  In this paper we propose a novel 3D single-shot object detection method for
detecting vehicles in monocular RGB images. Our approach lifts 2D detections to
3D space by predicting additional regression and classification parameters and
hence keeping the runtime close to pure 2D object detection. The additional
parameters are transformed to 3D bounding box keypoints within the network
under geometric constraints. Our proposed method features a full 3D description
including all three angles of rotation without supervision by any labeled
ground truth data for the object's orientation, as it focuses on certain
keypoints within the image plane. While our approach can be combined with any
modern object detection framework with only little computational overhead, we
exemplify the extension of SSD for the prediction of 3D bounding boxes. We test
our approach on different datasets for autonomous driving and evaluate it using
the challenging KITTI 3D Object Detection as well as the novel nuScenes Object
Detection benchmarks. While we achieve competitive results on both benchmarks
we outperform current state-of-the-art methods in terms of speed with more than
20 FPS for all tested datasets and image resolutions.
