Multi-Task Multi-Sensor Fusion for 3D Object Detection
This paper addresses the problem of improving 3D object detection accuracy for autonomous driving systems by leveraging multiple complementary tasks.
This paper tackles accurate multi-sensor 3D object detection by proposing an end-to-end learnable architecture that jointly reasons about 2D and 3D object detection, ground estimation, and depth completion. The approach leads the KITTI benchmark on 2D, 3D, and BEV object detection.
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.