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We present a novel graph-neural-network-based system to effectively representlarge-scale 3D point clouds with the applications to autonomous driving. Manyprevious works studied the representations of 3D point clouds based on twoapproaches, voxelization, which causes discretization errors and learning,which is hard to capture huge variations in large-scale scenarios. In thiswork, we combine voxelization and learning: we discretize the 3D space intovoxels and propose novel graph inception networks to represent 3D points ineach voxel. This combination makes the system avoid discretization errors andwork for large-scale scenarios. The entire system for large-scale 3D pointclouds acts like the blocked discrete cosine transform for 2D images; we thuscall it the point cloud neural transform (PCT). We further apply the proposedPCT to represent real-time LiDAR sweeps produced by self-driving cars and thePCT with graph inception networks significantly outperforms its competitors.

Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving