# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 1 Introduction  
Inspecting and maintaining the exterior walls of buildings on a regular basis is essential for ensuring structural safety, increasing energy efficiency, and maintaining aesthetics [1]. There are a growing number of aging buildings worldwide, and with increasing social demands for building safety and stricter regulations, the need for reliable exterior inspections is becoming even more critical [2]. In addition, these diagnostic and related maintenance activities form a significant economic scale, with related costs showing a continuous upward trend [3]. These strong economic and social demands highlight the need for alternative methods that can overcome the potential risks and inefficiencies of traditional manual methods. As a result, the sustained growth of the building exterior wall inspection market is serving as a key driver for the adoption of new technologies in this field [4]. The introduction of automated technologies for efficiently and objectively collecting information on the condition of building exteriors is actively underway, with particular attention being paid to 3D data acquisition methods [5]. Traditional exterior wall inspection methods require workers to physically access the exterior walls, resulting in significant time and cost expenditures, as well as safety hazards during high-rise operations and subjective results [6]. As an alternative to address these issues, among various automated technologies being explored, those focusing on non-contact 3D data acquisition methods such as laser scanning and photogrammetry are emerging [7]. Notably, photogrammetry using unmanned aerial vehicles (UAVs) enables rapid imaging of large areas and facilitates data acquisition in hard-to-reach high-rise or complex structures, leading to a rapid increase in their application [8]. In this technological transition, the Structure from Motion (SfM) method provides the core component of the process for generating 3D models from UAV images [9]. This computer vision algorithm automatically extracts and matches feature points in image sequences captured from multiple angles to simultaneously estimate the camera's external appearance features and the target's 3D structure [10]. Generally, the camera information and sparse point cloud estimated with high precision through SfM are used as input for subsequent Multi-View Stereo (MVS) algorithms, directly enabling more accurate and complete dense point cloud generation and thus contributing to the final generation of a dense 3D model [11]. Therefore, SfM can be defined as an essential method element responsible for initial data processing and geometric information restoration in UAV image-based 3D modeling pipelines. However, there is currently a lack of quantified standards or specific guidelines for image capture procedures and related parameter settings for SfM-based 3D building modeling using UAVs. The absence of standardized procedures or quantitative guidelines can exacerbate various practical problems during UAV photogrammetry work on large-scale buildings or structures with complex shapes. First, it is difficult to select the most appropriate shooting strategy based on objective evidence, considering the project objectives and the characteristics of the target building, thereby hindering rational decision-making. Second, inconsistently collected data increase the likelihood of errors in subsequent processing and reduce the reliability of key quality indicators such as the accuracy and completeness of the final 3D model. Third, it impairs the reproducibility of the data collection and processing processes, which manifests as issues such as failed alignment between upper and lower viewpoints of high-rise buildings [7], the necessity of a control system when operating multiple drones [12], and difficulties in establishing flight paths suitable for complex shapes [13]. These practical challenges act as constraints in ensuring the practical applicability and scientific rigor of UAV photogrammetry.