The internal or external attribute of building information modeling (BIM) objects is vital information for many BIM-based engineering analyses such as building energy analysis and cost estimation. Unfortunately, such information is often inaccurate, incomplete, or missing entirely in most BIM models. Manually checking, correcting, or inputting this data for large-scale BIM models can be time-consuming, laborious, and error-prone. This study proposes a two-stage ray tracing algorithm to automatically identify external BIM objects, based on the idea that external objects of a building can be viewed from somewhere outside the building. The first-stage ray tracing samples viewpoints from the six faces of an offset axis-aligned bounding box (AABB) of the building and emits a ray for each viewpoint to detect relevant external objects. The second-stage ray tracing recursively searches for any remaining external objects from the view of the external objects that have been detected in the previous round of ray tracing. Both stages are carefully designed for efficiency. Furthermore, a two-tier AABB tree is introduced to spatially index building objects on both model and object levels to accelerate relevant geometry operations. The proposed algorithm is validated with one synthetic and two large-scale real-world building models. The results show that all the external objects in the three models are accurately and efficiently identified, and the two-tier spatial indexing and other acceleration techniques improve the algorithm's efficiency significantly.

中文翻译：

---

一种用于自动识别建筑信息模型中外部建筑对象的两阶段递归光线追踪算法

建筑信息模型 (BIM) 对象的内部或外部属性是许多基于 BIM 的工程分析（例如建筑能耗分析和成本估算）的重要信息。不幸的是，在大多数 BIM 模型中，此类信息通常不准确、不完整或完全缺失。为大型 BIM 模型手动检查、更正或输入这些数据可能既费时又费力且容易出错。本研究提出了一种两阶段的光线追踪算法来自动识别外部 BIM 对象，基于可以从建筑物外部的某处查看建筑物的外部对象的想法。第一阶段光线追踪从建筑物的偏移轴对齐边界框 (AABB) 的六个面采样视点，并为每个视点发射一条光线以检测相关的外部对象。第二阶段光线追踪从上一轮光线追踪中检测到的外部对象的视图中递归搜索任何剩余的外部对象。两个阶段都经过精心设计以提高效率。此外，引入了两层 AABB 树来在模型和对象级别上对建筑对象进行空间索引，以加速相关的几何操作。所提出的算法通过一个合成模型和两个大型真实世界建筑模型进行了验证。结果表明，三种模型中的所有外部对象都被准确高效地识别出来，两层空间索引等加速技术显着提高了算法的效率。