纯粹端到端视觉推理提供了有吸引力的替代方案。
英文摘要：
Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reasoning. In this paper, we investigate whether decoupling perception and reasoning leads to improved spatial reasoning. We propose an agentic framework for static 3D indoor scene reasoning that grounds an LLM in an explicit 3D scene graph (3DSG). Rather than ingesting videos directly, each scene is represented as a persistent 3DSG constructed by a dedicated perception module. To isolate reasoning performance, we instantiate the 3DSG from ground-truth annotations. The agent interacts with the scene exclusively through structured geometric tools that expose fundamental properties such as object dimensions, distances, poses, and spatial relationships. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and we find that it is significantly higher than previous works, by up to 16\%, without task specific fine-tuning. Compared to base VLMs, our agentic variant achieves significantly better performance, with average improvements between 33\% to 50\%. These findings indicate that explicit geometric grounding substantially improves spatial reasoning performance, and suggest that structured representations offer a compelling alternative to purely end-to-end visual reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
