Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics

TL;DR

Aion introduces a hierarchical 4D scene graph framework that integrates temporal flow dynamics into 3D scene representations, enhancing autonomous navigation in dynamic environments by improving interpretability and scalability.

Contribution

It is the first to embed temporal flow dynamics directly into hierarchical 3D scene graphs, combining semantic, geometric, and motion information for better scene understanding.

Findings

Enhanced scene understanding with integrated temporal dynamics.

Improved navigation planning in dynamic environments.

Scalable and interpretable motion prediction.

Abstract

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