Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios. We propose to lift the 2D images to 3D representations using learned neural networks and leverage existing networks working directly on 3D data to perform 3D object detection and localization. We show that, with carefully designed training mechanism and automatically selected minimally noisy data, such a method is not only feasible, but gives higher results than many methods working on actual 3D inputs acquired from physical sensors. On the challenging KITTI benchmark, we show that our 2D to 3D lifted method outperforms many recent competitive 3D networks while significantly outperforming previous stateoftheart for 3D detection from monocular images. We also show that a late fusion of the output of the network trained on generated 3D images, with that trained on real 3D images, improves performance. We find the results very interesting and argue that such a method could serve as a highly reliable backup in case of malfunction of expensive 3D sensors, if not potentially making them redundant, at least in the case of low human injury risk autonomous navigation scenarios like warehouse automation.

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.