  This paper describes an optimized single-stage deep convolutional neural
network to detect objects in urban environments, using nothing more than point
cloud data. This feature enables our method to work regardless the time of the
day and the lighting conditions.The proposed network structure employs dilated
convolutions to gradually increase the perceptive field as depth increases,
this helps to reduce the computation time by about 30%. The network input
consists of five perspective representations of the unorganized point cloud
data. The network outputs an objectness map and the bounding box offset values
for each point. Our experiments showed that using reflection, range, and the
position on each of the three axes helped to improve the location and
orientation of the output bounding box. We carried out quantitative evaluations
with the help of the KITTI dataset evaluation server. It achieved the fastest
processing speed among the other contenders, making it suitable for real-time
applications. We implemented and tested it on a real vehicle with a Velodyne
HDL-64 mounted on top of it. We achieved execution times as fast as 50 FPS
using desktop GPUs, and up to 10 FPS on a single Intel Core i5 CPU. The deploy
implementation is open-sourced and it can be found as a feature branch inside
the autonomous driving framework Autoware. Code is available at:
https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection
