  LiDARs have been widely adopted to modern self-driving vehicles, providing 3D
information of the scene and surrounding objects. However, adverser weather
conditions still pose significant challenges to LiDARs since point clouds
captured during snowfall can easily be corrupted. The resulting noisy point
clouds degrade downstream tasks such as mapping. Existing works in de-noising
point clouds corrupted by snow are based on nearest-neighbor search, and thus
do not scale well with modern LiDARs which usually capture $100k$ or more
points at 10Hz. In this paper, we introduce an unsupervised de-noising
algorithm, LiSnowNet, running 52$\times$ faster than the state-of-the-art
methods while achieving superior performance in de-noising. Unlike previous
methods, the proposed algorithm is based on a deep convolutional neural network
and can be easily deployed to hardware accelerators such as GPUs. In addition,
we demonstrate how to use the proposed method for mapping even with corrupted
point clouds.
