# Collaborative Dynamic 3D Scene Graphs for Automated Driving

arXiv: https://arxiv.org/abs/2309.06635 · HTML: https://mlsanity.com/paper/2309.06635/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2023.

## Built on by (most cited first, 30 of 34)

- [Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation](https://mlsanity.com/paper/2403.17846/) (2024)
- [GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering](https://mlsanity.com/paper/2412.14480/) (2024)
- [Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies](https://mlsanity.com/paper/2312.11713/) (2023)
- [OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments](https://mlsanity.com/paper/2403.09412/) (2024)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [Universal Scene Graph Generation](https://mlsanity.com/paper/2503.15005/) (2025)
- [Articulated 3D Scene Graphs for Open-World Mobile Manipulation](https://mlsanity.com/paper/2602.16356/) (2026)
- [MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning](https://mlsanity.com/paper/2505.03035/) (2025)
- [MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning](https://mlsanity.com/paper/2512.16909/) (2025)
- [Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception](https://mlsanity.com/paper/2407.18145/) (2024)
- [GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving](https://mlsanity.com/paper/2511.11266/) (2025)
- [Schema-Guided Scene-Graph Reasoning based on Multi-Agent Large Language Model System](https://mlsanity.com/paper/2502.03450/) (2025)
- [S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM](https://mlsanity.com/paper/2502.18044/) (2025)
- [Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics](https://mlsanity.com/paper/2512.11903/) (2025)
- [FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning](https://mlsanity.com/paper/2604.03696/) (2026)
- [Learning High-level Semantic-Relational Concepts for SLAM](https://mlsanity.com/paper/2310.00401/) (2023)
- [Long-Term Planning Around Humans in Domestic Environments with 3D Scene Graphs](https://mlsanity.com/paper/2503.09173/) (2025)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks](https://mlsanity.com/paper/2409.11972/) (2024)
- [GraphPad: Inference-Time 3D Scene Graph Updates for Embodied Question Answering](https://mlsanity.com/paper/2506.01174/) (2025)
- [Rheos: Modelling Continuous Motion Dynamics in Hierarchical 3D Scene Graphs](https://mlsanity.com/paper/2603.20239/) (2026)
- [Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image](https://mlsanity.com/paper/2410.15312/) (2024)
- [Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks](https://mlsanity.com/paper/2504.12817/) (2025)
- [M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments](https://mlsanity.com/paper/2508.17044/) (2025)
- [Maps for Autonomous Driving: Full-process Survey and Frontiers](https://mlsanity.com/paper/2509.12632/) (2025)
- [From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP](https://mlsanity.com/paper/2606.26535/) (2026)
- [Learning Lane Graphs from Aerial Imagery Using Transformers](https://mlsanity.com/paper/2407.05687/) (2024)
- [CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving](https://mlsanity.com/paper/2512.03510/) (2025)
- [Semantic Enrichment of CAD-Based Industrial Environments via Scene Graphs for Simulation and Reasoning](https://mlsanity.com/paper/2601.06415/) (2026)
- [NoPA: Non-Parametric Online 3D Scene Graph Generation](https://mlsanity.com/paper/2607.00529/) (2026)

## Abstract (arXiv)

Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building an actionable hierarchical semantic representation of urban dynamic scenes and processing information from multiple agents are still challenging problems. In this work, we present Collaborative URBan Scene Graphs (CURB-SG) that enable higher-order reasoning and efficient querying for many functions of automated driving. CURB-SG leverages panoptic LiDAR data from multiple agents to build large-scale maps using an effective graph-based collaborative SLAM approach that detects inter-agent loop closures. To semantically decompose the obtained 3D map, we build a lane graph from the paths of ego agents and their panoptic observations of other vehicles. Based on the connectivity of the lane graph, we segregate the environment into intersecting and non-intersecting road areas. Subsequently, we construct a multi-layered scene graph that includes lane information, the position of static landmarks and their assignment to certain map sections, other vehicles observed by the ego agents, and the pose graph from SLAM including 3D panoptic point clouds. We extensively evaluate CURB-SG in urban scenarios using a photorealistic simulator. We release our code at http://curb.cs.uni-freiburg.de.