In recent years, there has been a significant increase in the utilization of deep learning methods, particularly convolutional neural networks (CNNs), which have emerged as the dominant approach in various domains that involve structured grid data, such as picture analysis and processing. Nevertheless, the exponential growth in the utilization of LiDAR and 3D sensors across many domains has resulted in an increased need for the analysis of 3D point clouds. The utilization of 3D point clouds is crucial in various applications, including object recognition and segmentation, as they offer a spatial depiction of things within a three-dimensional environment. In contrast to photos, point clouds exhibit sparsity and lack a regular grid, hence posing distinct processing and computational issues.

翻译：近年来，深度学习方法的应用显著增长，尤其是卷积神经网络（CNN）已成为图像分析处理等结构化网格数据领域的主流方法。然而，随着激光雷达和三维传感器在众多领域的广泛应用，对三维点云分析的需求日益迫切。三维点云在物体识别与分割等应用中至关重要，因其能提供三维环境中物体的空间表征。与图像不同，点云具有稀疏性且缺乏规则网格结构，这带来了独特的处理与计算挑战。