Enhancing 3-D LiDAR Point Clouds With Event-Based Camera

Boyang Li, Hao Meng, Yuzhang Zhu, Rihui Song, Mingyue Cui, Gang Chen, Kai Huang

Abstract: 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.