# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 2 Literature Review
### Structure from Motion (SfM)  
Three-dimensional reconstruction is essential in a wide range of fields, including computer vision, robotics, and cultural heritage preservation [24]. Structure from Motion (SfM) and Multi-View Stereo (MVS) are core image-based approaches that complement each other. SfM estimates the camera pose and sparse 3D structure of a scene from multiple 2D images, based on the principles of motion parallax and projection geometry [25]. This process typically involves stable feature detection and matching (e.g., SIFT, ORB), camera relative/absolute pose estimation using epipolar geometry and PnP algorithms, 3D point location calculation via triangulation, and global optimization through bundle adjustment [26]. SfM ultimately generates a sparse point cloud but inherently suffers from scale ambiguity and assumes static scenes, relying on robust geometric verification procedures such as RANSAC for outlier removal [27]. As a result, SfM serves the role of providing camera parameters and an initial geometric skeleton essential for subsequent dense reconstruction stages [28]. The quality and completeness of the results produced in the SfM stage directly influence the performance of the MVS stage, which aims to achieve detailed surface reconstruction of the scene [11]. SfM implementation strategies are broadly categorized into incremental and global approaches, each with distinct advantages and disadvantages in terms of accuracy, robustness and scalability. These two primary strategies, incremental and global SfM, present distinct trade-offs as summarized in Table 1. Meanwhile, MVS leverages the principle of photometric consistency based on SfM results. Incremental SfM starts with a small number of images, sequentially adds views, and pursues high accuracy through iterative local optimization (bundle adjustment), but it has issues such as error drift and increased computational costs when processing large datasets [29]. On the other hand, global SfM simultaneously estimates all camera poses, offering superior computational efficiency and scalability, but it is sensitive to initial relative pose estimation errors and outliers, and ensuring stability in the translation averaging stage remains a technical challenge [30]. MVS utilizes precisely estimated camera parameters from SfM to estimate dense geometric information by evaluating pixel value similarity across multiple views based on specific depth or surface location assumptions [31]. It primarily estimates 3D coordinates by searching for corresponding points based on photometric consistency between features within a scene. [PERSON] and [PERSON] [32] emphasized that this principle of photometric consistency forms the foundation of MVS methodologies. Therefore, MVS goes beyond sparse feature-based Structure from Motion (SfM) results to enable detailed 3D shape reconstruction of surfaces. The following discussion will examine the actual performance evaluation and technical limitations of the SfM-MVS pipeline in detail. Therefore, the choice of the SfM strategy determines the quality of the input information for MVS, and MVS itself fundamentally relies on the assumption that corresponding surface points appear similar across different views [33]. However, this core assumption of MVS is often violated in real-world environ ments, particularly with common building materials featuring reflective surfaces or large areas of uniform, textureless appearance necessitating advanced algorithms to handle such complexities during the dense reconstruction process [34]. One of the important limitations inherent in many MVS approaches is the difficulty of accurately restoring so-called weakly supported surfaces, which lack texture or have high reflectance. These types of surfaces do not provide sufficient photogrammetric information for reliable correspondence matching at multiple points, causing difficulties in the restoration process.