Tree Reconstruction

Tree reconstruction aims to create accurate 3D models of trees from various data sources, including images, LiDAR scans, and even neural network model weights, with objectives ranging from forest inventory to phylogenetic analysis. Current research explores diverse approaches, employing techniques like minimum spanning trees, denoising diffusion of implicit neural fields, and reinforcement learning to optimize tree structure inference from point clouds, images, or model weight distributions. These advancements have significant implications for precision agriculture, forestry management, medical imaging (e.g., reconstructing anatomical trees), and evolutionary biology (e.g., reconstructing phylogenetic trees), enabling more efficient and accurate analysis of complex tree structures.