## 1 Introduction  
A BIM is a digital building information modeling technology that integrates multi-dimensional data, such as geometric shape, spatial relationships, material, component information, construction sequence, cost, and time of building projects into an integrated model. It is widely used in various stages, such as architectural design [1; 2], construction [3], equipment management [4], and operation and maintenance [5]. Meanwhile, a BIM can effectively assist in the operation and maintenance of construction projects, promoting collaboration and communication among all areas involved in the project [6]. However, there are currently many difficulties when reconstructing indoor BIM models of existing buildings, such as indoor occlusion, high complexity [7], and the interrelationships between indoor target objects [8].  
At present, there are three main methods for indoor BIM reconstructions, namely, image-based 3D reconstructions [9], traditional geometric modeling techniques [10], and point cloud-based 3D reconstruction methods [11]. The image-based 3D reconstruction method requires a large amount of image data from different perspectives [12]. Traditional geometric modeling has a high accuracy, but requires professional modeling personnel and a longer time to conduct [13]. The point cloud-based 3D reconstruction method is widely used for indoor scene 3D reconstructions, for example, using a geometric feature enhancement [14] and combining images with point clouds [15] to achieve the BIM reconstruction of indoor scenes. However, overall, most indoor BIMs are manually created by modelers referring to point clouds or on-site photos [15]. Due to the large volume of point cloud data in indoor environments and the complex spatial layout, it is difficult to model target objects in complex indoor scenes. A method based on point cloud data that can achieve rapid, accurate, and automatic BIMs of indoor scenes is still lacking, to date. This seriously hinders the application of BIMs in the operation and maintenance stages of existing buildings, and limits the development of building intelligence.  
Considering the problems of low efficiency and low automation in BIM reconstructions of existing indoor scenes, this paper proposes an automated reconstruction method for BIMs of indoor scenes based on the feature-enhanced point transformer and octree algorithms. This study aims to achieve the automated semantic segmentation of indoor point cloud data through deep learning and further complete the reconstruction of the point cloud data into BIMs. This article studies the application of point cloud data to BIM indoor 3D reconstruction technology and improves the performance of the point transformer network through a feature position encoding enhancement to achieve point cloud semantic segmentation. Subsequently, the data are divided into entities using an octree to obtain the geometric and spatial information for the target subjects in the indoor scene. Finally, the BIM 3D reconstruction is completed by building Dynamo in Revit, and the accuracy of the automatically reconstructed BIM model is verified by combining the original data.  
The method proposed in this study enables the rapid, accurate, and automated construction of BIMs through on-site point cloud data, avoiding the manual reconstruction process of traditional BIMs and enhancing BIM reconstruction efficiency. Addressing the challenges posed by the vast and complex spatial layout of the point cloud data for existing building interiors, it achieves the precise segmentation of intricate indoor point cloud data and the automated reconstruction of high-quality BIMs. This approach provides data support for the application of BIMs in the operation and maintenance of existing buildings.