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.

基于 LiDAR 的语义场景理解是现代自动驾驶感知系统中的一个重要模块。然而，LiDAR 点云中的区分开分布点 (OOD) 是具有挑战性的，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了这个问题，该分类在标准的封闭集分类设置中引入了一个选择性函数。我们的解决方案基于一种基本思想，即避免选择任何已知类别，而是以边际损失学习点处的避免惩罚。为了近似无限的 OOD 样本，合成异常点对于这个思想也至关重要，因此我们提出了一个强大的合成流程，可以生成来自各种因素的异常值：不真实的对象类别，采样模式和尺寸。我们证明了在不同类型（合成）异常值的不同点处避免惩罚可以进一步提高性能。我们在 SemanticKITTI 和 nuScenes 上对我们的方法进行了基准测试，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固有特性。代码和模型将公开提供。