# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 2 Literature Review
### Structure from Motion (SfM)
This issue significantly impacts the reconstruction of planar structures commonly found in artificial environments, potentially resulting in incomplete or inaccurate models. [PERSON] et al. [35] proposed a method to improve the completeness of reconstruction in such cases by interpolating points within the detected 3D plane boundaries. However, this solution is limited to planar geometric structures and often requires manual intervention for object plane identification, thereby limiting its generalizability and automation potential. Therefore, developing methods to robustly and automatically restore various types of weakly supported surfaces remains an important research challenge. Previous research on SfM-MVS methodology has driven significant algorithmic advancements aimed at improving the quality and robustness of 3D reconstruction. These advancements are evident across various domains, ranging from feature detection and matching strategies to dense reconstruction algorithms that incorporate both traditional and learning-based paradigms. Algorithm improvements and comparative evaluations have been continuously conducted to optimize various stages of the reconstruction pipeline [32; 36]. Nevertheless, challenges such as handling surfaces with insufficient texture or high reflectivity, ensuring scalability and preserving fine details remain ongoing challenges that highlight the difficulties still present [32; 35]. While algorithmic advancements have mitigated some of the limitations from the past, consistently obtaining precise and complete 3D geometric information across diverse real-world environments remains a challenge, highlighting the need for novel approaches, potentially integrating learning-based priors or advanced data fusion techniques, to overcome these persistent limitations, especially in the context of building modeling. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Incremental SfM** & **Global SfM** & **Latest Trends** \\ \hline
**Accuracy** & Generally high & Initially low, improving & Aims for incremental SfM level or higher \\ \hline
**Robustness** & \begin{tabular}{c} High \\ (Iterative RANSAC/BA) \\ \end{tabular} & \
\begin{tabular}{c} Low \\ (Outlier sensitive, Weak \\ translation averaging) \\ \end{tabular} &
\begin{tabular}{c} Aims for incremental SfM robustness \\ \end{tabular} \\ \hline
**Scalability** & \begin{tabular}{c} Low \\ (Sequential, Iterative BA) \\ \end{tabular} & \
\begin{tabular}{c} High \\ (Parallelizable) \\ \end{tabular} &
\begin{tabular}{c} Maintains high \\ scalability \\ \end{tabular} \\ \hline
**Cost** & \begin{tabular}{c} High \\ (Repeated BA) \\ \end{tabular} & \
\begin{tabular}{c} Low \\ (Fewer BA) \\ \end{tabular} &
\begin{tabular}{c} Maintains low cost \\ (Faster than incremental) \\ \end{tabular} \\ \hline
**Error Accum.** & \
\begin{tabular}{c} Possible \\ (Drift) \\ \end{tabular} &
\begin{tabular}{c} Relatively low \\ approach \\ \end{tabular} \\ \hline
**Challenges** & Cost, Drift & \
\begin{tabular}{c} Translation avg. instability, \\ Outlier sensitivity \\ \end{tabular} &
\begin{tabular}{c} Solving translation avg., \\ Ensuring robustness \\ \end{tabular} \\ \hline
**Implement.** & COLMAP & Theia, OpenMVG & GLOMAP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SfM Approach Comparison.