# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 2 Related Work and Methods
### Methods
#### 2.2.3 Multi-Scale Cost Aggregation for Reduced Mismatching  
The fundamental purpose of cost aggregation is to make the cost accurately reflect the correlation between pixels and solve the issue of pixel matching being affected by noise. In fact, cost aggregation is similar to the disparity propagation step. Regions with a high signal-to-noise ratio have a good matching effect, but only local correlation is considered when calculating the initial cost. As a result, the disparity is not the smallest in the low signal-to-noise ratio and ill-posed regions of the image, so it is difficult to find the correct homonymous points. By propagating to these regions through cost aggregation, the optimal disparity can be more accurately acquired. Finally, the cost volume of all images can accurately reflect the true correlation.  
In the ill-posed area of the image, there is less information, so it is necessary to obtain the identifiable details from the high-resolution features so as to accurately find the homonymous point. We used cost aggregation at different scales, which can acquire detailed information and rich contextual information, which helps to reduce the problem of mismatching in ill-posed regions.  
Figure 3: The structure of the DenseGASPP module. We used the right sub-graph as an example to introduce the DenseGASPP module. Three groups of atrous convolution layers with the following dilation rates ([2,3) (4,5) (6,7)] are designed in the DenseGASPP module, and two groups of convolution layers with a continuous small disparity rate are distributed in each group. In addition, the results between the three groups of atrous convolution layers are densely connected, that is, the feature maps between the groups are concatenated, which is helpful for obtaining more feature information.  
For the multi-scale cost aggregation, we introduced the intra-scale and cross-scale cost aggregation. The model is shown in Figure 4. Because the feature resolution is the same at the same scale, intra-scale cost aggregation only aggregates the cost volumes with the same resolution, and the copy operation is directly adopted between the cost volumes. Cross-scale cost aggregation is used to aggregate the characteristics between cost volumes of different scales [21]. The cross-scale aggregation operation can be divided into two cases: (1) up-sampling: the scale of the current cost volume is larger than that of other cost volumes. This entails the up-sampling of other cost volumes to the current scale to align the resolution; (2) down-sampling: the scale of the current cost volume is smaller than that of other cost volumes. This entails the down-sampling other cost volumes to the current scale to align the resolution.  
Starting from the high-resolution subnet as the first stage, we gradually increased the high-resolution to low-resolution subnets to form more stages and connected the multi-resolution subnets in parallel. In the whole process, we performed multi-scale repeated fusion by repeatedly exchanging the information on parallel multi-resolution subnetworks. Finally, the high-resolution features were obtained.