Semantic segmentation of LiDAR point clouds has received significant attention due to its applications in autonomous driving, forestry, and urban planning. Despite their potential, accurately classifying three-dimensional points remains a significant challenge due to the irregular distribution of data and density variation. To address this, state-of-the-art approaches use various techniques, such as voxelization, point-based networks, and graph-based methods. However, these techniques have limitations regarding the point cloud size they can handle and can be computationally expensive. Therefore, in this work, we propose a method to process point clouds of different scales and densities for point classification.