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

本文提出了一种新颖的雨水模拟方法 DRET，将动力学与雨天环境理论相结合，以提供一种经济高效的方法来扩展用于 3D 检测训练的现实雨水数据。此外，本文还提出了一种从晴天到雨天的知识蒸馏方法（SRKD），以增强在雨天条件下的 3D 检测能力。大规模数据集 WaymoOpenDataset 上的广泛实验表明，将我们提出的框架与最先进的 DSVT 模型和其他经典 3D 检测器相结合，可以显著改善检测准确性，同时不会损失效率。值得注意的是，我们的框架还能提高在晴天条件下的检测能力，因此无论天气是雨天还是晴天，都提供了一种强大的 3D 检测解决方案。