In this paper, a method exploiting aerial LiDAR point clouds to build realistic building meshes suitable for electromagnetic simulation is proposed. One of the main challenges lies in reconstructing regularized building meshes with low polygonal density. Optimization-based methods, commonly used for building reconstruction from point clouds, are highly data-driven, making the quality of results dependent on the quality of input data. Aerial LiDAR scans can be incomplete or sparse, for instance due to occlusion. A novel LoD2 buildings reconstruction method based on deep learning is proposed, assuming that deep learning methods are more robust to incomplete or sparse data than optimization-based methods. A parametric building model is introduced, based on the Weighted Straight Skeleton algorithm, which generates realistic roofs from a building footprint and an associated set of slopes, and subsequently extrudes the roof to the specified building height. This parametric approach guarantees that a given set of parameters (height, footprint and slopes) produces a regularized building mesh with low polygonal density. A multimodal model, named Point2WSS, was trained to recover the variable number of building’s continuous parameters from its corresponding point cloud. This approach enables the generation of realistic building meshes suitable for electromagnetic simulation, if the predicted parameters accurately approximate real-world values. All the code and datasets used in this paper are available at : <code>https://github.com/KWIKERRR/point2wss</code>.