The generation of high-fidelity 3D point clouds is a critical yet challenging task, particularly for complex natural objects. This paper presents a systematic comparative analysis of five state-of-the-art generative architectures, including l-GANs, PointFlow, a Diffusion Probabilistic Model (DPM), and the autoregressive CanonicalVAE, on the specific task of synthesizing eucalyptus tree point clouds. To facilitate this investigation, we introduce Euca3D, a novel benchmark dataset of 440 real-world eucalyptus tree scans. Through rigorous quantitative and qualitative evaluation, our findings reveal that DPM and CanonicalVAE emerge as the most effective models. While DPM demonstrates superior performance in surface fidelity as measured by the Chamfer Distance, CanonicalVAE excels at preserving global structural integrity and perceptual quality, achieving the best results on Earth Mover’s Distance-based metrics. Visually, the point clouds generated by CanonicalVAE are of exceptional quality, nearly indistinguishable from the reference data. This study demonstrates that generating high-quality synthetic point cloud data of complex natural forms is achievable, establishing a strong foundation for data augmentation to enhance downstream tasks in forestry and computer vision.