  We present 4D-Net, a 3D object detection approach, which utilizes 3D Point
Cloud and RGB sensing information, both in time. We are able to incorporate the
4D information by performing a novel dynamic connection learning across various
feature representations and levels of abstraction, as well as by observing
geometric constraints. Our approach outperforms the state-of-the-art and strong
baselines on the Waymo Open Dataset. 4D-Net is better able to use motion cues
and dense image information to detect distant objects more successfully.
