  Reliable point cloud data is essential for perception tasks \textit{e.g.} in
robotics and autonomous driving applications. Adverse weather causes a specific
type of noise to light detection and ranging (LiDAR) sensor data, which
degrades the quality of the point clouds significantly. To address this issue,
this letter presents a novel point cloud adverse weather denoising deep
learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time
dimension unlike deep learning adverse weather denoising methods in the
literature. It performs about 10\% better in terms of intersection over union
metric compared to the previous work and is more computationally efficient.
These results are achieved on our novel SnowyKITTI dataset, which has over
40000 adverse weather annotated point clouds. Moreover, strong qualitative
results on the Canadian Adverse Driving Conditions dataset indicate good
generalizability to domain shifts and to different sensor intrinsics.
