# Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data
## 5 Conclusions  
This study analyzed the limitations of previous studies and generated improvements by presenting a new algorithm for the 3D geometric representation of indoor environments. The mean error of the 3D geometric representation generated through the case study is 0.407719\({}^{\circ}\), which is an error of 16.3 mm when considering the vertical height of the building interior at 2.3 m. Given the \(\pm\)30 mm error caused by the feature of the BLS used in this study, the algorithm proposed in this study proves to be suitable for generating the 3D indoor geometric representation.  
The benefits of this study in the AEC industry are as follows; first, the 3D geometric representation derived by the proposed algorithm can improve the efficiency of as-built building information modeling (BIM) generation. In the process of BIM generation, the executor who generates the structural members as BIM compares real structural member and detected members to increase the accuracy. Based on the accuracy of the proposed algorithm, the executor can use the information derived by this study for generating as-built BIM faster than the traditional method using iterative checking. In particular, a generation error caused by missing points is reduced by minimizing additional processes for confirming the existence of a structural member. Second, the result of this study supports decision making for retrofitting the interior building condition. The reconstructed members generated by the proposedalgorithm can distinguish movable objects from an indoor environment to optimize the position of members. An owner who wants to retrofit an existing building decides the location of furniture by using the data derived from the indoor PCD. Lastly, the proposed algorithm-based 3D scanning makes it possible to assess several buildings using MLS. The existing methods for reconstructing the indoor environment use fixed 3D scanning methods to acquire rich data that have sufficient information for detecting members. In contrast, the proposed algorithm uses the topological feature of members, which compensates for the lack of information in MLS-based scanning. The moving scanning can acquire the indoor PCD faster than the fixed method, thus more of the indoor environment is obtained in the same scanning time.  
The current limitations of this algorithm are that it is difficult to use the proposed methods in irregular structures and to accurately determine the sizes and positions of openings in a building such as windows and doors. Future research will be necessary to decrease errors in generating openings and increase the applicability to irregular objects by diversifying the types of recognizable objects.  
[PERSON] and [PERSON] analyzed the result; [PERSON] and [PERSON] collected the data; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] offered advice on this study; [PERSON] edited the paper. All authors read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A4A1026027).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.