Histogram-based Defogging Techniques for LiDAR

Sang, Tzu-Hsien; Tsai, Chia-Ming

doi:10.1109/icsict55466.2022.9963199

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2023

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“…I and R denote intensity and range, respectively. raw photon count histograms has been shown to be beneficial when denoising LiDAR data in fog [16].…”

Section: Introductionmentioning

confidence: 99%

4DenoiseNet: Adverse Weather Denoising From Adjacent Point Clouds

Seppänen

Ojala

Tammi

2023

IEEE Robot. Autom. Lett.

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Reliable point cloud data is essential for perception tasks 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.

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“…I and R denote intensity and range, respectively. raw photon count histograms has been shown to be beneficial when denoising LiDAR data in fog [16].…”

Section: Introductionmentioning

confidence: 99%