BuildingWorld: A Structured 3D Building Dataset for Urban Foundation Models
Shangfeng Huang, Ruisheng Wang, Xin Wang

TL;DR
BuildingWorld is a large, diverse 3D building dataset from multiple continents, designed to improve urban foundation models' generalizability and support various research applications in 3D urban modeling.
Contribution
The paper introduces BuildingWorld, a comprehensive, globally representative 3D building dataset with millions of models and a virtual city for diverse training data generation.
Findings
Provides about five million LOD2 building models from diverse regions.
Includes real and simulated airborne LiDAR point clouds for research.
Introduces Cyber City for unlimited training data generation.
Abstract
As digital twins become central to the transformation of modern cities, accurate and structured 3D building models emerge as a key enabler of high-fidelity, updatable urban representations. These models underpin diverse applications including energy modeling, urban planning, autonomous navigation, and real-time reasoning. Despite recent advances in 3D urban modeling, most learning-based models are trained on building datasets with limited architectural diversity, which significantly undermines their generalizability across heterogeneous urban environments. To address this limitation, we present BuildingWorld, a comprehensive and structured 3D building dataset designed to bridge the gap in stylistic diversity. It encompasses buildings from geographically and architecturally diverse regions -- including North America, Europe, Asia, Africa, and Oceania -- offering a globally representative…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsRemote Sensing and LiDAR Applications · 3D Modeling in Geospatial Applications · 3D Shape Modeling and Analysis
