  We introduce BuildingNet: (a) a large-scale dataset of 3D building models
whose exteriors are consistently labeled, (b) a graph neural network that
labels building meshes by analyzing spatial and structural relations of their
geometric primitives. To create our dataset, we used crowdsourcing combined
with expert guidance, resulting in 513K annotated mesh primitives, grouped into
292K semantic part components across 2K building models. The dataset covers
several building categories, such as houses, churches, skyscrapers, town halls,
libraries, and castles. We include a benchmark for evaluating mesh and point
cloud labeling. Buildings have more challenging structural complexity compared
to objects in existing benchmarks (e.g., ShapeNet, PartNet), thus, we hope that
our dataset can nurture the development of algorithms that are able to cope
with such large-scale geometric data for both vision and graphics tasks e.g.,
3D semantic segmentation, part-based generative models, correspondences,
texturing, and analysis of point cloud data acquired from real-world buildings.
Finally, we show that our mesh-based graph neural network significantly
improves performance over several baselines for labeling 3D meshes.
