  Lidar based 3D object detection is inevitable for autonomous driving, because
it directly links to environmental understanding and therefore builds the base
for prediction and motion planning. The capacity of inferencing highly sparse
3D data in real-time is an ill-posed problem for lots of other application
areas besides automated vehicles, e.g. augmented reality, personal robotics or
industrial automation. We introduce Complex-YOLO, a state of the art real-time
3D object detection network on point clouds only. In this work, we describe a
network that expands YOLOv2, a fast 2D standard object detector for RGB images,
by a specific complex regression strategy to estimate multi-class 3D boxes in
Cartesian space. Thus, we propose a specific Euler-Region-Proposal Network
(E-RPN) to estimate the pose of the object by adding an imaginary and a real
fraction to the regression network. This ends up in a closed complex space and
avoids singularities, which occur by single angle estimations. The E-RPN
supports to generalize well during training. Our experiments on the KITTI
benchmark suite show that we outperform current leading methods for 3D object
detection specifically in terms of efficiency. We achieve state of the art
results for cars, pedestrians and cyclists by being more than five times faster
than the fastest competitor. Further, our model is capable of estimating all
eight KITTI-classes, including Vans, Trucks or sitting pedestrians
simultaneously with high accuracy.
