发表机构
Research Institute of Intelligent Control and Systems, Harbin Institute of Technology（哈尔滨工业大学智能控制系统研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对室内环境中物体变化导致场景图不一致的问题，提出DSG框架，结合3D高斯表示、双视图变化检测与多粒度空间推理，在DynTHOR等基准上优于现有方法，提升动态场景图构建准确性。
AI 中文摘要
在室内环境中，由于人类活动或具身智能体交互，物体位置频繁发生变化，导致先前构建的场景图与当前场景不一致。为解决该问题，本文提出DSG，一种动态3D场景图构建框架，可检测物体变化并进行空间关系推理。首先，构建语义感知的3D高斯场景表示，开发基于双视图渲染的物体变化检测方法，以实现可靠的场景图节点更新。其次，提出结合多粒度视觉上下文的空间关系推理方法，使大语言模型能识别更丰富的物体间空间关系。此外，引入基于AI2-THOR仿真平台构建的动态室内场景图基准DynTHOR，用于评估动态环境下的场景图构建。在DynTHOR、3RScan及真实场景上的大量实验表明，DSG在物体节点构建和空间关系推理方面均持续优于现有方法，显著提升了动态场景图构建的准确性。
英文摘要
In indoor environments, object positions frequently change due to human activities or embodied-agent interactions, causing previously constructed scene graphs to become inconsistent with the current scene. To address this issue, we propose DSG, a dynamic 3D scene graph construction framework that detects object changes and performs spatial relationship reasoning. First, we construct a semantic-aware 3D Gaussian scene representation and develop a dual-view rendering-based object change detection method to enable reliable scene graph node updates. Second, we propose a spatial relationship reasoning method that incorporates multi-granularity visual context, enabling a large language model to identify a richer set of interobject spatial relationships. Furthermore, we introduce DynTHOR, a dynamic indoor scene graph benchmark built on the AI2-THOR simulation platform for evaluating scene graph construction in dynamic environments. Extensive experiments on Dyn-THOR, 3RScan, and real-world scenes demonstrate that DSG consistently outperforms existing methods in both object node construction and spatial relationship reasoning, significantly improving the accuracy of dynamic scene graph construction.
