  LiDAR sensors provide rich 3D information about their surrounding{s} and are
becoming increasingly important for autonomous vehicles tasks such as
{localization}, semantic segmentation, object detection, and tracking.
{Simulation} accelerates the testing, validation, and deployment of autonomous
vehicles while {also} reducing cost and eliminating the risks of testing in
real-world scenarios. We address the problem of high-fidelity LiDAR simulation
and present a pipeline that leverages real-world point clouds acquired by
mobile mapping systems. Point-based geometry representations, more specifically
splats {(2D oriented disks with normals)}, have proven their ability to
accurately model the underlying surface in large point clouds{, mainly with
uniform density}. We introduce an adaptive splat generation method that
accurately models the underlying 3D geometry {to handle real-world point clouds
with variable densities}, especially for thin structures. Moreover, we
introduce a {fast} LiDAR {sensor} simulator, {working} in the splatted model,
{that leverages} the GPU parallel architecture with an acceleration structure
while focusing on efficiently handling large point clouds. We test our LiDAR
simulation in real-world conditions, showing qualitative and quantitative
results compared to basic splatting and meshing techniques, demonstrating the
interest of our modeling technique.
