When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

翻译：在自动驾驶等安全关键应用中部署激光雷达语义分割模型时，理解和提升其对多种激光雷达数据损坏的鲁棒性至关重要。本文旨在全面分析激光雷达语义分割模型在各种损坏条件下的鲁棒性。为严格评估当前方法的鲁棒性与泛化能力，我们提出名为SemanticKITTI-C的新基准，包含三大类共16种域外激光雷达损坏：恶劣天气、测量噪声以及跨设备差异。随后，我们系统研究了11种激光雷达语义分割模型，重点涵盖不同输入表征（如点云、体素、投影图像等）、网络架构及训练方案。通过本项研究，我们获得两项启示：1）输入表征对鲁棒性起关键作用，具体而言，不同表征在不同损坏条件下表现各异；2）当前最先进的激光雷达语义分割模型虽在干净数据上取得优异结果，但在处理含噪数据时鲁棒性显著下降。基于上述发现，我们设计了一种鲁棒激光雷达分割模型（RLSeg），通过简单而有效的改进大幅提升了鲁棒性。本基准、系统性分析及观测结果有望推动面向安全关键应用的鲁棒激光雷达语义分割领域未来研究。