Isometric Energies for Recovering Injectivity in Constrained Mapping

Xingyi Du, Danny M. Kaufman, Qingnan Zhou, Shahar Kovalsky, Yajie Yan, Noam Aigerman, Tao Ju

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Computing injective maps with low distortions is a long-standing problem in computer graphics. Such maps are particularly challenging to obtain in the presence of positional constraints, because an injective initial map is often not available. Recently, several energies were proposed and shown to be highly successful in optimizing injectivity from non-injective initial maps while satisfying positional constraints. However, minimizing these energies tends to produce elements with significant isometric distortions. This paper presents simple variants of these energies that retain their desirable traits while promoting isometry. While our method is not guaranteed to provide an injective map, we observe that, on large-scale 2D and 3D data sets, minimizing the proposed isometric variants results in a similar level of success in recovering injectivity as the original energies but a significantly lower isometric distortion.

Original languageEnglish
Title of host publicationProceedings - SIGGRAPH Asia 2022 Conference Papers
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450394703
DOIs
StatePublished - Nov 29 2022
EventSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022 - Daegu, Korea, Republic of
Duration: Dec 6 2022Dec 9 2022

Publication series

NameProceedings - SIGGRAPH Asia 2022 Conference Papers

Conference

ConferenceSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022
Country/TerritoryKorea, Republic of
CityDaegu
Period12/6/2212/9/22

Keywords

  • injective
  • mapping
  • Parameterization

Fingerprint

Dive into the research topics of 'Isometric Energies for Recovering Injectivity in Constrained Mapping'. Together they form a unique fingerprint.

Cite this