# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 1 Introduction  
In recent years, significant advancements have been witnessed in the field of 3D computer vision, which has become a subject of extensive research. Various formats, including meshes, volumetric meshes, depth images, and point clouds, can be utilized to represent 3D data [1]. Point clouds offer an unorganized sparse depiction of a 3D point set while preserving the original geometric information of an object in 3D space. Their representation is characterized by its simplicity, flexibility, and retention of most information without the need for discretization. The rapid development of 3D sensortechnology, including various 3D scanners and LiDARs, has facilitated the acquisition of point cloud data [2]. Owing to its abundant geometric, shape, and scale information, 3D point clouds are crucial for scene understanding and find application in diverse fields such as autonomous driving, robotics, 3D reconstruction, and remote sensing, such as through RN4 and RN5. However, the disorder and irregularity inherent in 3D point cloud data present challenges for deep learning-based point cloud feature extraction methods, which play a vital role in various point cloud processing tasks. Numerous approaches have been proposed to transform point clouds into regular structures, such as projecting into multiview images [3; 4] and voxelization [5; 6]. Although these methods have shown superior results in point cloud classification and segmentation tasks compared to traditional manual feature extraction techniques, they compromise the intrinsic geometric relationships of 3D data during processing. Moreover, the computational complexity of voxelization, being proportional to the cube of the volume, limits its application in more complex scenes. To address these challenges, researchers have started considering the direct processing of raw point cloud data to reduce computational complexity and to fully leverage the characteristics of 3D point cloud data. PointNet [7] directly processes raw data by extracting point cloud features through MLP (MultiLayer Perceptron) and max pooling, thereby ensuring permutation invariance of the point cloud. Although the processing method is simple, it yields significant results and has become an important theoretical and ideological foundation in 3D point cloud processing. PointNet++ [8] extends PointNet by considering both global and local features. It obtains key point sets through farthest point sampling (FPS) and constructs a local graph using k-nearest neighbors (k-NN). Subsequently, MLP and max pooling are employed to aggregate the local features. Since PointNet++, the main trend in deep learning-based point cloud processing methods has been to add advanced local operators and extend the trainable parameters, and while the performance gain rises by the amount of parameters added, so does the cost of computing resources, and deep learning training is often time-consuming. Many previous works have approached deep learning from a lightweight perspective in order to efficiently address the training and inference time issues of deep learning. For example, MobileNet [9] uses depthwise separable convolution to build a lightweight network, which improves the overall network accuracy and speed; UL-DLA [10] proposes an ultralightweight deep learning architecture. It forms a Hybrid Feature Space (HFS), which is used for tumor detection using a Support Vector Machine (SVM), thereby culminating in high prediction accuracy and optimum false negatives. Point-NN [11] proposes a new approach to nonparametric point cloud analysis that employs simple trigonometric functions to reveal local spatial patterns and a nonparametric encoder for networks to extract the training set features, which are cached as a point feature repository. Finally, the point cloud classification is accomplished using naive feature matching. However, its simple use of trigonometric functions in the process of feature embedding is blind and may lead to the neglect of key features.