  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.
