提出建筑立面泛化等级与大规模点云分割数据集，推动城市数字孪生发展。
ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset
- 定义建筑立面泛化等级（LoFG），分层构建符合国际标准的分类体系
- 发布迄今最大3D立面语义分割数据集，含6亿多标注点，支持5/15类细分
- 提供基准模型评估与挑战分析，助力高鲁棒性立面分割方法研究
立面语义分割是摄影测量与计算机视觉中的长期挑战。尽管过去几十年涌现出多种立面分割方法，但普遍缺乏全面的立面类别和覆盖建筑多样性的数据。本文提出建筑立面泛化等级（LoFG），基于国际城市建模标准设计的新型分层类别体系，确保与真实复杂场景类别兼容，并支持方法统一比较。为实现LoFG，我们发布了目前规模最大的语义3D立面分割数据集，包含6.01亿个标注点，对应LoFG2的5类与LoFG3的15类。此外，我们对基准语义分割方法在新类别与数据上的性能进行了分析，并讨论了当前未解决的立面分割挑战。我们认为ZAHA将推动3D立面语义分割方法的进一步发展，为构建鲁棒的城市数字孪生提供关键支持。
原文摘要 · Abstract (English)
Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the architectural variability. In ZAHA, we introduce Level of Facade Generalization (LoFG), novel hierarchical facade classes designed based on international urban modeling standards, ensuring compatibility with real-world challenging classes and uniform methods' comparison. Realizing the LoFG, we present to date the largest semantic 3D facade segmentation dataset, providing 601 million annotated points at five and 15 classes of LoFG2 and LoFG3, respectively. Moreover, we analyze the performance of baseline semantic segmentation methods on our introduced LoFG classes and data, complementing it with a discussion on the unresolved challenges for facade segmentation. We firmly believe that ZAHA shall facilitate further development of 3D facade semantic segmentation methods, enabling robust segmentation indispensable in creating urban digital twins.
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