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The irregularity and permutation invariance of point cloud data posechallenges for effective learning. Conventional methods for addressing thisissue involve converting raw point clouds to intermediate representations suchas 3D voxel grids or range images. While such intermediate representationssolve the problem of permutation invariance, they can result in significantloss of information. Approaches that do learn on raw point clouds either havetrouble in resolving neighborhood relationships between points or are toocomplicated in their formulation. In this paper, we propose a novel approach torepresenting point clouds as a locality preserving 1D ordering induced by theHilbert space-filling curve. We also introduce Point2Point, a neuralarchitecture that can effectively learn on Hilbert-sorted point clouds. We showthat Point2Point shows competitive performance on point cloud segmentation andgeneration tasks. Finally, we show the performance of Point2Point onSpatio-temporal Occupancy prediction from Point clouds.

Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction