将动态流动信息嵌入分层场景图，提升复杂环境下的导航预测能力。
Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics
- 用图结构稀疏表示动态模式，捕捉任意时间间隔的运动流。
- 在场景图节点上附着动态流，实现可解释的长时序预测。
- 适合需要理解动态交互的自动驾驶与机器人路径规划场景。
自主导航在动态环境中需要同时捕捉语义结构与时间演化。3D 场景图（3DSGs）提供分层多分辨率抽象，编码几何与语义，但现有动态扩展多聚焦于单个物体或代理。与此同时，动态地图（MoDs）建模典型运动模式与时间规律，但通常依赖网格化离散化，缺乏语义感知且难以扩展至大环境。本文提出 Aion 框架，将时间流动动态直接嵌入分层 3DSG，有效引入时间维度。Aion 采用基于图的稀疏 MoD 表示，捕捉任意时间间隔的运动流，并将其附加到场景图中的导航节点，实现更可解释、可扩展的预测，显著提升复杂动态环境中的规划与交互性能。代码已开源：https://github.com/IacopomC/aion。
原文摘要 · Abstract (English)
Autonomous navigation in dynamic environments requires spatial representations that capture both semantic structure and temporal evolution. 3D Scene Graphs (3DSGs) provide hierarchical multi-resolution abstractions that encode geometry and semantics, but existing extensions toward dynamics largely focus on individual objects or agents. In parallel, Maps of Dynamics (MoDs) model typical motion patterns and temporal regularities, yet are usually tied to grid-based discretizations that lack semantic awareness and do not scale well to large environments. In this paper we introduce Aion, a framework that embeds temporal flow dynamics directly within a hierarchical 3DSG, effectively incorporating the temporal dimension. Aion employs a graph-based sparse MoD representation to capture motion flows over arbitrary time intervals and attaches them to navigational nodes in the scene graph, yielding more interpretable and scalable predictions that improve planning and interaction in complex dynamic environments. We provide the code at https://github.com/IacopomC/aion
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。