  Employing data augmentation methods to enhance perception performance in
adverse weather has attracted considerable attention recently. Most of the
LiDAR augmentation methods post-process the existing dataset by physics-based
models or machine-learning methods. However, due to the limited environmental
annotations and the fixed vehicle trajectories in the existing dataset, it is
challenging to edit the scene and expand the diversity of traffic flow and
scenario. To this end, we propose a simulator-based physical modeling approach
to augment LiDAR data in rainy weather in order to improve the perception
performance of LiDAR in this scenario. We complete the modeling task of the
rainy weather in the CARLA simulator and establish a pipeline for LiDAR data
collection. In particular, we pay special attention to the spray and splash
rolled up by the wheels of surrounding vehicles in rain and complete the
simulation of this special scenario through the Spray Emitter method we
developed. In addition, we examine the influence of different weather
conditions on the intensity of the LiDAR echo, develop a prediction network for
the intensity of the LiDAR echo, and complete the simulation of 4-feat LiDAR
point cloud data. In the experiment, we observe that the model augmented by the
synthetic data improves the object detection task's performance in the rainy
sequence of the Waymo Open Dataset. Both the code and the dataset will be made
publicly available at https://github.com/PJLab-ADG/PCSim#rainypcsim.
