  Autonomous driving is the key technology of intelligent logistics in
Industrial Internet of Things (IIoT). In autonomous driving, the appearance of
incomplete point clouds losing geometric and semantic information is inevitable
owing to limitations of occlusion, sensor resolution, and viewing angle when
the Light Detection And Ranging (LiDAR) is applied. The emergence of incomplete
point clouds, especially incomplete vehicle point clouds, would lead to the
reduction of the accuracy of autonomous driving vehicles in object detection,
traffic alert, and collision avoidance. Existing point cloud completion
networks, such as Point Fractal Network (PF-Net), focus on the accuracy of
point cloud completion, without considering the efficiency of inference
process, which makes it difficult for them to be deployed for vehicle point
cloud repair in autonomous driving. To address the above problem, in this
paper, we propose an efficient deep learning approach to repair incomplete
vehicle point cloud accurately and efficiently in autonomous driving. In the
proposed method, an efficient downsampling algorithm combining incremental
sampling and one-time sampling is presented to improves the inference speed of
the PF-Net based on Generative Adversarial Network (GAN). To evaluate the
performance of the proposed method, a real dataset is used, and an autonomous
driving scene is created, where three incomplete vehicle point clouds with 5
different sizes are set for three autonomous driving situations. The improved
PF-Net can achieve the speedups of over 19x with almost the same accuracy when
compared to the original PF-Net. Experimental results demonstrate that the
improved PF-Net can be applied to efficiently complete vehicle point clouds in
autonomous driving.
