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We introduce a supervised-learning framework for non-rigid point setalignment of a new kind - Displacements on Voxels Networks (DispVoxNets) -which abstracts away from the point set representation and regresses 3Ddisplacement fields on regularly sampled proxy 3D voxel grids. Thanks torecently released collections of deformable objects with known intra-statecorrespondences, DispVoxNets learn a deformation model and further priors(e.g., weak point topology preservation) for different object categories suchas cloths, human bodies and faces. DispVoxNets cope with large deformations,noise and clustered outliers more robustly than the state-of-the-art. At testtime, our approach runs orders of magnitude faster than previous techniques.All properties of DispVoxNets are ascertained numerically and qualitatively inextensive experiments and comparisons to several previous methods.

DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies