3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to real-world corruptions caused by adverse weathers, sensor noises, etc., provoking concerns about the safety and reliability of autonomous driving systems. To comprehensively and rigorously benchmark the corruption robustness of 3D detectors, in this paper we design 27 types of common corruptions for both LiDAR and camera inputs considering real-world driving scenarios. By synthesizing these corruptions on public datasets, we establish three corruption robustness benchmarks -- KITTI-C, nuScenes-C, and Waymo-C. Then, we conduct large-scale experiments on 24 diverse 3D object detection models to evaluate their corruption robustness. Based on the evaluation results, we draw several important findings, including: 1) motion-level corruptions are the most threatening ones that lead to significant performance drop of all models; 2) LiDAR-camera fusion models demonstrate better robustness; 3) camera-only models are extremely vulnerable to image corruptions, showing the indispensability of LiDAR point clouds. We release the benchmarks and codes at https://github.com/kkkcx/3D_Corruptions_AD. We hope that our benchmarks and findings can provide insights for future research on developing robust 3D object detection models.

翻译：3D目标检测是自动驾驶中感知周围环境的重要任务。尽管现有3D检测器性能优异，但面对恶劣天气、传感器噪声等真实世界干扰时缺乏鲁棒性，引发了对自动驾驶系统安全性和可靠性的担忧。为全面严谨地基准测试3D检测器的干扰鲁棒性，本文基于真实驾驶场景，针对LiDAR和摄像头输入设计了27种常见干扰类型。通过在公开数据集上合成这些干扰，我们建立了三个干扰鲁棒性基准——KITTI-C、nuScenes-C和Waymo-C。随后，我们对24种不同的3D目标检测模型进行了大规模实验，以评估其干扰鲁棒性。基于评估结果，我们得出若干重要发现，包括：1）运动级干扰是最具威胁性的干扰类型，导致所有模型性能显著下降；2）LiDAR-摄像头融合模型展现出更好的鲁棒性；3）仅依赖摄像头的模型对图像干扰极为脆弱，凸显了LiDAR点云的不可或缺性。我们在https://github.com/kkkcx/3D_Corruptions_AD发布了基准测试和代码。希望我们的基准和发现能为未来开发鲁棒3D目标检测模型的研究提供启示。