Predicting the future is a crucial first step to effective control. In this work, we study the problem of future prediction of 3D scenes, represented by point clouds captured by a LiDAR sensor, i.e., directly forecasting the evolution of >100,000 points that comprise a complete scene. We term this Sequential Pointcloud Forecasting (SPF). By directly predicting the densest-possible 3D representation of the future, the output contains richer information than output of prior forecasting tasks such as future object trajectories. We design a method, SPFNet, evaluate it on the KITTI and nuScenes datasets, and find that it demonstrates excellent performance on the SPF task. Our project website is at http://www.xinshuoweng.com/projects/SPF2.