# Collaborative Dynamic 3D Scene Graphs for Open-Vocabulary Urban Scene Understanding

arXiv: https://arxiv.org/abs/2503.08474 · HTML: https://mlsanity.com/paper/2503.08474/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2025.

## Built on by (most cited first, 4 of 4)

- [GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving](https://mlsanity.com/paper/2511.11266/) (2025)
- [Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics](https://mlsanity.com/paper/2512.11903/) (2025)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search](https://mlsanity.com/paper/2603.05642/) (2026)

## Abstract (arXiv)

Mapping and scene representation are fundamental to reliable planning and navigation in mobile robots. While purely geometric maps using voxel grids allow for general navigation, obtaining up-to-date spatial and semantically rich representations that scale to dynamic large-scale environments remains challenging. In this work, we present CURB-OSG, an open-vocabulary dynamic 3D scene graph engine that generates hierarchical decompositions of urban driving scenes via multi-agent collaboration. By fusing the camera and LiDAR observations from multiple perceiving agents with unknown initial poses, our approach generates more accurate maps compared to a single agent while constructing a unified open-vocabulary semantic hierarchy of the scene. Unlike previous methods that rely on ground truth agent poses or are evaluated purely in simulation, CURB-OSG alleviates these constraints. We evaluate the capabilities of CURB-OSG on real-world multi-agent sensor data obtained from multiple sessions of the Oxford Radar RobotCar dataset. We demonstrate improved mapping and object prediction accuracy through multi-agent collaboration as well as evaluate the environment partitioning capabilities of the proposed approach. To foster further research, we release our code and supplementary material at https://ov-curb.cs.uni-freiburg.de.