Predicting how the world can evolve in the future is crucial for motion
planning in autonomous systems. Classical methods are limited because they rely
on costly human annotations in the form of semantic class labels, bounding
boxes, and tracks or HD maps of cities to plan their motion and thus are
difficult to scale to large unlabeled datasets. One promising self-supervised
task is 3D point cloud forecasting from unannotated LiDAR sequences. We show
that this task requires algorithms to implicitly capture (1) sensor extrinsics
(i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e.,
the sampling pattern specific to the particular LiDAR sensor), and (3) the
shape and motion of other objects in the scene. But autonomous systems should
make predictions about the world and not their sensors. To this end, we factor
out (1) and (2) by recasting the task as one of spacetime (4D) occupancy
forecasting. But because it is expensive to obtain ground-truth 4D occupancy,
we render point cloud data from 4D occupancy predictions given sensor
extrinsics and intrinsics, allowing one to train and test occupancy algorithms
with unannotated LiDAR sequences. This also allows one to evaluate and compare
point cloud forecasting algorithms across diverse datasets, sensors, and
vehicles.

通过将 3D 点云预测任务从非注释 LiDAR 序列转变为时空 (4D) 占据预测任务，我们可以评估和比较各种数据集、传感器和车辆的点云预测算法。