# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 3 Methodology  
This section describes in detail the research methodology designed to address the lack of standardized guidelines for the collection of building exterior image data using UAVs and the application of SfM. To systematically conduct the research, an analysis framework was established, UAV shooting patterns and variables were defined based on this framework, and a specific implementation environment was set up to ensure consistent experimentation. This methodology covers the entire process from data collection to result analysis, with a focus on ensuring the reliability and reproducibility of the research results. This study was conducted based on a systematic analysis framework to quantitatively clarify the relationship between UAV shooting variables and SfM processing results. This framework consists of sequential steps: data collection planning, actual data acquisition, SfM processing, and result indicator evaluation (Figure 1). At each stage, core variables were defined, a consistent data processing pipeline was applied, and objective performance evaluation metrics were selected to achieve the research objectives. For example, in the data collection stage, scenarios were designed by combining various shooting paths, separation distances, overlap rates, and camera tilts, and all collected images were processed using the same SfM algorithm (SIFT-based) and parameter settings. This framework provides a systematic basis for comparing and analyzing the impact of various shooting conditions on the accuracy, completeness, and efficiency of SfM results. In this context, 'Reconstruction Completeness' is quantitatively defined as the percentage of the facade's surface area that is successfully represented in the final 3D point cloud, measured against a high-density ground truth model. Next, to effectively collect 3D shape information of building envelopes, a combination of various drone shooting patterns and key variables was systematically designed and tested. The main variables considered included shooting path type, distance from the target (A-offset, B-Radius), image overlap rate (forward and lateral overlap), and camera tilt angle. The imaging patterns applied in the experiments were defined into three main types: Circular, Surface, and Aerial (Figure 2, Table 2). Specifically, the circular shooting method was performed by adjusting the tilt angle toward the center of the building within a height range of 20 to 80 m from the building roof and a radius range of 10 to 50 m from the center point. The frontal shooting method was performed by setting the distance from the target to 10 to 30 m, the forward and lateral overlap distances to 1, 2, and 4 m, and the tilt angle to 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and the aerial photography method was conducted at altitudes of 20 and 40 m above the rooftop with an overlap rate of 80% and a tilt angle of 60-75\({}^{\circ}\). Additionally, to ensure precise location information for each shooting point,  
Figure 1: Research flowchart. GNSS-RTK was utilized to manage horizontal and vertical position errors within 2-3 cm. Prior to actual flight, simulations were conducted to verify the safety and appropriateness of the planned flight path. This comprehensively defined combination of capture patterns and variables enables data collection under various conditions, serving as a foundation for enhancing the reliability of subsequent analyses. In addition to analyzing these single patterns individually, this study also investigated composite shooting strategies, where data from two different patterns were combined. This was done to evaluate whether hybrid approaches could leverage the strengths of each pattern to produce a more comprehensive 3D model.