Multi-Echo Denoising in Adverse Weather
Alvari Seppänen Risto Ojala Kari Tammi
Adverse weather can cause noise to light detection and ranging (LiDAR) data.
This is a problem since it is used in many outdoor applications, e.g. object
detection and mapping. We propose the task of multi-echo denoising, where the
goal is to pick the echo that represents the objects of interest and discard
other echoes. Thus, the idea is to pick points from alternative echoes that are
not available in standard strongest echo point clouds due to the noise. In an
intuitive sense, we are trying to see through the adverse weather. To achieve
this goal, we propose a novel self-supervised deep learning method and the
characteristics similarity regularization method to boost its performance.
Based on extensive experiments on a semi-synthetic dataset, our method achieves
superior performance compared to the state-of-the-art in self-supervised
adverse weather denoising (23% improvement). Moreover, the experiments with a
real multi-echo adverse weather dataset prove the efficacy of multi-echo
denoising. Our work enables more reliable point cloud acquisition in adverse
weather and thus promises safer autonomous driving and driving assistance
systems in such conditions. The code is available at
https://github.com/alvariseppanen/SMEDNet