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:当使用LiDAR 语义分解模型进行自主驱动等安全关键应用时,必须了解并改进这些模型对于利达AR 大量腐败的稳健性。 在本文中,我们的目标是全面分析利达AR 语义分解模型在各种腐败情况下的稳健性。为了严格评估当前方法的稳健性和可概括性,我们提出了一个新的基准,称为SemantiKITTI-C, 其特点是三个组中16种外部的利达AR腐败, 即不利的天气、 测量噪音和交叉偏差。 然后,我们系统地调查11个利达AR 语义分解模型, 特别是涵盖不同的输入表达( 如点云、 voxel、 预测图像等 ) 、 网络架构和培训计划的稳健性。 我们通过这项研究发现, 投入代表在稳健性方面发挥着关键作用。 具体地说, 在具体的腐败下, 不同的表达方式可以不同。 2 尽管利达AR 语义分解( ) 的状态- 方法是推进性分解模型观测,, 特别是在我们进行稳妥性数据分析时, 稳健健性数据 。