Resumen
Building information models (BIMs) offer advantages, such as visualization and collaboration, making them widely used in the management of existing buildings. Currently, most BIMs for existing indoor spaces are manually created, consuming a significant amount of manpower and time, severely impacting the efficiency of building operations and maintenance management. To address this issue, this study proposes an automated reconstruction method for an indoor scene BIM based on a feature-enhanced point transformer and an octree. This method enhances the semantic segmentation performance of point clouds by using feature position encoding to strengthen the point transformer network. Subsequently, the data are partitioned into multiple segments using an octree, collecting the geometric and spatial information of individual objects in the indoor scene. Finally, the BIM is automatically reconstructed using Dynamo in Revit. The research results indicate that the proposed feature-enhanced point transformer algorithm achieves a high segmentation accuracy of 71.3% mIoU on the S3DIS dataset. The BIM automatically generated from the field point cloud data, when compared to the original data, has an average error of ±1.276 mm, demonstrating a good reconstruction quality. This method achieves the high-precision, automated reconstruction of the indoor BIM for existing buildings, avoiding extensive manual operations and promoting the application of BIMs for the maintenance processes of existing buildings.