# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Single Shooting Pattern  
#### 4.1.1 Multiple Regression  
Multiple Regression analysis assumes a linear relationship between each shooting variable and SfM result indicators (final number of points, reconstruction completeness, reprojection error, calculation time, etc. ), and aims to determine the magnitude of the influence of individual variables on changes in result indicators (regression coefficients), statistical significance (\(p\)-value), and the explanatory power of the model as a whole (adjusted R-squared). Figure 3: Collected Data Sample. (**a**) Target Building, (**b**) Circular Dataset, (**c**) Surface Dataset, (**d**) Aerial Dataset. The results of the surface capture pattern analysis showed that the variables related to the overlap between images, Forward Overlap and Side Overlap, exhibited a close linear relationship with the quality indicators of SfM results (Table 4). Specifically, these two variables showed statistically highly significant (\(p<0.001\)) negative regression coefficients for both the number of points generated and the model reconstruction completeness. This quantitatively demonstrates that as the values of these variables decrease, i.e., as the physical overlapping between images increases, the number of points and reconstruction completeness increases linearly. These results align precisely with the fundamental principle of SfM that sufficient feature matching between multi-view images is essential for successful 3D reconstruction and statistically reaffirm that ensuring overlap is a critical factor for quality improvement. Data analysis results show that the Forward Overlap and Side Overlap variables accounted for approximately 70% (adjusted R\({}^{2}\approx 0.70\)) of the variability in Final Points and approximately 68% (adjusted R\({}^{2}\approx 0.68\)) of the variability in Reconstruction Completeness. Additionally, these variables had a significant negative impact on reducing projection errors; however, the model's explanatory power was relatively low, at approximately 21% (adjusted R\({}^{2}\approx 0.21\)). On the other hand, the camera tilt angle variable did not show statistically significant linear effects on most of the result indicators in this analysis model. In the case of the Circular shooting pattern, the influence of variables different from those in the Surface pattern was prominent. An increase in the radius when shooting in a circular pattern around the subject suggested a significant (\(p\approx 0.052\)) positive relationship with an increase in the reprojection error. This implies that as the shooting distance increases, the geometric accuracy of the final model may decrease due to changes in image scale and increased likelihood of feature point matching errors. The explanatory power of the model was high, at approximately 80% (adjusted R\({}^{2}\approx 0.80\)). In contrast, an increase in the camera tilt angle showed a statistically significant (\(p\approx 0.003\)) positive relationship with an increase in the final number of points (adjusted R\({}^{2}\approx 0.69\)). This suggests that capturing objects from more diverse angles can reduce occlusion areas and enhance the model's detail. However, high condition numbers were observed in some regression models used in the Circular pattern analysis, raising the possibility of multicollinearity. Multicollinearity implies strong correlations among independent variables, meaning that changes in one variable (e.g., Diameter) may be associated with changes in another variable (e.g., Tilt angle). Therefore, a cautious approach is required to interpret the independent pure effects of each variable on the outcome metric. Similarly to how specific tilt angle settings may impose  
constraints on the minimum shooting radius in real-world scenarios, these variables may be difficult to control independently. In conclusion, multiple regression analysis clearly demonstrated that the key variables influencing SfM results can vary depending on the shooting pattern.