Abstract
We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons and surfaces, partial and complete scans, etc. The architecture of the P2P-NET is that of a bi-directional point displacement network, which transforms a source point set to a prediction of the target point set with the same cardinality, and vice versa, by applying point-wise displacement vectors learned from data. P2P-NET is trained on paired shapes from the source and target domains, but without relying on point-to-point correspondences between the source and target point sets. The training loss combines two uni-directional geometric losses, each enforcing a shape-wise similarity between the predicted and the target point sets, and a cross-regularization term to encourage consistency between displacement vectors going in opposite directions. We develop and present several different applications enabled by our general-purpose bidirectional P2P-NET to highlight the effectiveness, versatility, and potential of our network in solving a variety of point-based shape transformation problems.
Original language | English |
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Article number | 152 |
Journal | ACM Transactions on Graphics |
Volume | 37 |
Issue number | 4 |
DOIs | |
State | Published - 2018 |
Keywords
- Deep neural network
- Point cloud processing
- Point set transform
- Point-wise displacement
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design