Abstract
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 this https URL
Abstract (translated)
恶劣的天气可能导致光检测和范围(LiDAR)数据的噪声。这个问题因为许多户外应用，例如物体检测和地图制作，都使用LiDAR数据。我们提出了任务多回声去噪，其目标是选择代表感兴趣的物体的回声并删除其他回声。因此，我们的目标是从其他回声中选择点，由于噪声原因，这些点在标准最强的回声点云中不存在。从直觉上看，我们试图穿过恶劣的天气。为了实现这个目标，我们提出了一种全新的自监督深度学习方法，并使用特征相似性 Regularization方法来提高其性能。基于对半合成数据集的广泛实验，我们的方法和自监督恶劣的天气去噪的当前最佳方法相比取得了更好的性能(23%改进)。此外，与真实的多回声恶劣的天气数据集的实验证明了多回声去噪的有效性。我们的方法在恶劣的天气下提供更可靠的点云获取，因此在这样的条件下，可以承诺更安全的自主驾驶和驾驶辅助系统。代码在此https URL上可用。
URL
https://arxiv.org/abs/2305.14008
