## 2 Related Works  
This section will introduce related works on three aspects: point cloud object recognition, point cloud object recognition for distant or small objects, and data augmentation for point cloud object recognition. **Point cloud object recognition. **  
Point cloud object recognition encompasses both point-based and grid-based methods. Point-based methods [1; 2; 3; 4; 5; 6; 7] process points directly to recognize objects. Among these, PointNet [1] represents a pioneering work that laid the foundation for subsequent research. It was the first to employ neural networks to directly handle point clouds and extract features from them. PointNet++ [2] further improved upon PointNet by enabling the network to extract not only local but also global features. Subsequent studies often utilized these works [1,2] as the backbone for feature extraction from point clouds. This paper also employs a simplified version of PointNet for feature extraction from point clouds. Works [3; 4; 5] focused on feature extraction and object proposal generation during the first stage, followed by proposal refinement for classification and bounding box regression in the second stage. PointRCNN [3] segments the point clouds into foreground and background in the first stage, generating a small number of 3D proposals. Subsequently, in the second stage, it extracts local features and integrates them with the global semantic features obtained in the first stage, enabling the accurate prediction of bounding boxes. VoteNet [4] leverages PointNet++ [2] for feature extraction from point clouds, then uses a voting module to generate votes based on the features of seed points and aggregates these vote features to form object proposals. STD [5] also uses PointNet++ [2] for feature extraction in the first stage, but it voxelizes the proposals in the second stage before classification, combining the advantages of both point-based and voxel-based methods, thereby enhancing the recognition speed. Moreover, 3 DSSD [6] eliminates the refinement modules and feature propagation layers used in previous works, proposing a distance-based feature sampling method (F-FPS) to preserve object points and remove background points, and introduces an anchor-free regression head, significantly increasing the speed of the method beyond previous point-based approaches. Point-GNN [7] innovatively applies a graph neural network to 3D recognition, encoding the point clouds into a graph representation and utilizing the designed graph neural network for object identification, demonstrating the potential of using graph neural networks for object recognition. While point-based methods have garnered considerable research attention, their high computational resource requirements have led to increased interest in grid-based methods. Grid-based methods have received extensive research attention and are categorized into two primary approaches: voxel-based and pillar-based methods. The voxel-based approach [8; 9; 10; 11; 12; 13; 14] typically encodes point clouds into voxels and employs 3D backbones to extract features, followed by 2D convolution (e.g., RPN [25]) for classification and regression tasks. VoxelNet [8] represents a seminal work in this domain, utilizing Voxel Feature Encoding (VFE) to transform point clouds into voxels and leveraging 3D convolution to extract features. However, 3D convolution is computationally expensive. Thus, Second: Sparsely Embedded Convolutional Detection [9] adopts 3D sparse convolution (SpConv) for feature extraction, thereby enhancing computational efficiency. Voxel R-CNN [10] further introduces Voxel RoI pooling to directly extract RoI features from voxel features, aiming to reduce the computational cost. PDV [11] proposes a Density-Aware RoI Grid Pooling method that captures the density information of local point clouds for better refinement in the second stage.