A Closest Point Proposal for MCMC-based Probabilistic Surface Registration

Madsen, Dennis and Morel-Forster, Andreas and Kahr, Patrick and Rahbani, Dana and Vetter, Thomas and Lüthi, Marcel. (2020) A Closest Point Proposal for MCMC-based Probabilistic Surface Registration. In: Computer Vision - ECCV 2020. 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII. Cham, pp. 281-296.

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Official URL: https://edoc.unibas.ch/81299/

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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.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Ehemalige Einheiten Mathematik & Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Vetter, Thomas and Madsen, Dennis and Morel, Andreas and Kahr, Patrick and Rahbani, Dana G and Lüthi, Marcel
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Series Name:Lecture Notes in Computer Science
Issue Number:12362
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:27 Jan 2021 08:45
Deposited On:27 Jan 2021 08:45

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