  In this paper we propose to exploit multiple related tasks for accurate
multi-sensor 3D object detection. Towards this goal we present an end-to-end
learnable architecture that reasons about 2D and 3D object detection as well as
ground estimation and depth completion. Our experiments show that all these
tasks are complementary and help the network learn better representations by
fusing information at various levels. Importantly, our approach leads the KITTI
benchmark on 2D, 3D and BEV object detection, while being real time.
