# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 5 Conclusions  
This study was initiated to address the lack of standardized guidelines for UAV-based image data collection and the application of Structure from Motion (SfM) techniques for diagnosing building exteriors. Previous studies have primarily focused on specific cases or algorithm improvements, resulting in a lack of systematic and quantitative evidence for establishing a shooting strategy applicable to various field conditions. Therefore, this study aimed to derive an optimal methodology for collecting shape information by comprehensively analyzing the influence of UAV shooting patterns, shooting variables, and their complex combinations on SfM result metrics. To this end, an experimental framework was designed, and actual data were collected according to various single and composite shooting pattern scenarios. The collected data were processed using an SfM pipeline (based on the SIFT algorithm), and quantitative evaluations of the results were conducted using multiple regression analysis, nonparametric tests (Kruskal-Wallis), random forest variable importance analysis, principal component analysis (PCA) and K-means clustering, response surface analysis (RSM), multi-criteria decision-making (TOPSIS), and Pareto optimization. The analysis yielded the following key conclusions. The analysis results provided strong experimental evidence that the selection of the basic shooting pattern \"type\" has a primary and decisive influence on the quality and efficiency of SfM results. Second, within the selected patterns, the influence of detailed shooting variables such as overlap ratio, shooting radius, and camera tilt varied depending on the target performance metrics, and this relationship exhibited a complex pattern that included nonlinearity and interactions between variables rather than a simple linear relationship. Third, we confirmed that composite shooting patterns, which complement the limitations of single patterns, have the potential to achieve multiple performance goals in a balanced manner under specific conditions. Finally, through Pareto optimization analysis, we quantitatively identified the inevitable trade-offs between key performance objectives and demonstrated that it is possible to support rational decision-making by identifying the set of most efficient shooting strategy alternatives (Pareto frontier) under given constraints. This study contributes as follows. We conducted a systematic and quantitative comparison and verification study of shooting patterns and variables, which were lacking in the field of UAV-SfM-based 3D data collection of building exteriors, and provided empirical data. We also demonstrated the possibility of multifaceted interpretation of complex data through the integrated application of various analytical methodologies. Furthermore, the study provides specific quantitative grounds for selecting the optimal shooting pattern type (e.g., overlap rate management in the Surface pattern, consideration of radius/tilt combination in the Circular pattern) and detailed parameters tailored to practical conditions. The Pareto optimization results clearly present the most efficient set of shooting strategy alternatives amid various conflicting relationships, providing a practical tool to support rational decision-making. These results can be utilized as basic data for input data quality management and optimization module design in the future development of UAV-based automatic building exterior wall diagnosis systems. However, this study was conducted on a specific building typology (a mid-rise apartment-like structure) using a particular hardware and software configuration (a DJI, Mavic 3 Enterprise and a SIFT-based SfM pipeline), so caution is required when generalizing the results. Additionally, statistical verification of the interaction effects of complex patterns is insufficient, and the analysis primarily focused on result indicators at the SfM stage.