Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures

3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning algorithms require correspondence between each point when compar...

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Bibliographic Details
Published inarXiv.org
Main Authors Besnier, Thomas, Arguillère, Sylvain, Pierson, Emery, Daoudi, Mohamed
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 27.06.2023
Subjects
Algorithms
Cognitive tasks
Data acquisition
Deep learning
Finite element method
Machine learning
Mesh generation
Resampling
Three dimensional models
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Summary:3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning algorithms require correspondence between each point when comparing the predicted shape and the target shape. We propose an architecture able to cope with different parameterizations, even during the training phase. In particular, our loss function is built upon a kernel-based metric over a representation of meshes using geometric measures such as currents and varifolds. The latter allows to implement an efficient dissimilarity measure with many desirable properties such as robustness to resampling of the mesh or point cloud. We demonstrate the efficiency and resilience of our model with a generative learning task of human faces.
ISSN:2331-8422