# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 3 Results and Discussion
### Semantic-Based SGM
The first row of images presents the results obtained without filtering, while the second row displays the outcomes after filtering. The left side depicts the results obtained using the original method, and the right side shows the results after semantic optimization. It is evident that using the original SGM reconstruction method introduces a substantial amount of noise, particularly in areas of depth discontinuity, such as the edges of the building. Even after filtering the dense point cloud, the noise level remains significantly higher compared to that obtained using the sSGM method. In the collected multi-view images, the building is adjacent to the sky. During computation, it becomes challenging to accurately identify the positions of disparity discontinuities when using the original SGM method, especially when calculating the one-dimensional path costs transitioning from the building to the sky. This is due to the lack of distinct features in the sky region. Hence, the subsequent cost aggregation process is prone to computing some incorrect disparities. Next, we conducted a quantitative statistical analysis to evaluate the reconstruction accuracy. Ultimately, the dense point cloud obtained from the reconstruction was aligned with the true-value grid using the Iterative Closest Point (ICP) algorithm, and the distance between the point cloud and  
Figure 11: Comparison of results of dense reconstruction of Tsinghua Life Sciences Building: (**a**) reconstruction results using the original SGM method and without post-processing; (**b**) reconstruction results using the semantic-based sSGM method and without post-processing; (**c**) reconstruction results using the original SGM method and with filtering; and (**d**) reconstruction results using the semantic-based sSGM method and with filtering. the grid was calculated. The calculation process was consistent with that used for sparse point clouds, and the results are presented in Table 7.  
We aligned the reconstructed dense cloud point with the ground-truth grid and compared the reconstruction accuracy while ensuring the same alignment accuracy. As shown in Table 7, we compared three sets of reconstructed data, and the results all show that the dense point clouds obtained using sSGM have a higher accuracy, which is mainly manifested as a smaller max. distance, a smaller average distance, and a lower Sigma value. There are two reasons for the smaller maximum distance. One is that sSGM has semantic perception capabilities and can judge depth continuity based on semantic consistency, which greatly improves the reconstruction accuracy of the edge areas of objects. The second reason is that we can perform directional output according to the semantic attributes contained in the reconstructed dense point clouds, thereby excluding some outliers and errors. A lower Sigma value indicates that the sSGM method can improve the overall accuracy. We also compared this method with the current mainstream PatchMatch method, and the results are shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Old Gate**} & \multicolumn{2}{c}{**Life Sciences Building**} & \multicolumn{2}{c}{**Xuetang**} \\ \hline  & **SGM** & **sSGM** & **SGM** & **sSGM** & **SGM** & **sSGM** \\ \hline Alignment accuracy (RMS) & 0.11 & 0.12 & 0.098 & 0.098 & 0.078 & 0.11 \\ \hline Max. distance & 28.5 & 6.66 & 10.004 & 9.92 & 3.8 & 2.48 \\ \hline Average distance & 0.085 & 0.084 & 0.077 & 0.024 & 0.021 & 0.016 \\ \hline Sigma & 0.435 & 0.12 & 0.646 & 0.173 & 0.11 & 0.08 \\ \hline Max. error & 0.18 & 0.048 & 0.228 & 0.22 & 0.15 & 0.144 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Dense reconstruction accuracy comparison. Figure 12: Comparison of results of dense reconstruction of Tsinghua Xuetang: (**a**) ground truth; (**b**) reconstruction results using the sSGM method; (**c**) reconstruction results using the original SGM method; and (**d**) reconstruction results using the PatchMatch method.