  Object detection and classification in 3D is a key task in Automated Driving
(AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction
of the surrounding environment, while the task of 3D object bounding box
detection in real time remains a strong algorithmic challenge. In this paper,
we build on the success of the one-shot regression meta-architecture in the 2D
perspective image space and extend it to generate oriented 3D object bounding
boxes from LiDAR point cloud. Our main contribution is in extending the loss
function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian
coordinates and the height of the box as a direct regression problem. This
formulation enables real-time performance, which is essential for automated
driving. Our results are showing promising figures on KITTI benchmark,
achieving real-time performance (40 fps) on Titan X GPU.
