Particle accelerator simulators, pivotal for acceleration optimization, are constrained by computational time.While current machine learning surrogate models for particle accelerator simulators offer efficiency, they lack in-depth positional data for individual particles.Drawing from 3D computer vision, we propose adapting the point cloud deep learning methods, adept at processing 3D point sets, to model particle beams.