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