Tree Point Cloud

Tree point clouds, derived from laser scanning technologies, are increasingly used to extract detailed information about individual trees and forests. Current research focuses on developing deep learning models, such as PointNet++ and its variants, to perform tasks like tree species classification, wood-leaf segmentation, and branch skeletonization. These advancements enable automated analysis of large-scale forestry data, improving accuracy and efficiency in applications such as biomass estimation, forest management, and precision forestry. The availability of large, publicly accessible benchmark datasets is crucial for driving further progress in this rapidly evolving field.