  Despite the increasing popularity of LiDAR sensors, perception algorithms
using 3D LiDAR data struggle with the 'sensor-bias problem'. Specifically, the
performance of perception algorithms significantly drops when an unseen
specification of LiDAR sensor is applied at test time due to the domain
discrepancy. This paper presents a fast and flexible LiDAR augmentation method
for the semantic segmentation task, called 'LiDomAug'. It aggregates raw LiDAR
scans and creates a LiDAR scan of any configurations with the consideration of
dynamic distortion and occlusion, resulting in instant domain augmentation. Our
on-demand augmentation module runs at 330 FPS, so it can be seamlessly
integrated into the data loader in the learning framework. In our experiments,
learning-based approaches aided with the proposed LiDomAug are less affected by
the sensor-bias issue and achieve new state-of-the-art domain adaptation
performances on SemanticKITTI and nuScenes dataset without the use of the
target domain data. We also present a sensor-agnostic model that faithfully
works on the various LiDAR configurations.
