## 3 Methods  
### Network Overview  
The overall structure of our designed multi-scale augmentation Point-Cylinder network (MAPC-Net) is shown in Figure 1. In outdoor scenes, LiDAR point cloud distribution is as follows: the closer the scene the more point clouds there are, the further the scene the fewer there are. However, the traditional voxel-based algorithm divides the entire space evenly, which leads to a large proportion of empty voxels, making the calculation based on voxels not stable and real-time. Inspired by the Cylinder algorithm [25], the MAPC-Net proposed in this paper uses the cylindrical coordinate system to replace the traditional 3D coordinate system during the encoding stage, that is, it uses Cylinder to replace the traditional voxel, and fuses it with the point cloud sequence for feature extraction. As shown in Figure 1, the Point-Cylinder structure designed in this paper takes into account the low computational complexity of point cloud sequences and the locality of the Cylinder, so that the network can obtain better semantic segmentation results in real-time. The network adopts an encoder-decoder structure. The encoding stage is used for feature extraction, and the decoding stage is used to restore the original size and output semantic segmentation results. Besides, skip connections are added to the network so that the recovered feature maps can have more low-scale features to obtain better semantic segmentation results. We use an asymmetric encoder-decoder structure in the network, where the PointTensor and Cylinder voxels are fused once in the encoding stage and twice in the decoding stage. The PointTensor needs more information from the Cylinder voxels in the decoding stage to ensure that the recovered feature map has more details to make the final output semantic segmentation result more accurate. In the cylindrical voxel part, 3D sparse convolution and two-layer residual modules are used as basic units in the encoder part, which can effectively extract features; in the decoder part, 3D deconvolution and two-layer residual modules are used as basic units. At the same time, cross-layer connections are added between the four layers of down-sampling and the four layers of up-sampling, and the high-level semantic features and fine-grained, low-level features are fused to enhance the up-sampling results. The point cloud sequence first passes through the MRFFM (multi-layer receptive field fusion module) to extract and fuse the feature information of large, medium, and small scales off-road. Then, PointTensor with shape (\(n\), 32) is obtained. After that, the multi-layer perceptron is directly used for processing. Although the method is simple, it can still extract fine-grained features in units of points, which is a good complement to the coarse-grained domain features extracted by cylindrical voxels. After feature extraction from PointTensor and Cylinder voxels, voxel-based features must be converted to point-based features for fusion. Generally, the most direct implementation is to assign Cylinder features to all points of the grid. However, this will result in the  
Figure 1: The overall structure of MAPC-Net. The original point clouds are first sent in the feature extraction of the PointTensor and Cylinder branches and then sent in the gated fusion module to obtain the semantic segmentation output. same point features within the same voxel, so we use trilinear interpolation to convert the Cylinder features to the point cloud feature format ensuring the features of each point are different. Finally, the features extracted from the PointTensor and the point-based features after trilinear interpolation are fused to obtain the final output. For two feature tensors, there are two methods for feature fusion: add and concatenate. The essence of feature fusion is to aggregate useful information together under the interference of useless information, and the above two feature fusion methods will be affected by non-informative features due to heterogeneous information sources.