  In this paper, we unify popular non-rigid registration methods for point sets
and surfaces under our general framework, GiNGR. GiNGR builds upon Gaussian
Process Morphable Models (GPMM) and hence separates modeling the deformation
prior from model adaptation for registration. In addition, it provides
explainable hyperparameters, multi-resolution registration, trivial inclusion
of expert annotation, and the ability to use and combine analytical and
statistical deformation priors. But more importantly, the reformulation allows
for a direct comparison of registration methods. Instead of using a general
solver in the optimization step, we show how Gaussian process regression (GPR)
iteratively can warp a reference onto a target, leading to smooth deformations
following the prior for any dense, sparse, or partial estimated correspondences
in a principled way. We show how the popular CPD and ICP algorithms can be
directly explained with GiNGR. Furthermore, we show how existing algorithms in
the GiNGR framework can perform probabilistic registration to obtain a
distribution of different registrations instead of a single best registration.
This can be used to analyze the uncertainty e.g. when registering partial
observations. GiNGR is publicly available and fully modular to allow for
domain-specific prior construction.
