  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.
