## 5 Conclusions  
In response to the insufficient research on the low efficiency and automation of BIM reconstructions for existing indoor scenes, this study proposed an automated reconstruction method for an indoor scene BIM based on a feature-enhanced point transformer and octree algorithms. Using the point cloud data collected from a conference room, the study achieved the automated reconstruction of complex indoor scenes in existing buildings, validating the feasibility of the proposed method. The feature-enhanced point Transformer algorithm presented in the study achieved a high segmentation accuracy of 71.3% mIoU. The average error of the reconstructed BIM was effectively controlled within 2 mm, indicating the high precision of the reconstruction results. The proposed method addressed the challenges of dealing with large and spatially complex point cloud data in existing building interiors, enabling the high-precision and automated reconstructions of indoor BIMs. This eliminated the need for extensive manual operations, thereby enhancing the efficiency of BIM reconstructions. The study contributed valuable data support for the application of BIMs for the operation and maintenance management of existing buildings. The feature-enhanced point transformer algorithm introduced in this study exhibited higher accuracy results than traditional algorithms, providing robust algorithmic support for point cloud data segmentation practices.  
Figure 21: The precise registration results using ICP.  
However, the study presented certain limitations. The semantic segmentation network in this research was trained on the SSDIS dataset, which comprised only 13 classes of point cloud labels, limiting its ability to fully cover all types of elements in various indoor scenes. Moreover, the BIM often included rich attributes, and relying on a single-point cloud dataset to achieve semantic segmentation resulted in limited information being obtained, constraining the development of point clouds for building information modeling. Future research should focus on establishing more generalized and detailed point cloud data training sets. Additionally, exploring how to enhance the accuracy and reliability of BIM reconstructions by integrating different data sources, such as images, point clouds, and RFID tags, and enriching the attribute information of elements, is crucial to better support the development of building information models.  
Conceptualization, J.C. and Z.X. ([PERSON]); methodology, J.C., Y.L., Z.X. ([PERSON]) and Z.X. ([PERSON]); software, Z.X. ([PERSON]) and S.W.; validation, Y.L., Z.X. ([PERSON]), S.W. and Z.X. ([PERSON]); formal analysis, J.C. and Z.X. ([PERSON]); investigation, Y.L., Z.X. ([PERSON]), [PERSON] and Z.X. ([PERSON]); writing--original draft preparation, [PERSON], Y.L., Z.X. ([PERSON]) and Z.X. ([PERSON]); writing--review and editing, J.C., Y.L., S.W. and Z.X. ([PERSON]); visualization, Y.L. and Z.X. ([PERSON]); funding acquisition, Z.X. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (72071043), the Natural Science Foundation of Jiangsu Province (BK20201280), and the Ministry of Education of Humanities and Social Science Project in China (20 YJAZH114).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors specially thank all the survey participants and reviewers of the paper.  
Author [PERSON] and Author [PERSON] were employed by the company China Railway Siyuan Survey and Design Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.