  Existing LiDAR semantic segmentation methods often struggle with performance
declines in adverse weather conditions. Previous work has addressed this issue
by simulating adverse weather or employing universal data augmentation during
training. However, these methods lack a detailed analysis and understanding of
how adverse weather negatively affects LiDAR semantic segmentation performance.
Motivated by this issue, we identified key factors of adverse weather and
conducted a toy experiment to pinpoint the main causes of performance
degradation: (1) Geometric perturbation due to refraction caused by fog or
droplets in the air and (2) Point drop due to energy absorption and occlusions.
Based on these findings, we propose new strategic data augmentation techniques.
First, we introduced a Selective Jittering (SJ) that jitters points in the
random range of depth (or angle) to mimic geometric perturbation. Additionally,
we developed a Learnable Point Drop (LPD) to learn vulnerable erase patterns
with a Deep Q-Learning Network to approximate the point drop phenomenon from
adverse weather conditions. Without precise weather simulation, these
techniques strengthen the LiDAR semantic segmentation model by exposing it to
vulnerable conditions identified by our data-centric analysis. Experimental
results confirmed the suitability of the proposed data augmentation methods for
enhancing robustness against adverse weather conditions. Our method achieves a
notable 39.5 mIoU on the SemanticKITTI-to-SemanticSTF benchmark, improving the
baseline by 8.1\%p and establishing a new state-of-the-art. Our code will be
released at \url{https://github.com/engineerJPark/LiDARWeather}.
