Lifting simplices to find injectivity

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

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Mapping a source mesh into a target domain while preserving local injectivity is an important but highly non-trivial task. Existing methods either require an already-injective starting configuration, which is often not available, or rely on sophisticated solving schemes. We propose a novel energy form, called Total Lifted Content (TLC), that is equipped with theoretical properties desirable for injectivity optimization. By lifting the simplices of the mesh into a higher dimension and measuring their contents (2D area or 3D volume) there, TLC is smooth over the entire embedding space and its global minima are always injective. The energy is simple to minimize using standard gradient-based solvers. Our method achieved 100% success rate on an extensive benchmark of embedding problems for triangular and tetrahedral meshes, on which existing methods only have varied success.

Original languageEnglish
Article number120
JournalACM Transactions on Graphics
Volume39
Issue number4
DOIs
StatePublished - Jul 8 2020

Keywords

  • injective embedding
  • parameterization

Fingerprint

Dive into the research topics of 'Lifting simplices to find injectivity'. Together they form a unique fingerprint.

Cite this