## 2 Related Work  
In this section, we will review the research related to dataset and 3D point cloud semantic segmentation. In recent years, due to the rapid development of deep learning, a variety of data sets applied to visual understanding have appeared, thus promoting the development of visual understanding. Most datasets utilize RGB cameras to capture 2D images with color features, for example [6; 7; 8; 9; 10], including images captured in different weather and lighting conditions. However, [PERSON] [11] has pioneered the provision of a multi-modal dataset KITTI, which provides dense point clouds, frontal stereo images, and GPS/IMU data from LiDAR sensors. Meanwhile, [PERSON] [12] also provides data composed of RGB and thermal cameras, RGB stereo sound, 3D LiDAR, and GPS/IMU. [PERSON] [13] has done the same work as well. [PERSON] [14] introduces the multi-view mode 3D detection dataset Nuscenes. [PERSON] [15] and [PERSON] [16] focus on creating datasets for pixel-level semantic segmentation tasks, including scene parsing, 3D car instance, and lane segmentation tasks. [PERSON] [17] focuses on 3D detection tasks using LiDAR. The existing methods for extracting features from 3D point clouds can generally be divided into three groups: projection-based [18; 19; 20; 21], voxel-based [22; 23; 24], and point-based [25; 26; 27; 28; 29]. Table 1 shows the relevant details. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Methods** & **Dataset Used** & **Characteristic** & **Limited** \\ \hline \multirow{4}{*}{Projection-based} & SnapNet [18] & Semantic3D & These methods project a 3D point cloud onto a 2D image plane. They can be segmented by using the mature 2D image processing technology. They have low computational complexity. & These methods lose some information in the process of projection. They have poor segmentation for complex 3D shapes. They select the appropriate projection angle and parameters manually. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of semantic segmentation methods for 3D point clouds. Figure 1: Framework of the LDE-Net. Projection-based methods first project the 3D point clouds onto a 2D plane, then use 2D convolution to obtain the semantic labels of each pixel, and finally fuse the multi-view semantic labels to obtain the semantic labels of each point. [PERSON] et al. [18] first projected a 3D point cloud onto 2D planes from multiple virtual camera views. To achieve fast and accurate segmentation of 3D point clouds, [PERSON] et al. [19; 20] proposed an end-to-end network based on SqueezeNet and SqueezeNetV2. [PERSON] et al. [21] proposed RangeNet++ for real-time semantic segmentation of LiDAR point clouds. Voxel-based methods voxelize the point clouds and then perform semantic segmentation using standard 3D convolution. [PERSON] and [PERSON] [22] first convert a point cloud into a group of voxels. Then, they input these data into 3D CNN for voxel segmentation. Finally, they assign all points within a voxel with the same label as the voxel. [PERSON] et al. [23] proposed a fully convolutional network (FCN) which extracts different levels of geometric relationships layer by layer from the point clouds using 3D convolution and weighted average. The FCN is used for feature extraction and long-range dependency integration, and it can effectively handle colored point clouds. [PERSON] et al. [24] proposed a deep learning network based on index structures that can significantly reduce both memory and computational costs. Point-based methods directly take the 3D point clouds into the network. [PERSON] et al. [25] designed a shared MLP to learn the features of each point. [PERSON] et al. [26] implemented orientation encoding and scale awareness using a three-stage and order-wise convolution method. This method effectively stacks and encodes information from eight spatial directions, which concatenates multi-scale features for adaptive processing of different scales. [PERSON] et al.