Structured Analytic Coherent Point Drift for Non-Rigid Point Set Registration
For non-rigid point set registration, this work offers a more compact and interpretable alternative to kernel-based methods, though it is an incremental improvement over existing CPD.
Analytic-CPD replaces the kernel-based deformation model in coherent point drift with a truncated multivariate Taylor mapping, reducing parameters and improving convergence. It achieves lower final errors and faster convergence than standard CPD on large-deformation registration tasks.
We introduce Analytic-CPD, a structured analytic variant of coherent point drift for non-rigid point set registration. The method retains the CPD posterior correspondence layer, but replaces the point-indexed Gaussian-kernel displacement-field M-step with a finite-dimensional structured analytic mapping estimator. Posterior probabilities from the Gaussian mixture model are condensed through a barycentric identity into weighted soft target points, converting the CPD pairwise soft-correspondence objective into a weighted analytic fitting problem. The deformation is represented by a truncated multivariate Taylor mapping of a vector-valued function, so the number of deformation parameters is controlled by the ambient dimension and the analytic order rather than by an M-by-M kernel system over the moving points. A degree-continuation strategy is further introduced to stabilize large-deformation registration by progressively activating higher-order analytic modes. Experiments on two-dimensional analytic deformations and three-dimensional smooth non-analytic deformations show that Analytic-CPD achieves lower final errors and faster convergence than standard CPD in representative large-deformation settings. The results suggest that CPD-style probabilistic correspondences and structured analytic mappings provide a compact and interpretable alternative to kernel-based non-rigid registration. Code is available at https://github.com/monge-ampere/Analytic-CPD.