Terrestrial laser scanning (TLS) has emerged as a valuable technology for forest monitoring, providing detailed 3D measurements of vegetation structure. However, the semantic understanding of tropical tree point clouds, particularly the separation of woody and non-woody components, remains a challenge. Therefore, this paper addresses the gaps in both (1) data availability and (2) knowledge regarding the potential of 3D deep learning algorithms for leaf-wood segmentation of tropical tree point clouds. First, we contribute a new dataset consisting of 148 tropical tree point clouds with manual leaf-wood annotations. Second, we present initial results using the RandLA-Net 3D deep learning architecture to establish a benchmark on our dataset, achieving a mean intersection over union (mIoU) of 86.8% and overall accuracy of 94.8%. Visual inspection of predictions reveals areas of confusion and indicates applicability across different forest types. Our study demonstrates the potential of 3D deep learning for leaf-wood segmentation in tropical tree point clouds and highlights avenues for future research, including exploring different architectures and investigating the influence of prediction errors on volumetric tree reconstruction.