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

翻译：基于LiDAR的3D目标检测模型在雨雾条件下常因信号衰减与噪声干扰而性能受限。现有研究试图通过模拟雨噪声提升模型鲁棒性，但模拟雨数据与实际雨数据点之间存在显著差异。本文提出一种名为DRET的新型降雨模拟方法，融合动力学与雨环境理论，以低成本扩展可用于3D检测训练的真实雨数据。此外，我们提出晴到雨知识蒸馏（SRKD）方法，以增强雨雾条件下的3D检测性能。在大规模WaymoOpenDataset数据集上的大量实验表明，当与当前最先进的DSVT模型及其他经典3D检测器结合时，本框架在保持效率的同时显著提升了检测精度。值得注意的是，该框架在晴朗条件下同样改善了检测能力，从而为不同天气（雨/晴）下的3D检测提供了鲁棒性解决方案。