构建620万栋3D建筑合成数据集，支持智能建模与语义一致性生成
SYNBUILD-3D: A large, multi-modal, and semantically rich synthetic dataset of 3D building models at Level of Detail 4
- 三模态合成：线框图、平面图、屋顶点云，每栋建筑含完整语义标注
- 覆盖超620万栋住宅建筑，精度达LoD 4，支持自动化3D建模生成
- 适合建筑生成、智能设计、数字孪生等领域的研究者使用
3D建筑模型在建筑、能源模拟和导航等领域至关重要，但因缺乏大规模公开标注数据集，自动构建准确且语义丰富的3D建筑仍面临挑战。受计算机视觉中合成数据成功的启发，我们提出SYNBUILD-3D，一个包含超过620万栋住宅建筑的大型、多样、多模态合成数据集，其精度达到层级细节（LoD）4。每栋建筑以三种模态表示：语义丰富且结构完整的LoD 4 3D线框图（模态一）、对应平面图（模态二）及类似激光雷达的屋顶点云（模态三）。线框图的语义标注源自平面图，包含房间、门、窗等信息。该数据集的三模态特性可支持未来开发新型生成式AI算法，实现基于预设平面布局与屋顶几何的自动化3D建筑生成，并保证语义与几何的一致性。数据集与代码样本已公开于https://github.com/kdmayer/SYNBUILD-3D。
原文摘要 · Abstract (English)
3D building models are critical for applications in architecture, energy simulation, and navigation. Yet, generating accurate and semantically rich 3D buildings automatically remains a major challenge due to the lack of large-scale annotated datasets in the public domain. Inspired by the success of synthetic data in computer vision, we introduce SYNBUILD-3D, a large, diverse, and multi-modal dataset of over 6.2 million synthetic 3D residential buildings at Level of Detail (LoD) 4. In the dataset, each building is represented through three distinct modalities: a semantically enriched 3D wireframe graph at LoD 4 (Modality I), the corresponding floor plan images (Modality II), and a LiDAR-like roof point cloud (Modality III). The semantic annotations for each building wireframe are derived from the corresponding floor plan images and include information on rooms, doors, and windows. Through its tri-modal nature, future work can use SYNBUILD-3D to develop novel generative AI algorithms that automate the creation of 3D building models at LoD 4, subject to predefined floor plan layouts and roof geometries, while enforcing semantic-geometric consistency. Dataset and code samples are publicly available at https://github.com/kdmayer/SYNBUILD-3D.
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