  The irregularity and permutation invariance of point cloud data pose
challenges for effective learning. Conventional methods for addressing this
issue involve converting raw point clouds to intermediate representations such
as 3D voxel grids or range images. While such intermediate representations
solve the problem of permutation invariance, they can result in significant
loss of information. Approaches that do learn on raw point clouds either have
trouble in resolving neighborhood relationships between points or are too
complicated in their formulation. In this paper, we propose a novel approach to
representing point clouds as a locality preserving 1D ordering induced by the
Hilbert space-filling curve. We also introduce Point2Point, a neural
architecture that can effectively learn on Hilbert-sorted point clouds. We show
that Point2Point shows competitive performance on point cloud segmentation and
generation tasks. Finally, we show the performance of Point2Point on
Spatio-temporal Occupancy prediction from Point clouds.
