Virtual Reality (VR)-based construction design review applications have shown potential to enhance user performance in many research projects and experiments. Currently, visualizing occluded objects in VR is a challenge, and this function is indispensable for construction design review and coordination. This paper proposes an occlusion detection framework that semi-automatically identifies occluded objects in 3D construction models. The framework determines the visibility status of an object by converting the object to a point cloud and comparing the point cloud to the virtual laser scanning result of the original model. It exports models that are interoperable with VR development software so that visualization effects can be easily employed to occluded objects. The authors validated the framework using two building information models. The algorithm achieved a recall rate of 90.30% and a precision rate of 75.05% in a gasoline refinery facility model. It reached a higher 98.06% recall rate and a 97.53% precision rate in an academic building model. This paper contributes to the body of knowledge by proposing a semi-automatic occlusion detection framework and validating that point cloud-based algorithms are appropriate for this classification task.

在许多研究项目和实验中，基于虚拟现实 (VR) 的施工设计审查应用程序已显示出提高用户性能的潜力。目前，在 VR 中可视化被遮挡的物体是一个挑战，这个功能对于施工设计审查和协调是必不可少的。本文提出了一种遮挡检测框架，可以半自动识别 3D 构造模型中的遮挡对象。该框架通过将对象转换为点云并将点云与原始模型的虚拟激光扫描结果进行比较来确定对象的可见性状态。它导出可与 VR 开发软件互操作的模型，以便可以轻松地将可视化效果应用于被遮挡的对象。作者使用两个建筑信息模型验证了该框架。该算法在汽油炼油厂模型中实现了 90.30% 的召回率和 75.05% 的准确率。它在学术建筑模型中达到了更高的 98.06% 召回率和 97.53% 的准确率。本文通过提出半自动遮挡检测框架并验证基于点云的算法是否适用于该分类任务，为知识体系做出了贡献。