Addressing Scale and Density Challenges in LiDAR Point Classification

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.