  LiDAR-based 3D object detection models have traditionally struggled under
rainy conditions due to the degraded and noisy scanning signals. Previous
research has attempted to address this by simulating the noise from rain to
improve the robustness of detection models. However, significant disparities
exist between simulated and actual rain-impacted data points. In this work, we
propose a novel rain simulation method, termed DRET, that unifies Dynamics and
Rainy Environment Theory to provide a cost-effective means of expanding the
available realistic rain data for 3D detection training. Furthermore, we
present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D
detection under rainy conditions. Extensive experiments on the WaymoOpenDataset
large-scale dataset show that, when combined with the state-of-the-art DSVT
model and other classical 3D detectors, our proposed framework demonstrates
significant detection accuracy improvements, without losing efficiency.
Remarkably, our framework also improves detection capabilities under sunny
conditions, therefore offering a robust solution for 3D detection regardless of
whether the weather is rainy or sunny
