# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.5 PMVS Dense Reconstruction Results  
PMVS (patch-based multi-view stereo) is a multi-view stereo matching algorithm used to reconstruct 3D models from multiple photos. The basic principle is to generate a dense \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**SfSM**} & \multicolumn{2}{c}{**VSFM**} & \multicolumn{2}{c}{**Colmap**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **R\({}_{e}\)\(\downarrow\)** & **N\(\uparrow\)** & **R\({}_{e}\)\(\downarrow\)** & **N\(\uparrow\)** & **R\({}_{e}\)\(\downarrow\)** & **N\(\uparrow\)** & **R\({}_{e}\)\(\downarrow\)** & **N\(\uparrow\)** \\ \hline Statue1 & 4.35 & 40,973 & 3.25 & 60,588 & 1.21 & 17,566 & **0.98** & **78,986** \\ Statue2 & 9.96 & 32,000 & 2.48 & 100,257 & 1.12 & 42,858 & **1.06** & **220,861** \\ Archi1 & 8.56 & 46,749 & 2.69 & 114,350 & 1.11 & 33,599 & **1.03** & **570,330** \\ Archi2 & 11.56 & 34,284 & 2.32 & 182,470 & **1.33** & 74,195 & 1.58 & **500,247** \\ \hline \hline \end{tabular}  
* The direction of the arrow indicates the optimal trend of the metric, while the bolded data signifies the best results.  
\end{table} 
Table 8: Comparison of quantitative results of various sparse reconstruction algorithms across four sets of self-collected data. Figure 21 illustrates the dense reconstruction results of the Statue. From the frontal perspective, the VSFM algorithm produces sparse point clouds with significant detail loss, while COLMAP increases point cloud density but still exhibits reconstruction blind spots. In contrast, the proposed algorithm generates uniformly distributed high-density point clouds, effectively capturing the statue's textures and intricate details. In the lateral view, VSFM displays blurred outlines, and COLMAP inadequately handles complex geometries; however, the proposed algorithm achieves precise contour delineation and fine feature reconstruction. The rear perspective comparison is even more pronounced: VSFM suffers from extensive point cloud omissions, and COLMAP fails to provide complete coverage, whereas the proposed algorithm ensures full rear-view coverage. The comparative results of the supplementary data for Statue2, Archi1, and Archi2 presented in Figures 22-24 further validate the significant advantages of the proposed algorithm in terms of point cloud quantity and completeness. Figure 23: The dense reconstruction results of the self-collected data Archi1 under various algorithms. Figure 24: The dense reconstruction results of the self-collected data Archi2 under various algorithms. Through a comparative analysis from various perspectives, it is evident that the algorithm presented in this paper demonstrates significant improvements over VSFM and COLMAP in terms of point cloud quantity and detail reconstruction. The point clouds generated by VSFM appear relatively sparse across different viewpoints, resulting in the failure to recover numerous architectural details, which compromises the completeness of the reconstruction. Although COLMAP shows an increase in point cloud density, its performance in detail representation remains suboptimal, particularly concerning the textures and complex structures of buildings, failing to achieve the desired reconstruction quality. In contrast, the algorithm proposed in this paper not only generates a greater number of point clouds but also ensures a more uniform distribution, effectively capturing the intricate features of the architecture thereby enhancing the completeness and accuracy of the reconstruction across all perspectives. Table 9 quantitatively illustrates the specific number of reconstructed point clouds generated by various dense reconstruction algorithms applied to self-acquired data. This study conducts a comparative analysis of the dense reconstruction performance across four typical 3D reconstruction datasets (Statue1, Statue2, Archi1, Archi2) using three feature matching algorithms (VSFM, COLMAP, and an improved algorithm).