# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 3 Results and Discussion
### SfM Based on Dynamic and Static Separation
The resulting sparse point cloud reconstruction is shown in Figure 7.  
We compared the accuracy of the reconstructed sparse point clouds with the ground truth (mesh model) by calculating the distance from the point clouds to the mesh, which allowed us to assess the accuracy of the Structure-from-Motion (SfM) reconstruction. With the semantically enhanced SfM, we could directly output point clouds of architectural targets based on semantic information, but the point clouds output by the original openMVG required the target architecture to be manually cropped. We used the CloudCompare 2.12.0 software for progress comparison. The results are shown in Figure 8.  
Due to the limited quantity of point clouds for the Tsinghua University's Old Gate, we magnified the display of the gate's sparse point clouds. We used different colors to represent the magnitude of the points' errors. As shown in the figure, in the color transitions from blue to green to red, blue indicates smaller errors and red indicates larger errors. The assignment of colors was based on relative error values. Due to the large structure of the Tsinghua Life Sciences Building, the overall relative error is small, resulting in most points being displayed in blue. For a fair comparison, we calculated the alignment accuracy of each point cloud with the true model. We compared the reconstruction precision under the condition of ensuring the same possible alignment accuracy. The results of the accuracy evaluation are shown in Table 5.  
Figure 7: Sparse point cloud reconstruction results: (**a**) the sparse point cloud of the Old Gate reconstructed using the original SfM; (**b**) the sparse point cloud of the Tsinghua Life Sciences Building reconstructed using the original SfM; (**c**) the sparse point cloud of the Old Gate reconstructed using semantic SfM based on static and dynamic separation; and (**d**) the sparse point cloud of the Life Sciences Building reconstructed using semantic SfM based on static and dynamic separation. As shown in Table 5, the semantic-based SfM demonstrates higher precision, even under conditions of equal or slightly lower alignment accuracy. This increase in precision is particularly evident in more complex scenes. Given that the gate is located by the roadside, where there is a higher volume of pedestrians and vehicular traffic, the scene contains more dynamic objects. Therefore, the enhancement in Sigma is more pronounced after incorporating semantic optimization in this scenario. To further examine the significance of our static-dynamic separation process, we analyzed the proportions of various objects in the final sparse point cloud. This helped us determine the extent to which dynamic feature points, without semantic integration, contributed to the final point cloud computation. After feature extraction, we applied a matching filter based on the fundamental matrix. We then performed semantic filtering on the matched feature point pairs, which included inconsistencies in semantic attributes of the left and right feature points or those belonging to dynamic objects. The results are presented in Table 6.  
We conducted a statistical analysis of the feature matches between all pairs of images, wherein the most significant source of error was found to be the sky, followed by the impact caused by people and vehicles. The influence of rapidly moving people and vehicles is not significant; rather, points \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Old Gate** & **Life Sciences Building** \\ \hline Sky & 3610 & 1909 \\ Human & 254 & 863 \\ Vehicle & 308 & 30 \\ Cycle & 151 & 4442 \\ Dynamic-other & 595 & 3501 \\ Sum feature point & 284,252 & 2,324,635 \\ Instability point in landmark & 6.59\% & 6.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Semantic statistical analysis of feature points. Figure 8: Visualization of sparse point cloud accuracy evaluation results: (**a**) Tsinghua University’s Old Gate and (**b**) Tsinghua Life Sciences Building.