  We present a novel graph-neural-network-based system to effectively represent
large-scale 3D point clouds with the applications to autonomous driving. Many
previous works studied the representations of 3D point clouds based on two
approaches, voxelization, which causes discretization errors and learning,
which is hard to capture huge variations in large-scale scenarios. In this
work, we combine voxelization and learning: we discretize the 3D space into
voxels and propose novel graph inception networks to represent 3D points in
each voxel. This combination makes the system avoid discretization errors and
work for large-scale scenarios. The entire system for large-scale 3D point
clouds acts like the blocked discrete cosine transform for 2D images; we thus
call it the point cloud neural transform (PCT). We further apply the proposed
PCT to represent real-time LiDAR sweeps produced by self-driving cars and the
PCT with graph inception networks significantly outperforms its competitors.
