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We introduce P2P-NET, a general-purpose deep neural network which learnsgeometric transformations between point-based shape representations from twodomains, e.g., meso-skeletons and surfaces, partial and complete scans, etc.The architecture of the P2P-NET is that of a bi-directional point displacementnetwork, which transforms a source point set to a target point set with thesame cardinality, and vice versa, by applying point-wise displacement vectorslearned from data. P2P-NET is trained on paired shapes from the source andtarget domains, but without relying on point-to-point correspondences betweenthe source and target point sets. The training loss combines twouni-directional geometric losses, each enforcing a shape-wise similaritybetween the predicted and the target point sets, and a cross-regularizationterm to encourage consistency between displacement vectors going in oppositedirections. We develop and present several different applications enabled byour general-purpose bidirectional P2P-NET to highlight the effectiveness,versatility, and potential of our network in solving a variety of point-basedshape transformation problems.

P2P-NET: Bidirectional Point Displacement Net for Shape Transform