A framework for semi-automatically identifying fully occluded objects in 3D models: Towards comprehensive construction design review in virtual reality

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.

Automated summary

The paper proposes a semi-automatic occlusion detection framework that can identify occluded objects in 3D construction models, enhancing the efficiency of construction design review applications. The validation results indicate that point cloud-based algorithms are suitable for this classification task.