Point clouds from 3-D light detection and ranging (LiDAR) are useful for roadside units (RSU) applications in intelligent transportation system (ITS). High-density LiDAR products are restricted by high cost while the low-density and cheap ones are usually insufficient to perceive. Event-based cameras react to the changes in light intensity and output dense event streams consisting of triggered pixels. Unfortunately, there currently lacks depth information for event cameras. In order to address these problems, this article presents an approach to enhance sparse 3-D LiDAR point clouds with event pixels from an event-based camera. In our approach, the depth values of event pixels are estimated based on the distribution models which they belong to. The distribution models of event pixels are determined by the spatial information of the neighboring LiDAR points in a structural manner which we called the physical structure. To verify our approach, we conduct several real-world experiments about RSU applications in ITS. Results demonstrate that our approach can effectively improve 3-D point clouds density. The average accuracy of 3-D and 2-D vehicle detection increase by a factor of 14.6 and 8.8, respectively.

中文翻译：

---

使用基于事件的相机增强 3D LiDAR 点云


来自 3D 光探测和测距 (LiDAR) 的点云对于智能交通系统 (ITS) 中的路边单元 (RSU) 应用非常有用。高密度的激光雷达产品受到成本高的限制，而低密度、廉价的产品通常感知不足。基于事件的相机对光强度的变化做出反应，并输出由触发像素组成的密集事件流。不幸的是，目前缺乏事件摄像机的深度信息。为了解决这些问题，本文提出了一种利用基于事件的相机的事件像素增强稀疏 3D LiDAR 点云的方法。在我们的方法中，事件像素的深度值是根据它们所属的分布模型来估计的。事件像素的分布模型是由相邻激光雷达点的空间信息以结构方式确定的，我们称之为物理结构。为了验证我们的方法，我们对 RSU 在 ITS 中的应用进行了几次实际实验。结果表明，我们的方法可以有效提高 3D 点云密度。 3D 和 2D 车辆检测的平均准确度分别提高了 14.6 倍和 8.8 倍。