构建1.2万栋带丰富元数据的精确多层建筑三维模型库
BuildingBRep-11K: Precise Multi-Storey B-Rep Building Solids with Rich Layout Metadata
- 基于规则生成器自动构建符合设计规范的建筑体素
- 可精准预测楼层数、房间数与平均面积，误差极低
- 适合建筑生成、质量评估等方向研究者使用
随着人工智能发展，大规模三维建筑的自动生成成为热点，但训练仍需大量清洁且标注丰富的数据。本文提出BuildingBRep-11K，包含11,978栋2至10层的多层建筑（约10 GB），由遵循建筑学原则的形状语法管道生成。每个样本包含几何精确的B-rep实体（覆盖楼层、墙体、楼板及规则开口），并附带快速加载的.npy元数据文件，记录每层详细参数。生成过程融入空间尺度、采光优化与室内布局约束，并通过多阶段筛选（剔除布尔运算失败、过小房间与极端长宽比）确保符合建筑标准。为验证数据集可学习性，我们训练了两个轻量级PointNet基线：(i) 多属性回归——单编码器从4000点云预测楼层数、总房间数、每层向量与平均面积，在100个未见建筑上实现0.37层的MAE（87%在±1内）、5.7间房间的MAE、3.2 m²的平均面积MAE；(ii) 缺陷检测——同一骨干网络分类为良品或缺陷，在平衡的100模型集上达到54%准确率，召回82%真实缺陷，精度53%（41真阳性，9假阴性，37假阳性，13真阴性）。实验表明BuildingBRep-11K具备可学习性且对几何回归与拓扑质量评估均具挑战性。
原文摘要 · Abstract (English)
With the rise of artificial intelligence, the automatic generation of building-scale 3-D objects has become an active research topic, yet training such models still demands large, clean and richly annotated datasets. We introduce BuildingBRep-11K, a collection of 11 978 multi-storey (2-10 floors) buildings (about 10 GB) produced by a shape-grammar-driven pipeline that encodes established building-design principles. Every sample consists of a geometrically exact B-rep solid-covering floors, walls, slabs and rule-based openings-together with a fast-loading .npy metadata file that records detailed per-floor parameters. The generator incorporates constraints on spatial scale, daylight optimisation and interior layout, and the resulting objects pass multi-stage filters that remove Boolean failures, undersized rooms and extreme aspect ratios, ensuring compliance with architectural standards. To verify the dataset's learnability we trained two lightweight PointNet baselines. (i) Multi-attribute regression. A single encoder predicts storey count, total rooms, per-storey vector and mean room area from a 4 000-point cloud. On 100 unseen buildings it attains 0.37-storey MAE (87 \% within $\pm1$), 5.7-room MAE, and 3.2 m$^2$ MAE on mean area. (ii) Defect detection. With the same backbone we classify GOOD versus DEFECT; on a balanced 100-model set the network reaches 54 \% accuracy, recalling 82 \% of true defects at 53 \% precision (41 TP, 9 FN, 37 FP, 13 TN). These pilots show that BuildingBRep-11K is learnable yet non-trivial for both geometric regression and topological quality assessment
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