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

arXiv: https://arxiv.org/abs/2512.11903 · HTML: https://mlsanity.com/paper/2512.11903/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 19 of 19)

- [Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization](https://mlsanity.com/paper/2201.13360/) (2022)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems](https://mlsanity.com/paper/2305.07154/) (2023)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.01176/) (2021)
- [VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud](https://mlsanity.com/paper/2303.14408/) (2023)
- [Collaborative Dynamic 3D Scene Graphs for Automated Driving](https://mlsanity.com/paper/2309.06635/) (2023)
- [S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations](https://mlsanity.com/paper/2212.11770/) (2022)
- [SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation](https://mlsanity.com/paper/2303.11048/) (2023)
- [3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs](https://mlsanity.com/paper/2209.07896/) (2022)
- [OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments](https://mlsanity.com/paper/2403.09412/) (2024)
- [Hydra-Multi: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams](https://mlsanity.com/paper/2304.13487/) (2023)
- [Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd](https://mlsanity.com/paper/2104.14177/) (2021)
- [Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics](https://mlsanity.com/paper/2208.02121/) (2022)
- [Bayesian Floor Field: Transferring people flow predictions across environments](https://mlsanity.com/paper/2208.10851/) (2022)
- [Collaborative Dynamic 3D Scene Graphs for Open-Vocabulary Urban Scene Understanding](https://mlsanity.com/paper/2503.08474/) (2025)
- [Situationally-aware Path Planning Exploiting 3D Scene Graphs](https://mlsanity.com/paper/2508.06283/) (2025)
- [Learning State-Space Models for Mapping Spatial Motion Patterns](https://mlsanity.com/paper/2309.00333/) (2023)

## Built on by (most cited first, 5 of 5)

- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Rheos: Modelling Continuous Motion Dynamics in Hierarchical 3D Scene Graphs](https://mlsanity.com/paper/2603.20239/) (2026)
- [GESTO: Human-Centric Spatio-Temporal Memory for Reasoning in Dynamic Scenes](https://mlsanity.com/paper/2608.10886/) (2026)
- [Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs](https://mlsanity.com/paper/2609.27467/) (2026)
- [A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs](https://mlsanity.com/paper/2610.08800/) (2026)

## Abstract (arXiv)

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