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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com