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patent

A method and system for enforcing smoothness constraints on surface meshes from a graph convolutional neural network

Wickramasinghe, Pamuditha Udaranga  
•
Fua, Pascal  
2023

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimensional object or a three-dimensional image stack of the three-dimensional object, performing a GCNN mesh deformation step on the image data to obtain an approximation of a surface of the three-dimensional object, the surface represented by triangulated surface meshes, at least some vertices of the triangulated surface meshes having a different number of neighboring vertices compared to other vertices in a same triangulated surface mesh, and performing a deep active surface model (DASM) transformation step on the triangulated surface meshes to obtain a corrected representation of the surface of three-dimensional object to improve smoothness of the surface.

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Type
patent
EPO Family ID

86500450

Author(s)
Wickramasinghe, Pamuditha Udaranga  
Fua, Pascal  
TTO classification

TTO:6.2225

EPFL units
AVP-R-TTO  
DOICountry codeKind codeDate issued

US2023169732

US

A1

2023-06-01

Available on Infoscience
June 5, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/198141
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