An unsupervised semantic segmentation network for wood-leaf separation in 3D point clouds.

ABSTRACT: Separating wood and leaf components in tree point clouds is one of the key tasks for achieving automated forest inventory and management. To obtain accurate wood‒leaf separation results, traditional methods typically rely on large amounts of annotated point cloud data to train supervised semantic segmentation networks. However, point wise annotation is not only extremely labor-intensive but also time-consuming and costly, which greatly limits the widespread application and adoption of supervised learning methods in wood‒leaf separation tasks. To eliminate the dependence on annotated point clouds, this study explores the feasibility of wood‒leaf separation under completely unsupervised conditions. To this end, we propose an unsupervised semantic segmentation network that is capable of direc

SUBMITTER: Zhong Y 

PROVIDER: S-EPMC12709894 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature