## 5 Discussion
### Network Structure  
\(D^{2}\)HC-MVSNet improves the R-MVSNet GRU gating to some extent, with more powerful loop convolution units and dynamic consistency checking strategies. Firstly, the network uses dilated convolution [39] to obtain a larger range of feature information in the 2D feature extraction part, connects the feature output via different convolutional layers, and aggregates context feature information without losing resolution. \(D^{2}\)HC-MVSNet introduces a cyclic encoding-decoding HU-LSTM structure [24] to regularize the cost volume along the depth direction in the cost aggregation part, and realizes the connection memory of the same size features along the depth direction on the feature map of each scale  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assessment of Damage** & **Orthophoto** & **Field Survey** & **3D Models** \\ \hline G0 & 22.7\% & 20.3\% & 21.6\% \\ G1 & 37.7\% & 37.7\% & 36.7\% \\ G2 & 21.6\% & 26.1\% & 25.5\% \\ G3 & 13.2\% & 10.1\% & 11.1\% \\ G4 & 4.8\% & 5.8\% & 5.2\% \\ Total & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results of the 2D and 3D assessment ratio and field survey.  
Figure 7: Schematic diagram of various deep learning MVS networks. (**a**) \(D^{2}\)HC-MVSNet; (**b**) AA-RMVSNet.  
in the regularization process. This method not only aggregates the spatial geometric context information of the cost map but also preserves the cost aggregation output of the original resolution size with lower memory consumption. However, this method decomposes the cost volume into cost maps at each depth and processes them sequentially using a recurrent neural network, resulting in a slower computational efficiency of the network. When used to assist in assessing the damage level of houses in post-earthquake areas, it cannot meet the urgent needs of this work.  
AA-RMVSNet is improved on the basis of \(D^{2}\)HC-MVSNet. The feature extraction part uses the common CNN to obtain the high-dimensional information of the image. The last two downsamplings output the feature maps of the original image with 1/4 and 1/16 size 32 channels, respectively. One of the innovations of this method is that an Intra-view AA module composed of deformable convolution [40; 41] is added to the feature extraction part, which is used to adaptively aggregate the features of different scales and regions with different texture richness. This module processes the feature maps of the last three layers through deformable convolution and then upsamples the output of the last two layers after processing, and integrates with the previous layer as the final output of the feature extraction part, maintaining the geometric shape of the object in the image to the greatest extent when extracting features. In addition, when the network aggregates the cost volume of multiple perspectives, the Inter-view AA module is added to suppress the mismatched pixels by pixel-level weighting, and the pixels with higher matching correlations are given greater weight, rather than the matching results of all perspectives being treated equally. In short, the network fully retains the original geometric information of the object in the image and pays attention to the correlation problem after matching each perspective, thus improving the quality of 3D reconstruction. However, this method also uses the recurrent neural network, joining the above two optimization methods. Therefore, the quality of 3D reconstruction is guaranteed but the time efficiency is not well balanced when used in the post-earthquake building damage assessment work, and the added Inter-view AA module makes the network's memory consumption higher, resulting in higher GPU hardware requirements when carrying out this work.