  Light Detection And Ranging (LiDAR) has been widely used in autonomous
vehicles for perception and localization. However, the cost of a
high-resolution LiDAR is still prohibitively expensive, while its
low-resolution counterpart is much more affordable. Therefore, using
low-resolution LiDAR for autonomous driving is an economically viable solution,
but the point cloud sparsity makes it extremely challenging. In this paper, we
propose a two-stage neural network framework that enables 3D object detection
using a low-resolution LiDAR. Taking input from a low-resolution LiDAR point
cloud and a monocular camera image, a depth completion network is employed to
produce dense point cloud that is subsequently processed by a voxel-based
network for 3D object detection. Evaluated with KITTI dataset for 3D object
detection in Bird-Eye View (BEV), the experimental result shows that the
proposed approach performs significantly better than directly applying the
16-line LiDAR point cloud for object detection. For both easy and moderate
cases, our 3D vehicle detection results are close to those using 64-line
high-resolution LiDARs.
