## 4 Experiment
### Experimental Results
#### 4.3.3 Multilayer Receptive Field Module Experiment Result  
This paper intends to further verify the effectiveness of the MRFFM we designed, that is, whether the module can effectively segment objects of different scales in the scene. In this section, the inference results of MAPC-Net-1, whose MRFFM is removed, and MAPC-Net are compared. The quantitative and the qualitative results are shown in Table 4 and Figure 7.  
It can be seen from Table 4 that MRFFM significantly improves the mIoU in both Test A and B. In Test A, the network trained with MRFFM improved by 2% on the IoU of \"grass\", 4% on the IoU of \"rocks\", and 4% on the IoU of \"people\". In Test B, the network trained with MRFFM improved by 3% on the IoU of \"grass\", 9% on the IoU of \"rocks\", and 6% on the IoU of \"people\". The results show the effectiveness of MRFFM in the segmentation of objects of different scales.  
The results in Figure 7 demonstrate the effectiveness of the multi-layer receptive field fusion module. Due to the three convolution kernels of different sizes in this module, information of different scales can be extracted more effectively. Furthermore, objects of different scales can be segmented more effectively. Especially for small and medium objects, the addition of MRFFM significantly increases the segmentation results of these classes, which greatly improves the performance of off-road environments.  
To sum up, the performance of MAPC-Net, SalsaNext, and PVCNN are compared and tested in this section. As the experimental results show, relying on the method of directly extracting features in three-dimensional space and the design of the Cylinder, MAPC-Net performs better in off-road scenes compared with the projection-based method and the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & & \multicolumn{4}{c}{IoU (\%)} & \multirow{2}{*}{**mIoU (\%)**} \\  & & **Grass** & **Mud** & **Vegetation** & **Rocks** & **Water** & **People** & **mIoU (\%)** \\ \hline \multirow{3}{*}{A} & MAPC-Net-1 & 97.86 & **84.24** & **24.37** & 45.63 & 65.82 & 50.74 & 61.44 \\  & MAPC-Net & **99.98** & 83.70 & 23.57 & **50.76** & **68.11** & **56.30** & **63.74** \\ \cline{1-1}  & MAPC-Net-1 & 62.37 & **17.28** & 81.07 & 75.52 & **32.24** & 72.65 & 56.86 \\ \cline{1-1}  & MAPC-Net & **65.18** & 16.09 & **85.40** & **84.58** & 31.09 & **88.51** & **61.81** \\ \hline \end{tabular}
\end{table}
Table 4: The IoU changes with the multilayer receptive field module.  
Figure 7: The results in the figure demonstrate the effectiveness of the multi-layer receptive field fusion module. The MAPC-Net with multi-layer receptive field module can more effectively identify obstacles of different scales. Among them, red represents people in small objects, vehicles in medium objects, and roads in large objects.  
point-voxel-based method. Additionally, the experiments illustrate the positive effects of the data augmentation module and the MRFFM on MAPC-Net. Among them, the MRFFM can help the network capture the features of objects of different scales, to segment them more accurately. Additionally, the data augmentation module effectively improves the segmentation accuracy of objects with a small number of point clouds. Therefore, according to the results, MAPC-Net can effectively perform semantic segmentation tasks in off-road scenes.