## 2 Related Work
### Point Cloud Segmentation  
The proposed PointNet made the deep learning-based point cloud analysis task enter a rapid development stage [6]. PointNet learns the rotational transformation properties of a point cloud by T-Net, uses a multi-layer perceptron (MLP) to construct the model, uses pooling to obtain global features, and finally combines the point features with the global features, which shows satisfactory results on semantic segmentation tasks. Subsequently, in order to utilize the local information of the neighborhood of the point cloud, the PointNet++ model was proposed to aggregate the neighboring point features within the sphere radius through the PointNet pooling operation [7]. [PERSON] et al. studied the downsampling of point cloud data and fused the local information of the point cloud through local spatial coding and attention pooling methods [8]. [PERSON] et al. proposed RandLA-Net [9], argued that the random sampling method can achieve better results while significantly saving memory and computational costs, and designed a local spatial coding and attention pooling method, which better fuses the local information of the point cloud and retains the fine geometric information of the point cloud. Due to the irregularity of the point cloud, [PERSON] et al. proposed the VoxelNet model by using voxelization [10], and the model is mainly composed of Voxel Feature Encoding (VFE) and a Region Proposal Network (RPN). However, a fixed voxel grid configuration may lead to challenges arising from the varying sparsity of point clouds generated by different Lidar sensors and scenes. Also, voxel resolution and boundary artifacts caused by point cloud partitioning limit performance. [PERSON] et al. proposed SegCloud [11], which employs trilinear interpolation combined with conditional random fields to better learn the fine-grained information in the scene and remap the coarse voxel information into the point cloud via 3D-FCNN [12].  
Due to the high complexity of 3D convolutional operations, projection-based methods can benefit 2D image deep learning techniques by converting point clouds into 2.5D structured image arrays or projecting 3D coordinates onto 2D planes, and are also gaining rapid development. [PERSON] et al. proposed SqueezeSeg [13], which is widely used in the task of road object segmentation for point cloud data in autonomous driving. [PERSON] et al. proposed RangeNet++ to explicitly addresses the semantic segmentation problem for rotated 3D Lidar, which effectively solves the object scale problem by using semantic reconstruction of raw points [14]. In order to make better use of neighborhood features, SalsaNext [15] introduced a context-aware module to extract richer feature representations by considering the neighborhood information of the points in the point cloud, and fused features at different scales through a multi-scale pyramid structure for semantic objects of different sizes and shapes. [PERSON] et al. proposed LMNet for motion target segmentation by combining multi-frame point cloud data using projected images and motion residuals [4]. In order to better utilize the motion residual data, [PERSON] et al. proposed a motion attention module to optimize the LMNet [16].