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

翻译：恶劣天气会对光探测与测距（LiDAR）数据引入噪声，这对其在诸多户外应用（例如目标检测与地图构建）中造成问题。我们提出多回波去噪任务，其目标在于选取代表感兴趣目标的回波并丢弃其他回波。因此，其核心思想是从替代回波中选取那些因噪声而在标准最强回波点云中不可用的点。直观而言，我们试图透过恶劣天气进行感知。为实现这一目标，我们提出了新颖的自监督深度学习方法，并引入特征相似性正则化方法以提升其性能。基于在半合成数据集上的大量实验，我们的方法在自监督恶劣天气去噪任务中相较于当前最优方法实现了23%的显著提升。此外，真实多回波恶劣天气数据集上的实验证明了多回波去噪的有效性。我们的工作使得在恶劣天气下获取更可靠的点云成为可能，从而为保障此类条件下自动驾驶与驾驶辅助系统的安全性提供了前景。代码已开源：https://github.com/alvariseppanen/SMEDNet