Research on Tree Point Cloud Enhancement Based on Deep Learning

The acquisition of high-quality tree point cloud datasets facilitates research in various forestry fields, including tree species classification, diversity monitoring, and biomass estimation. However, due to limitations in sensor performance and occlusion between trees, tree point clouds acquired using LiDAR scanners often exhibit missing data. This not only degrades the quality of the point clouds, but also significantly reduces the number of usable samples. Therefore, this study proposed a tree point cloud enhancement system, which included the completion network and the sample augmentation network. The point cloud completion network utilized a transformer-based improved module to predict missing point clouds and combined up-sampling processing to progressively complete the point clouds from coarse to fine. This could improve the subsequent model decisions and performance through data balancing. On the other hand, the sample augmentation network, based on an adversarial learning strategy, separately constructed the generator and the classifier. By applying shape transformations, point displacements, and point drop to complete point cloud samples, the learnable parameters in the generator and the classifier were alternately optimized. This process enhanced both the quality and the quantity of the tree point cloud dataset. In addition, this study introduced a multi-head attention pooling layer, which further enhanced the joint network’s ability to learn and extract tree structural features. The experimental results showed that the completion network successfully restored missing tree point clouds of various types, achieving an average Chamfer Distance of 4.84 and an average F-score of 0.90. The experiments also demonstrated the effectiveness and robustness of the sample augmentation network, which improved classification accuracy by approximately 2.9% compared to the original dataset.