LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying Out-Of-Distribution (OOD) points in a LiDAR point cloud is challenging as point clouds lack semantically rich features when compared with RGB images. We revisit this problem from the perspective of selective classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any known categories but learns a point-wise abstaining penalty with a marginbased loss. Synthesizing outliers to approximate unlimited OOD samples is also critical to this idea, so we propose a strong synthesis pipeline that generates outliers originated from various factors: unrealistic object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve state-of-the-art results. Risk-coverage analysis further reveals intrinsic properties of different methods. Codes and models will be publicly available.

翻译：基于LiDAR的语义场景理解是现代自动驾驶感知栈中的一个重要模块。然而，在LiDAR点云中识别分布外（OOD）点具有挑战性，因为与RGB图像相比，点云缺乏语义丰富的特征。我们从选择性分类的角度重新审视这一问题，该分类方法将选择性函数引入标准闭集分类设置中。我们的解决方案基于从任何已知类别中弃权的基本思想，但通过基于间隔的损失学习逐点弃权惩罚。合成异常值以近似无限的OOD样本对这一思想也至关重要，因此我们提出一个强大的合成流程，生成源于多种因素的异常值：不现实的对象类别、采样模式和大小。我们证明，针对不同类型的（合成）异常值学习不同的弃权惩罚（除逐点惩罚外）可以进一步提升性能。我们在SemanticKITTI和nuScenes数据集上对方法进行基准测试，并取得了最先进的结果。风险覆盖分析进一步揭示了不同方法的内在特性。代码和模型将公开发布。