# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Single Shooting Pattern
#### 4.1.2 Kruskal-Wallis Test  
Next, considering the possibility that the distribution characteristics of SfM result data generated under various shooting conditions, particularly the assumptions of normality and homoscedasticity, may not be satisfied, we applied the Kruskal-Wallis test, a nonparametric statistical test. The purpose of this analysis was to rigorously verify whether there were statistically significant differences in the median distributions of the main SfM result indicators according to the three main shooting pattern types ('Circular', 'Surface', 'Aerial') as categorical variables.  
The results of the Kruskal-Wallis test analysis confirmed that statistically significant differences existed in the median distributions of all major SfM result metrics investigated in this study, namely reprojection error, final number of points generated, total computation time, and reconstruction completeness, across shooting pattern types. This demonstrates that the Kruskal-Wallis test is less stringent in its assumptions about data distribution, making it flexible for application in real-world data analysis scenarios, and clearly shows that the 'type' of shooting pattern itself is an important factor explaining the variability in the result metrics in this dataset. The specific Kruskal-Wallis test results for each performance metric are summarized in Table 5. For the reprojection error, the H statistic was 20.23 (\(p<0.001\)), for the final number of points, H = 7.50 (\(p=0.024\)), for computation time, H = 25.84 (\(p<0.001\)), and for reconstruction completeness, H = 15.71 (\(p<0.001\)). All \(p\)-values were significantly lower than the conventional significance level of 0.05, strongly supporting that the observed differences in the distribution of outcome metrics between shooting pattern types were not due to chance.  
These results provide important insights beyond the parameter influences identified in the previous multiple regression analysis. Specifically, they provide strong experimental evidence that the choice of shooting 'type' itself has a decisive influence on the overall result quality (model accuracy and completeness) and processing efficiency (computation time) of SfM-based 3D modeling projects.  
Therefore, when conducting actual 3D modeling projects, it is essential to comprehensively consider various factors such as the geometric shape of the target building or structure (e.g., vertically tall buildings, horizontally wide structures, presence of complex curved surfaces or corners, etc.), accessibility constraints during on-site photography, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dependent Variable** & **Kruskal–Wallis (H)** & **Kruskal–Wallis (\(p\))** \\ \hline Reprojection Error & 20.2338 & 0.0001 \\ Final Points & 7.4992 & 0.0235 \\ Computation Time & 25.8498 & 0.0001 \\ Reconstruction Completeness & 15.7081 & 0.0004 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of Kruskal–Wallis test on the effect of shooting variables on SfM result metrics.  
the intended use of the final modeling results (e.g., focus on overall shape restoration or detailed diagnosis and precise analysis of specific parts) to select the most appropriate basic shooting pattern type (e.g., Surface pattern for wide area coverage, Circular pattern for specific objects or complex shapes) as a priority.