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We present the UrbanBIS benchmark for large-scale 3D urban understanding,supporting practical urban-level semantic and building-level instancesegmentation. UrbanBIS comprises six real urban scenes, with 2.5 billionpoints, covering a vast area of 10.78 square kilometers and 3,370 buildings,captured by 113,346 views of aerial photogrammetry. Particularly, UrbanBISprovides not only semantic-level annotations on a rich set of urban objects,including buildings, vehicles, vegetation, roads, and bridges, but alsoinstance-level annotations on the buildings. Further, UrbanBIS is the first 3Ddataset that introduces fine-grained building sub-categories, considering awide variety of shapes for different building types. Besides, we propose B-Seg,a building instance segmentation method to establish UrbanBIS. B-Seg adopts anend-to-end framework with a simple yet effective strategy for handlinglarge-scale point clouds. Compared with mainstream methods, B-Seg achievesbetter accuracy with faster inference speed on UrbanBIS. In addition to thecarefully-annotated point clouds, UrbanBIS provides high-resolutionaerial-acquisition photos and high-quality large-scale 3D reconstructionmodels, which shall facilitate a wide range of studies such as multi-viewstereo, urban LOD generation, aerial path planning, autonomous navigation, roadnetwork extraction, and so on, thus serving as an important platform for manyintelligent city applications.

UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation