UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset
For researchers in 3D semantic segmentation, this benchmark addresses the lack of large-scale, geographically diverse facade datasets to evaluate and improve model robustness.
The paper introduces UnderOneFacade, the largest cross-country 3D facade benchmark with 2.7 billion annotated points, and shows that current segmentation methods achieve only up to 33 IoU on fine-grained tasks, highlighting poor cross-domain generalization.
Globally consistent semantic digital twins require centimeter-accurate and geographically transferable 3D facade segmentation. However, progress in facade parsing is limited by the lack of large-scale, standardized benchmarks for evaluating cross-domain generalization. Existing datasets are geographically narrow, semantically inconsistent, or insufficiently precise. We introduce UnderOneFacade, the largest cross-country and cross-continent 3D facade benchmark to date, comprising centimeter-accurate point clouds with hierarchical, harmonized, and architecturally grounded semantic labels totaling 2.7 billion annotated points. Through a systematic evaluation of representative point-, graph- and transformer-based architectures, we show that current methods struggle to recognize fine-grained architectural elements and degrade significantly across geographic domains, with the best models achieving only up to 33 IoU on the fine-grained LoFG3 benchmark. By combining geometric precision with standardized semantics at unprecedented scale, UnderOneFacade establishes a rigorous benchmark for developing robust and transferable 3D segmentation models. The dataset, evaluation scripts, and pretrained models will be released upon publication.