Intelligent virtual trial assembly of prefabricated frame structures for large and complex construction scenes

Virtual Trial Assembly (VTA) is increasingly employed in engineering projects to reduce costs and improve assembly efficiency. However, the complexity of construction sites and the high precision requirements for assembly make manual point cloud segmentation both time-consuming and ineffective in accurately extracting assembly features. In order to solve the problem, this paper proposes a VTA method based on semantic segmentation and 3D bounding box estimation. It enables efficient semantic segmentation of large-scale point clouds and calculates precise assembly feature points based on the estimated 3D bounding boxes, facilitating optimal assembly analysis of all components. The results show that this method enables automated VTA analysis of assembly components and guides physical assembly. This research contributes to improving the monitoring of the entire construction process, ensuring efficient and precise assembly of building components.