# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 2 Literature Review
### Previous Research on SfM-Based 3D Reconstruction  
Previous studies have extensively demonstrated the usefulness of combining unmanned aerial vehicle (UAV) platforms and the SfM-MVS pipeline for 3D reconstruction in various fields, particularly in the areas of cultural heritage preservation and building modeling. These studies have explored various platform types, onboard sensors, and data processing workflows, recognizing that careful planning is crucial for successful reconstruction [37]. Additionally, efforts have been made to improve specific algorithm components, such as enhancing the robustness of the MVS stage through learning-based approaches or developing multiple drone systems to increase the efficiency of large-scale data acquisition [12; 20]. However, many studies have primarily focused on the application of the technology itself or algorithm improvements after data acquisition and often use flight paths that are manually designed or limited to specific situations without undergoing rigorous optimization processes. As a result, there is a significant gap in systematic verification and optimization studies on the impact of data acquisition strategies, particularly the influence of various flight patterns and parameters on the reconstruction quality of complex structures. To address these issues, it is necessary to shift the focus of research toward quantitatively evaluating the impact of flight design on the entire SfM-MVS workflow.  
The absence of a systematic flight planning and quantitative evaluation framework is hindering the establishment of reliable and optimized UAV data acquisition protocols for SfM-MVS-based 3D reconstruction. Some studies have attempted model-based path planning methods that consider surface coverage or \"reconstructability\" metrics, but these methods do not necessarily guarantee globally optimal solutions, and thorough quantitative evaluation of the resulting SfM-MVS outputs is often lacking [13]. Other studies point out issues arising from inappropriate viewpoint distributions, such as registration failures due to large viewpoint differences or performance degradation of new viewpoint image synthesis algorithms, indirectly emphasizing the need for well-designed data acquisition patterns [38]. Crucially, the existing literature generally lacks comprehensive and comparative analyses of quantitative metrics such as feature point density distribution, reprojection error statistics, model completeness, processing time, and memory usage across various flight scenarios (e.g., grid-based, spiral, oblique angle capture patterns, overlap ratio, and flight altitude changes). The absence of such a systematic and metric-driven validation process makes it challenging to generalize research results or establish standardized best practices for acquiring high-quality data tailored to the complexity and reconstruction objectives associated with specific objects.  
Therefore, this study aims to establish standardized guidelines for image data collection using UAV and the application of SfM for building exterior diagnosis. To achieve this, we will systematically experiment and quantitatively verify the effects of various UAV shooting patterns, key variables (distance, overlap rate, camera angle, etc.), and their combinations on the quality and processing efficiency of SfM results.