  Robust point cloud parsing under all-weather conditions is crucial to level-5
autonomy in autonomous driving. However, how to learn a universal 3D semantic
segmentation (3DSS) model is largely neglected as most existing benchmarks are
dominated by point clouds captured under normal weather. We introduce
SemanticSTF, an adverse-weather point cloud dataset that provides dense
point-level annotations and allows to study 3DSS under various adverse weather
conditions. We study all-weather 3DSS modeling under two setups: 1) domain
adaptive 3DSS that adapts from normal-weather data to adverse-weather data; 2)
domain generalizable 3DSS that learns all-weather 3DSS models from
normal-weather data. Our studies reveal the challenge while existing 3DSS
methods encounter adverse-weather data, showing the great value of SemanticSTF
in steering the future endeavor along this very meaningful research direction.
In addition, we design a domain randomization technique that alternatively
randomizes the geometry styles of point clouds and aggregates their embeddings,
ultimately leading to a generalizable model that can improve 3DSS under various
adverse weather effectively. The SemanticSTF and related codes are available at
\url{https://github.com/xiaoaoran/SemanticSTF}.
