Web: http://arxiv.org/abs/2209.07121

Sept. 16, 2022, 1:15 a.m. | Alvari Seppänen, Risto Ojala, Kari Tammi

cs.CV updates on arXiv.org arxiv.org

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 …

arxiv denoising weather

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