Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting

Figure 1 . We focus on the problem of scene perception and forecasting for autonomous systems. As traditional methods rely on costly human annotations, we look towards emerging self-supervisable and scalable tasks such as point cloud forecasting [11, 18, 19] . However, we argue that the formulation of point cloud forecasting unnecessarily focuses on learning the sensor extrinsics and intrinsics as part of predicting future point clouds, whereas the only physical quantity of central importance to autonomous perception is future spacetime 4D occupancy. We recast the task as that of 4D occupancy forecasting and show how using the same data as point cloud forecasting, one can learn a meaningful and generic intermediate quantity -future spacetime 4D occupancy.