Dataset, Benchmark for Learning Exterior Architectural Structures from Point Clouds
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.
Variants: ARCH2S
This dataset is used in 1 benchmark:
No recent benchmark submissions available for this dataset.
No papers with results on this dataset found.