论文标题
lisnownet：雨点云的实时降雪
LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud
论文作者
论文摘要
激光雷达已被广泛采用现代自动驾驶汽车，提供了现场和周围物体的3D信息。但是，敌人的天气状况仍然对激光雷达构成重大挑战，因为在降雪过程中捕获的点云很容易被损坏。由此产生的嘈杂点云降低了下游任务，例如映射。被雪损坏的灰点云中的现有作品是基于最近的邻居搜索，因此与通常在10Hz处捕获$ 100k $或更多点的现代痛苦相比，无法很好地扩展。在本文中，我们引入了一种无监督的去命名算法，Lisnownet，运行52 $ \ times $比最先进的方法快，同时实现了较高的表现。与以前的方法不同，所提出的算法基于深度卷积神经网络，并且可以轻松地部署到诸如GPU之类的硬件加速器上。此外，我们演示了如何使用所提出的方法来映射即使在损坏的点云中。
LiDARs have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects. However, adverser weather conditions still pose significant challenges to LiDARs since point clouds captured during snowfall can easily be corrupted. The resulting noisy point clouds degrade downstream tasks such as mapping. Existing works in de-noising point clouds corrupted by snow are based on nearest-neighbor search, and thus do not scale well with modern LiDARs which usually capture $100k$ or more points at 10Hz. In this paper, we introduce an unsupervised de-noising algorithm, LiSnowNet, running 52$\times$ faster than the state-of-the-art methods while achieving superior performance in de-noising. Unlike previous methods, the proposed algorithm is based on a deep convolutional neural network and can be easily deployed to hardware accelerators such as GPUs. In addition, we demonstrate how to use the proposed method for mapping even with corrupted point clouds.
