  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.
