自动从点云生成符合IFC标准的建筑信息模型，提升结构建模精度。
BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement
- 融合学习分割与拓扑感知几何重建，实现精准结构建模。
- 在DeKH和CV4AEC数据集上显著优于基于RANSAC的基线方法。
- 开源德国医院数据集，支持扫描转BIM研究与评估。
从建筑扫描自动生成建筑信息模型（BIM）是建筑与施工领域的重要挑战。本文提出一个模块化流水线，可从3D点云生成符合IFC标准的BIM。该混合方法结合基于学习的语义分割与拓扑感知几何重建，准确建模结构构件。提出vIoU，将体素重叠评估适配到扫描转BIM任务中，实现无需实例匹配的全局模型对比。公开发布德国医院数据集（DeKH），包含高分辨率点云、真实BIM及语义标注。在DeKH与CV4AEC数据集上的实验表明，该方法显著优于基于RANSAC的基线，展现出鲁棒性与可扩展性。
原文摘要 · Abstract (English)
Automatic generation of Building Information Models (BIM) from building scans is a key challenge in architecture and construction. We present a modular pipeline for generating IFC-compliant BIM from 3D point clouds. The hybrid approach combines learning-based semantic segmentation with topology-aware geometric reconstruction to model structural elements accurately. We propose vIoU, adapting voxel-based overlap evaluation to Scan-to-BIM by enabling holistic, instance-matching-free comparison of reconstructed and ground-truth models. We release the German Hospital dataset (DeKH), including high-resolution point clouds, ground truth BIMs, and semantic annotations. Experiments on DeKH and CV4AEC datasets show significant improvements over a RANSAC-based baseline, demonstrating robustness and scalability.
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