This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that gener- ates collision-free trajectories in environments populated with both static and dynamic obstacles, leveraging point cloud data. Our approach consists of a 2-fork neural network fed with sensing and localization data, able to communicate intermediate learned features among the agents. One network branch crafts an initial collision-free trajectory estimate, while the other devises a neural collision constraint for subsequent optimiza- tion, ensuring trajectory continuity and adherence to physical actuation limits. Extensive simulations in challenging cluttered environments, involving up to 25 robots and 25% obstacle density, show a collision avoidance success rate in the range of 100 − 85%. Finally, we introduce a saliency map computation method acting on the point cloud data, offering qualitative insights into our methodology.