Realtime 3D Traffic Cone Detection for Autonomous Driving. Considerable progress has been made in semantic scene understanding of road scenes with monocular cameras. It is, however, mainly related to certain classes such as cars and pedestrians. This work investigates traffic cones, an object class crucial for traffic control in the context of autonomous vehicles. 3D object detection using images from a monocular camera is intrinsically an illposed problem. In this work, we leverage the unique structure of traffic cones and propose a pipelined approach to the problem. Specifically, we first detect cones in images by a tailored 2D object detector; then, the spatial arrangement of keypoints on a traffic cone are detected by our deep structural regression network, where the fact that the crossratio is projection invariant is leveraged for network regularization; finally, the 3D position of cones is recovered by the classical Perspective nPoint algorithm. Extensive experiments show that our approach can accurately detect traffic cones and estimate their position in the 3D world in real time. The proposed method is also deployed on a realtime, critical system. It runs efficiently on the lowpower Jetson TX2, providing accurate 3D position estimates, allowing a racecar to map and drive autonomously on an unseen track indicated by traffic cones. With the help of robust and accurate perception, our racecar won both Formula Student Competitions held in Italy and Germany in 2018, cruising at a topspeed of 54 kmph. Visualization of the complete pipeline, mapping and navigation can be found on our project page.

A Geometric Convolutional Neural Network for 3D Object Detection We propose a method for accurate 3D vehicle detection based on geometric deep neural networks. From only a single RGB image, the framework is able to recover the 3D positions and predict 3D bounding boxes. In particular, the algorithm leverages single image depth estimation and semantic segmentation to produce 3D point cloud for specific objects. By geometrically constraining the object dimensions, an accurate and stable 3D bounding box which tightly fits into the real object can be estimated. We verify the effectiveness and robustness of our method by comparing with other recent stateofart methods on the challenging KITTI 3D benchmark dataset as well as synthetic Virtual KITTI dataset. Without requiring ground truth 3D labels, our method is able to produce competitive and robust performance in 3D scene understanding and detection.