The ability to grasp objects is essential in multiple contexts, ranging from the mimic of human activities to automation of industrial tasks. Recent works based on datasets obtained through simulation have proven to achieve promising performances on unseen objects also in real-case scenarios. Nevertheless, many of them still strongly rely on approaches built on top of ad-hoc geometric heuristics to generate grasp candidates, failing in generalizing to different settings and making it hard to reproduce the same approach in different environments. Moreover, some of the heuristics require different and sometimes independent modules to reduce the redundancy of generated grasps. In this thesis, we propose a lightweight end-to-end solution for the generation of 6-DoF parallel-jaw pose grasps starting from partial view of the object which relies on completely learned sampling techniques, making it easy to translate the same approach to different datasets and settings.