UR-MAT: A Multimodal, Material-Aware Synthetic Dataset of Urban Scenarios

摘要

We present UR-MAT, a multimodal, material-aware synthetic dataset for urban scene understanding and physics-based simulation. UR-MAT comprises seven diverse outdoor environments, ranging from historic districts to modern office areas, reconstructed from OpenStreetMap data and procedurally enhanced in Unreal Engine. Each scene includes semantically structured 3D meshes and physically based rendering (PBR) materials annotated with electromagnetic properties such as permittivity, reflectance, and attenuation. The dataset provides spatially aligned RGB images, material segmentation masks, depth maps, point clouds, camera poses, and 3D mesh files in .glb format. All data is generated through a deterministic, reproducible pipeline integrating OSM2World, Unreal Engine, and UnrealCV. UR-MAT supports a wide range of research tasks, from semantic segmentation and 3D reconstruction to material-aware electromagnetic simulation (e.g., mmWave propagation). We also release two utility scripts to extract mesh-material relationships and assign physical metadata, enabling dataset extension and reproducibility. By bridging computer vision and physical modeling, UR-MAT serves as a testbed for multimodal AI and signal-aware urban simulation.