## 5 Conclusions  
In this study, the authors proposed a novel method to determine the indoor clutter objects based on the assumptions that (a) the structural elements stretched from the floor to the ceiling and (b) the indoor clutter objects existed on the floor and did not stretch to the ceiling. The proposed method includes the removal of the floor and ceiling, voxel-grid downsampling, DBSCAN, SOR filter application, and an unsupervised radius-based nearest neighbor search algorithm.  
The experiment with the six scan datasets from actual sites showed higher accuracy, precision, recall, and F1 scores than conventional methods in identifying indoor clutter objects. Specifically, the proposed method achieved an accuracy of 0.94, a precision of 0.97, a recall of 0.90, and an F1 score of 0.94. When compared to the Auto-Classify Indoor function of commercial point-cloud processing software, the proposed method showed higher performances by 0.10, 0.09, 0.07, and 0.09 in terms of accuracy, precision, recall, and F1 score, respectively.  
The contributions of this study are as follows:  
Figure 14: Examples of erroneous classifications of the point cloud at the window and door. (**a**) Point cloud classification results near the door of lecture room 2; (**b**) point cloud classification results near the windows of apartment 2 (red points: structural elements; cyan points: indoor clutter objects).  
Figure 13: Comparison of images of the apartment 2 dataset and the actual target object. (**a**) Point cloud data that Auto-Classify Indoor function classified as structural elements; (**b**) photograph of an actual target object.  
* The proposed method can accurately determine and remove indoor clutter objects with higher performance than commercial software;
* The proposed method can extract an appropriate x-y plane that represents structural elements, including inner walls and columns;
* The proposed method can identify indoor clutter objects among objects with similar geometrical features to structural elements;
* The parameters of DBSCAN, the SOR filter, and the unsupervised radius-based nearest neighbor search algorithm used in the proposed method are automatically determined by the voxel size.  
However, the proposed method has some limitations in accurately determining the structural elements near windows and doors. In the future, the authors plan to improve this method to more accurately determine the structural elements at all locations, including regions near windows and doors. Furthermore, we will also consider pipes that are installed horizontally. The authors will adopt the proposed method in the Scan-to-BIM process to improve point cloud semantic segmentation results. The proposed method has the potential to be applied in various fields, such as architecture, civil engineering, and interior design.  
Conceptualization, S.-J.B. and J.-Y.K.; Methodology, S.-J.B. and J.-Y.K.; Software, S.-J.B.; Validation, S.-J.B.; Formal analysis, S.-J.B.; Investigation, S.-J.B.; Resources, S.-J.B.; Writing--original draft, S.-J.B.; Visualization, S.-J.B.; Writing--review and editing, J.-Y.K.; Supervision, J.-Y.K.; Project administration, J.-Y.K.; Funding acquisition, J.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by grant No. 1615012983 from the Digital-based Building Construction and Safety Supervision Technology Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government.  
Not applicable.  
The authors declare no conflict of interest.