# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 3 Results and Discussion
### Semantic-Based SGM
The transition from blue to green and then to red signifies a gradation of error from minimal to maximal. Figure 12 shows the reconstruction results of Tsinghua Xuetang as an example. We compared the sSGM method to the original SGM and PatchMatch methods. It is evident that in the point clouds obtained using the SGM and PatchMatch methods, the trees and building sides on both sides of the door are mixed together. However, the point cloud reconstructed by using the sSGM method can well separate the trees and building sides. Moreover, for the eaves of the building, the reconstruction error of the sSGM method is also smaller. We calculated the statistics related to the reconstruction accuracy of the three methods for the three datasets by performing an error analysis of the three models and averaging the results. The results are shown in Table 8.  
As can be seen from Table 8, after adding semantics to the SGM method, the reconstruction accuracy is greatly improved, and the reconstruction results are better than the reconstruction results of the mainstream method PatchMatch. The max. distance may be affected by gross errors, so we compared the average distance. The accuracy of sSGM is 32.79% higher than the original SGM method, and 63.06% higher than the PatchMatch method. This is mainly reflected in the smaller distance error from the ground-truth mesh. The error caused by outliers is smaller. Our experimental results are therefore consistent with our theoretical design. After obtaining the dense point clouds, we performed a mesh reconstruction operation. Figure 13 shows the results of mesh reconstruction from dense point clouds generated by three methods. We can find that compared to the original SGM method and the PatchMatch method, the point clouds obtained by the sSGM method have better results in the mesh reconstruction process. Buildings can be completely and independently reconstructed using the sSGM method. The original SGM method is insensitive to the location of depth mutations and inaccurately reconstructs a part of the sky at the edge of the building. The sSGM method that we proposed can well judge the depth continuity of adjacent pixels through the semantically optimized penalty term, so it can obtain high reconstruction accuracy at the edge of the building. During the mesh reconstruction process, the point clouds obtained by sSGM can avoid simply connecting all points. Both the SGM method and the PatchMatch method inevitably reconstruct the building and other objects (such as bicycles, bushes, etc.) into a mesh as a whole. Thanks to the semantic information that we incorporated into the reconstruction process, the dense point clouds obtained by the sSGM method can improve the accuracy of subsequent mesh reconstruction. \begin{table}
\begin{tabular}{c c c c} \hline  & **SGM** & **sSGM** & **PatchMatch** \\ \hline Max. distance & 14.101 & 6.353 & 16.843 \\ \hline Average distance & 0.061 & 0.041 & 0.111 \\ \hline Sigma & 0.397 & 0.124 & 0.629 \\ \hline Max. error & 0.186 & 0.137 & 0.205 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of dense reconstruction accuracy of different methods.