  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 state-of-the-art 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.
