ECCV 2020poster20 citations
A Closest Point Proposal for MCMC-based Probabilistic Surface Registration
Dennis Madsen, Andreas Morel-Forster, Patrick Kahr, Dana Rahbani, Thomas Vetter, Marcel Lüthi
Abstract
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.
BibTeX
@inproceedings{eccv2020_aclosestpointpro,
title = {A Closest Point Proposal for MCMC-based Probabilistic Surface Registration},
author = {Dennis Madsen and Andreas Morel-Forster and Patrick Kahr and Dana Rahbani and Thomas Vetter and Marcel Lüthi},
booktitle = {ECCV 2020},
year = {2020}
}