## 5 Conclusions  
This study proposed a method to address the degradation of segmentation performance in indoor construction environments caused by environmental complexity and the lack of standardized datasets. The approach integrates the iterative application of the Segment Anything Model (SAM), post-processing algorithms, and a label assignment algorithm to enhance segmentation quality without requiring additional training data. Through experimental study, we verified that the proposed method significantly improves segmentation quality in two key aspects. First, we quantitatively evaluated the exactness of label assignment within object boundaries. The proposed method recorded 27.4 percentage points (p.p.) and 16.8 p.p. higher Mean MIoU for horizontal (H-plane) and vertical (V-plane) surfaces, respectively, compared to Panoplane360 alone. Additionally, compared to a method that simply integrates a single execution of SAM, the proposed approach improved segmentation performance by 17.6 p.p. for the H-plane and 6.1 p.p. for the V-plane. Second, we qualitatively evaluated whether the assigned labels preserved accurate geometric information. Visual inspection of the plane segmentation masks confirmed that the proposed method reduced label omissions and improved boundary delineation. Additionally, the visualized 3D models reconstructed from the predicted labels confirmed that the segmented planes were spatially accurate and structurally consistent with the environment. These results suggest that the proposed method not only improves segmentation accuracy by refining object boundaries but also ensures that labels are appropriately assigned to maintain spatial consistency in indoor construction environments. Notably, the method enhances segmentation quality without requiring the construction of additional datasets for indoor construction, highlighting its practical significance. However, this study has certain limitations. First, while the proposed method improves segmentation accuracy, it does not consider computational efficiency. The iterative execution of SAM leads to high computational costs and increased processing time. Second, the performance of the method depends on parameter configurations, yet no systematic approach for determining optimal hyperparameters has been established. Several key hyperparameters--such as SAM's initial parameter values and adjustment increments, mask filtering thresholds, and label assignment thresholds--affect segmentation performance, but in this study, they were determined empirically. Third, the evaluation was limited to plane segmentation using Panoplane360, and the applicability of the method to other segmentation tasks and models was not explored. Further research is needed to verify the robustness of the approach across diverse segmentation tasks and deep learning models. Future studies should focus on improving computational efficiency by optimizing SAM's iterative execution process and developing a structured approach for hyperparameter tuning to enhance performance across different environments. Additionally, the applicability of the proposed method should be extended beyond plane segmentation with Panoplane360 to other panoramic segmentation tasks and related deep learning models for further performance validation. Through these efforts, the proposed method is expected to facilitate the expansion and advancement of panoramic segmentation technology in the construction domain. This is expected to improve segmentation performance and promote its broader adoption in real-world applications, such as construction progress monitoring, safety inspections, and spatial layout planning in indoor environments. Conceptualization, J.H. ; Methodology, J.H. ; Software, J.H. ; Formal analysis, J.H. ; Writing--original draft, J.H. ; Visualization, M.K. ; Supervision, S.Y. ; Project administration, T.K. ;Writing--review and editing, T.K. ; Funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.