  Precise segmentation of architectural structures provides detailed
information about various building components, enhancing our understanding and
interaction with our built environment. Nevertheless, existing outdoor 3D point
cloud datasets have limited and detailed annotations on architectural exteriors
due to privacy concerns and the expensive costs of data acquisition and
annotation. To overcome this shortfall, this paper introduces a
semantically-enriched, photo-realistic 3D architectural models dataset and
benchmark for semantic segmentation. It features 4 different building purposes
of real-world buildings as well as an open architectural landscape in Hong
Kong. Each point cloud is annotated into one of 14 semantic classes.
