# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 3 Results and Discussion
### Semantic Segmentation for Complex Urban Scenes  
We opted to utilize the MMSegmentation platform to train our semantic segmentation models [34]. This platform has benchmarked models and datasets that it supports, significantly easing our model selection process. We analyzed the performance of all semantic segmentation networks supported by the platform using the Cityscapes dataset. We identified the top three semantic segmentation networks in terms of accuracy, including DeeplabV3+, Ocrnet, and Mask2 Former. These networks were then trained using our new classification scheme, after which we compared the accuracy of the various networks. In 2017, [PERSON] conducted a study on atrous convolutions for image semantic segmentation (DeepLab V3) and employed atrous convolutions to expand the receptive field [35]. This approach facilitated target segmentation on various scales by concatenating atrous convolutions with different dilation rates. In 2018, [PERSON] proposed an architecture for image semantic segmentation by incorporating atrous separable convolutions within an encoder-decoder structure (DeepLab V3+) [35]. This model aimed to integrate the best aspects of spatial pyramid pooling and the encoder-decoder framework, thereby creating a faster and more efficient overall model. In the OCRNet (Object-Contextual Representations Network), a novel approach to constructing contextual information for semantic segmentation tasks is proposed which focuses on new object contextual information. By utilizing features corresponding to the object classes to describe pixels, this method transforms the pixel classification challenge into an object region classification problem, thereby explicitly enhancing object information [36]. The High-Resolution Net (HRNet) is a specialized Convolutional Neural Network designed to retain high-resolution inputs throughout the network, thus enhancing the accuracy of pixel-level segmentation. Its primary goal is to improve semantic segmentation in high-resolution images while effectively managing the balance among multiple classes. This design ensures detailed and precise segmentation, which is particularly important in complex scenarios with diverse object categories [37]. Mask2 Former is composed of a backbone feature extractor, a pixel decoder, and a transformer decoder. The backbone feature extractor is typically a transformer model, such as Swin. The pixel decoder is a deconvolution network that gradually restores the feature map resolution to the original image size through deconvolution operations. Finally, the transformer decoder is used to manipulate image features to process object queries [38; 39]. The equipment information used is shown in Table 3.  
The specific configurations for several networks are described below. We set the network batch size of OCR_hr48 to four and that of the other two networks to two. The number of training iterations was set to 100 epochs. Considering that both our reconstructed data and the Cityscape dataset images are not square, the image input for our segmentation network was set to 512 \(\times\) 1024. During the \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Configuration** \\ \hline OS & Ubuntu 20.04 \\ GPU & NVIDIA TITAN RTX (Santa Clara, CA, USA) \\ CPU & Intel i9-10900k (Santa Clara, CA, USA) \\ CUDA & Cuda 11.3 (NVIDIA, Santa Clara, CA, USA) \\ RAM & 32 G \\ Deep Learning Framework & Pytorch 1.12 \\ Python & 3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental platform configuration. training process, an SGD optimizer was used. Taking Mask2 Former as an example, the semantic segmentation results of 3D reconstructed data are shown Figure 5 below. Pixel accuracy (\(P_{A}\)) is a metric that quantifies the proportion of correctly classified pixels in an image segmentation output relative to the total number of pixels.