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Computer Graphics Forum
Article . 2022 . Peer-reviewed
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Article . 2023
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Article . 2021
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Article . 2023 . Peer-reviewed
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Mesh Draping: Parametrization‐Free Neural Mesh Transfer

Authors: Hertz, A.; Perel, O.; Giryes, R.; Sorkine‐Hornung, O.; Cohen‐Or, D.;

Mesh Draping: Parametrization‐Free Neural Mesh Transfer

Abstract

AbstractDespite recent advances in geometric modelling, 3D mesh modelling still involves a considerable amount of manual labour by experts. In this paper, we introduce Mesh Draping: a neural method for transferring existing mesh structure from one shape to another. The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh. At its core, our method deforms the source mesh using progressive positional encoding (PE). We show that by leveraging gradually increasing frequencies to guide the neural optimization, we are able to achieve stable and high‐quality mesh transfer. Our approach is simple and requires little user guidance, compared to contemporary surface mapping techniques which rely on parametrization or careful manual tuning. Most importantly, Mesh Draping is a parameterization‐free method, and thus applicable to a variety of target shape representations, including point clouds, polygon soups and non‐manifold meshes. We demonstrate that the transferred meshing remains faithful to the source mesh design characteristics, and at the same time fits the target geometry well.

Country
Switzerland
Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Graphics, shape modelling, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, shape synthesis, shape synthesis; shape modelling; neural networks, neural networks, Graphics (cs.GR), Machine Learning (cs.LG)

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    4
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
Green
hybrid