We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of lidar in autonomous driving and have proposed processing and learning algorithms that exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe AVs. We also offer perspectives on open issues that are needed to be solved in the future.

中文翻译：

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自动驾驶的 3D 点云处理和学习：影响地图创建、定位和感知

我们回顾了自动驾驶的 3D 点云处理和学习。作为自动驾驶汽车 (AV) 中最重要的传感器之一，激光雷达传感器收集 3D 点云，可精确记录物体和场景的外表面。3D 点云处理和学习工具对于 AV 中的地图创建、定位和感知模块至关重要。尽管从相机收集的数据（例如图像和视频）受到了很多关注，但越来越多的研究人员已经认识到激光雷达在自动驾驶中的重要性和意义，并提出了利用 3D 点云的处理和学习算法。我们回顾了该研究领域的最新进展，并总结了实际和安全的 AV 已经尝试过的内容以及需要什么。