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Lidar based 3D object detection is inevitable for autonomous driving, becauseit directly links to environmental understanding and therefore builds the basefor prediction and motion planning. The capacity of inferencing highly sparse3D data in real-time is an ill-posed problem for lots of other applicationareas besides automated vehicles, e.g. augmented reality, personal robotics orindustrial automation. We introduce Complex-YOLO, a state of the art real-time3D object detection network on point clouds only. In this work, we describe anetwork that expands YOLOv2, a fast 2D standard object detector for RGB images,by a specific complex regression strategy to estimate multi-class 3D boxes inCartesian 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 realfraction to the regression network. This ends up in a closed complex space andavoids singularities, which occur by single angle estimations. The E-RPNsupports to generalize well during training. Our experiments on the KITTIbenchmark suite show that we outperform current leading methods for 3D objectdetection specifically in terms of efficiency. We achieve state of the artresults for cars, pedestrians and cyclists by being more than five times fasterthan the fastest competitor. Further, our model is capable of estimating alleight KITTI-classes, including Vans, Trucks or sitting pedestrianssimultaneously with high accuracy.

Complex-YOLO: Real-time 3D Object Detection on Point Clouds