构建全球最大跨域立面语义分割数据集，推动高精度3D建模发展
UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset
- 构建跨国家跨大陆的27亿点三维立面数据集，标签统一且精准
- 现有模型在细粒度识别上表现差，跨区域性能下降明显，最佳仅达33 IoU
- 适合做3D场景理解、数字孪生和可迁移性研究的团队使用
全球一致的语义数字孪生需要厘米级精度且地理可迁移的3D立面分割。然而，立面解析的发展受限于缺乏大规模、标准化的跨域泛化评估基准。现有数据集地理范围窄、语义不一致或精度不足。我们提出UnderOneFacade，目前最大规模的跨国、跨大陆3D立面基准数据集，包含27亿个标注点的厘米级精度点云，带有层级化、统一化且基于建筑学的语义标签。通过对代表性点、图与变压器架构的系统评估，发现当前方法难以识别细粒度建筑元素，跨地理域性能显著下降，最佳模型在细粒度LoFG3基准上仅达33 IoU。通过结合几何精度与标准化语义的空前规模，UnderOneFacade为开发鲁棒且可迁移的3D分割模型建立了严格基准。数据集、评估脚本及预训练模型将在发表后公开。
原文摘要 · Abstract (English)
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.
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