# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Discussion  
The individual variable effects on analyzed single and composite shooting patterns, performance comparisons between pattern types, and multi-criteria and multi-objective optimization evaluations clearly demonstrated that the UAV-based SfM 3D reconstruction process is a multifaceted optimization problem that goes beyond a simple input-output relationship. From the fundamental selection of capture patterns to the fine-tuning of individual parameters and the inevitable trade-offs between different performance metrics (e.g., accuracy, completeness, efficiency), the quantitative results emphasize the necessity of a systematic and data-driven strategic approach to maximize the efficiency and quality of SfM-based 3D modeling. Most importantly, the fundamental and significant influence of the basic shooting pattern \"type\" (e.g., Circular, Surface, Aerial) on the quality and efficiency of subsequent results was statistically clarified through Kruskal-Wallis tests. The results of this non-parametric test proved that the median distributions of key SfM result metrics, such as reprojection error, final point count, computation time, and reconstruction completeness, differ statistically significantly (\(p<0.05\)) depending on the shooting pattern type (Table 5). This suggests that determining the most appropriate macro-level shooting approach that aligns with the project's objectives (e.g., geometric characteristics of the subject, required model precision, available resources) is a prerequisite before optimizing individual variables. In other words, the pattern type itself acts as a critical design factor that primarily constrains or defines the result space, serving as the foundation for determining the direction and effectiveness of subsequent parameter optimization. Within a specific selected shooting pattern type, detailed parameters such as overlap, shooting distance/radius, and camera tilt function as key variables that control the result metrics. The results of the multiple regression analysis, Random Forest variable importance evaluation, and response surface analysis (RSM) in this study consistently demonstrated  
Figure 8: Pareto optimization visualization of multiple shooting patterns. (**a**) Error_vs_Points, (**b**) Time_vs_Completeness, (**c**) Time_vs_Error, (**d**) Time_vs_Points. that the influence of these variables varies depending on the target performance metric and the selected pattern type, and includes not only simple linear relationships but also complex nonlinear relationships and interactions between variables. In the Surface pattern, the overlap degree variable exerted a dominant influence on the number of points and reconstruction completeness (regression analysis R\({}^{2}\approx\) 0.70, RF importance sum \(\approx\) 0.9), In the Circular pattern, the shooting radius and tilt angle each played distinct yet important roles in the projection error and number of points (regression analysis R\({}^{2}\approx\) 0.80 and 0.69, RF importance \(\approx\) 0.82 and 0.71, respectively). Notably, some variables (e.g., Tilt angle in the Surface pattern) were not significant in the linear model but showed high importance in the nonlinear model, warning of the risk of bias that may arise when relying on a single analysis method for evaluating variable effects. The RSM results visually confirm these interaction effects and suggest the possibility of a'sweet spot' where optimal performance is achieved under specific variable combinations (Figures 4 and 5). This suggests that SfM optimization cannot be achieved through independent adjustments of individual variables alone, and that a multivariate optimization approach is required to understand and consider the complex functional relationships between variables depending on the target metrics and pattern types. Finally, the potential of composite shooting patterns was confirmed as an alternative to overcome the inherent limitations of single shooting patterns and improve performance.