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  4. MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks
 
conference paper

MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks

Guillard, Benoît  
•
Stella, Federico  
•
Fua, Pascal  
2022
Computer Vision – ECCV 2022: 17th European Conference
European Conference on Computer Vision (ECCV 2022)

Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces. However, current approaches to converting them into explicit meshes tend to either be expensive or to degrade the accuracy. Here, we extend the marching cube algorithm to handle UDFs, both fast and accurately. Moreover, our approach to surface extraction is differentiable, which is key to using pretrained UDF networks to fit sparse data.

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Type
conference paper
DOI
10.1007/978-3-031-20062-5_33
Author(s)
Guillard, Benoît  
Stella, Federico  
Fua, Pascal  
Date Issued

2022

Publisher

Springer

Published in
Computer Vision – ECCV 2022: 17th European Conference
Start page

576

End page

592

Subjects

unsigned distance fields

•

UDF

•

isosurface extraction

•

differentiable geometry

•

meshing

•

open surfaces

URL

Project page

https://bguillard.github.io/meshudf/
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CVLAB  
Event nameEvent placeEvent date
European Conference on Computer Vision (ECCV 2022)

Tel-Aviv, Israel

October 23-27, 2022

Available on Infoscience
July 8, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/189129
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