PosE-Enhanced Point Transformer with Local Surface Features (LSF) for Wood–Leaf Separation

Wood–leaf separation from forest LiDAR point clouds is a challenging task due to the complex and irregular structures of tree canopies. Traditional machine vision and deep learning methods often struggle to accurately distinguish between fine branches and leaves. This challenge arises primarily from the lack of suitable features and the limitations of existing position encodings in capturing the unique and intricate characteristics of forest point clouds. In this work, we propose an innovative approach that integrates Local Surface Features (LSF) and a Position Encoding (PosE) module within the Point Transformer (PT) network to address these challenges. We began by preprocessing point clouds and applying a machine vision technique, supplemented by manual correction, to create wood–leaf-separated datasets of forest point clouds for training. Next, we introduced Point Feature Histogram (PFH) to construct LSF for each point network input, while utilizing Fast PFH (FPFH) to enhance computational efficiency. Subsequently, we designed a PosE module within PT, leveraging trigonometric dimensionality expansion and Random Fourier Feature-based Transformation (RFFT) for nuanced feature analysis. This design significantly enhances the representational richness and precision of forest point clouds. Afterward, the segmented branch point cloud was used to model tree skeletons automatically, while the leaves were incorporated to complete the digital twin. Our enhanced network, tested on three different types of forests, achieved up to 96.23% in accuracy and 91.51% in mean intersection over union (mIoU) in wood–leaf separation, outperforming the original PT by approximately 5%. This study not only expands the limits of forest point cloud research but also demonstrates significant improvements in the reconstruction results, particularly in capturing the intricate structures of twigs, which paves the way for more accurate forest resource surveys and advanced digital twin construction.