Self-supervised multi-echo point cloud denoising in snowfall
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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7
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Pattern Recognition Letters, Volume 185, pp. 52-58
Abstract
Snowfall can cause noise to light detection and ranging (LiDAR) data. This is a problem since it is used in many outdoor applications, e.g., autonomous driving. 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 unavailable in standard strongest echo point clouds. Intuitively, we are trying to see through the snowfall. We propose a novel self-supervised deep learning method and the characteristics similarity regularization to achieve this goal. The characteristics similarity regularization utilizes noise characteristics to increase performance. The experiments with a real-world multi-echo snowfall dataset prove the efficacy of multi-echo denoising and superior performance to the baseline. Moreover, based on extensive experiments on a semi-synthetic dataset, our method achieves superior performance compared to the state-of-the-art in self-supervised snowfall denoising. Our work enables more reliable point cloud acquisition in snowfall. The code is available at https://github.com/alvariseppanen/SMEDen.Description
Publisher Copyright: © 2024 The Author(s)
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Seppänen, A, Ojala, R & Tammi, K 2024, 'Self-supervised multi-echo point cloud denoising in snowfall', Pattern Recognition Letters, vol. 185, pp. 52-58. https://doi.org/10.1016/j.patrec.2024.07.007