Recent advances in UAV-based remote sensing have made high-resolution 2D imagery widely available, however the extraction of 3D tree structure from such data remains a primary challenge. This paper presents a novel framework for deriving True Tree Crown (TTC) geometry from Pseudo Tree Crown (PTC) representations, through a graph-based learning model. The PTC is generated from single nadir RGB images by interpreting grayscale intensity as height. This serves as an intermediate 2.5D representation that bridges the gap between conventional imagery and full 3D structure. We establish a spatial correlation between PTC and LiDAR-derived TTC meshes using geometric feature extraction and correspondence analysis. Preliminary results on synthetic data demonstrate a strong correlation between PTC and TTC height distributions, confirming that PTC encodes meaningful structural information. To learn the mapping from PTC to TTC, we propose a Graph Neural Network architecture with three GraphConv layers (64 – 128 – 256 channels), residual connections, and a composite loss function combining Chamfer distance with Laplacian and edge regularization. This framework enables the estimation of complete 3D tree crowns from single RGB images, transforming vast historical 2D image archives into valuable 3D forest data for ecological monitoring, carbon accounting, and sustainable forest management.