  Autonomous vehicles rely heavily on sensors such as camera and LiDAR, which
provide real-time information about their surroundings for the tasks of
perception, planning and control. Typically a LiDAR can only provide sparse
point cloud owing to a limited number of scanning lines. By employing depth
completion, a dense depth map can be generated by assigning each camera pixel a
corresponding depth value. However, the existing depth completion convolutional
neural networks are very complex that requires high-end GPUs for processing,
and thus they are not applicable to real-time autonomous driving. In this
paper, a light-weight network is proposed for the task of LiDAR point cloud
depth completion. With an astonishing 96.2% reduction in the number of
parameters, it still achieves comparable performance (9.3% better in MAE but
3.9% worse in RMSE) to the state-of-the-art network. For real-time embedded
platforms, depthwise separable technique is applied to both convolution and
deconvolution operations and the number of parameters decreases further by a
factor of 7.3, with only a small percentage increase in RMSE and MAE
performance. Moreover, a system-on-chip architecture for depth completion is
developed on a PYNQ-based FPGA platform that achieves real-time processing for
HDL-64E LiDAR at the speed 11.1 frame per second.
