Hierarchical 3D Scene Graphs Construction Outdoors

Jon Nyffeler, Federico Tombari, Daniel Barath; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 26817-26826

Understanding and structuring outdoor environments in 3D is critical for numerous applications, including robotics, urban planning, and autonomous navigation. In this work, we propose a pipeline to construct hierarchical 3D scene graphs from outdoor data, consisting of posed images and 3D reconstructions. Our approach systematically extracts and organizes objects and their subcomponents, enabling representations that span from entire buildings to their facades and individual windows. By leveraging geometric and semantic relationships, our method efficiently groups objects into meaningful hierarchies while ensuring robust spatial consistency. We integrate efficient feature extraction, hierarchical object merging, and relationship inference to generate structured scene graphs that capture both global and local dependencies. Our approach scales to large outdoor environments while maintaining efficiency, and we demonstrate its effectiveness on real-world datasets. We also demonstrate that these constructed outdoor scene graphs are beneficial for downstream applications, such as 3D scene alignment. The code is available on GitHub.