Search Results for author: Marcel Lüthi

Found 5 papers, 3 papers with code

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

1 code implementation18 Mar 2022 Dennis Madsen, Jonathan Aellen, Andreas Morel-Forster, Thomas Vetter, Marcel Lüthi

Furthermore, we show how existing algorithms in the GiNGR framework can perform probabilistic registration to obtain a distribution of different registrations instead of a single best registration.

GPR regression

Dynamic multi feature-class Gaussian process models

no code implementations8 Dec 2021 Jean-Rassaire Fouefack, Bhushan Borotikar, Marcel Lüthi, Tania S. Douglas, Valérie Burdin, Tinashe E. M. Mutsvangwa

A deformation field-based metric is adapted in the method for modelling shape and intensity feature variation as well as for comparing rigid transformations (pose).

Decision Making Management

A Closest Point Proposal for MCMC-based Probabilistic Surface Registration

2 code implementations ECCV 2020 Dennis Madsen, Andreas Morel-Forster, Patrick Kahr, Dana Rahbani, Thomas Vetter, Marcel Lüthi

Furthermore, in a reconstruction task, we show how to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence.

Morphable Face Models - An Open Framework

2 code implementations25 Sep 2017 Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Lüthi, Sandro Schönborn, Thomas Vetter

Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs).

Face Model Gaussian Processes

Gaussian Process Morphable Models

no code implementations23 Mar 2016 Marcel Lüthi, Christoph Jud, Thomas Gerig, Thomas Vetter

However, while for SSMs the shape variation is restricted to the span of the example data, with GPMMs we can define the shape variation using any Gaussian process.

Gaussian Processes

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