3D computer vision has grown in popularity in the last few years due to its centrality in many innovative industries such as robotics and autonomous driving, and the application of Deep Learning techniques to the 3D medium has given rise to new challenges. Common applications found in literature that target 3D point clouds range from scene semantic segmentation, to object classification, to object part segmentation. This thesis focuses on the latter - a task that requires the classification of different clusters of the 3D object point cloud into object parts - and aims at exploring the type of impact self-supervised learning techniques can have on such task.