A Closest Point Proposal for MCMC-based Probabilistic Surface Registration
This work addresses uncertainty in surface registration for shape modeling and reconstruction, offering a method to visualize correspondence uncertainty and estimate missing data distributions, though it appears incremental in its approach.
The paper tackles non-rigid surface registration by framing it as a probabilistic inference problem, introducing a closest-point proposal for Metropolis-Hastings to improve convergence speed over random-walk strategies.
We propose to view non-rigid surface registration as a probabilistic inference problem. Given a target surface, we estimate the posterior distribution of surface registrations. We demonstrate how the posterior distribution can be used to build shape models that generalize better and show how to visualize the uncertainty in the established correspondence. Furthermore, in a reconstruction task, we show how to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence. We introduce the closest-point proposal for the Metropolis-Hastings algorithm. Our proposal overcomes the limitation of slow convergence compared to a random-walk strategy. As the algorithm decouples inference from modeling the posterior using a propose-and-verify scheme, we show how to choose different distance measures for the likelihood model. All presented results are fully reproducible using publicly available data and our open-source implementation of the registration framework.