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

GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression