Point Cloud Segmentation and Recognition Deep Learning Dataset for Ornamental trees and Shrubs Environment
The rapid collection and real time detection of forest resources information is the key to improving the efficiency of forestry survey and realizing the intelligent cultivation and harvesting of under forest resources. In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. Few prior works study deep learning on point sets. The 3D point cloud classification in urban scenes has been widely applied in the fields of automatic driving, map updating, change detection, etc. Accurate and effective classification of mobile laser scanning (MLS) point clouds remains a big challenge for these applications. The existing point cloud processing algorithms have poor generalization ability, weak robustness, low precision, and poor real time performance, which are urgent scientific problems to be solved. PointNet is a pioneer in this direction. In this paper a dataset produced by FARO 3D laser scanner was generated to test its ability to recognize fine grained patterns like artificial pruning Ornamental trees and shrubs. As the result, the typical spherical bush segmentation in a big scene could be precisely acquire throughout the Pointnet method. It shows a great potential in identify and segment complex forest environment point clouds in the future.