Singularity-Free Frame Fields for Line Drawing Vectorization

  • Olga Guţan
  • , Shreya Hegde
  • , Erick Jimenez Berumen
  • , Mikhail Bessmeltsev
  • , Edward Chien

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

State-of-the-art methods for line drawing vectorization rely on generated frame fields for robust direction disambiguation, with each of the two axes aligning to different intersecting curve tangents around junctions. However, a common source of topological error for such methods are frame field singularities. To remedy this, we introduce the first frame field optimization framework guaranteed to produce singularity-free fields aligned to a line drawing. We first perform a convex solve for a roughly-aligned orthogonal frame field (cross field), and then comb away its internal singularities with an optimal transport–based matching. The resulting topology of the field is strictly maintained with the machinery of discrete trivial connections in a final, non-convex optimization that allows non-orthogonality of the field, improving smoothness and tangent alignment. Our frame fields can serve as a drop-in replacement for frame field optimizations used in previous work, improving the quality of the final vectorizations.

Original languageEnglish
Article numbere14901
JournalComputer Graphics Forum
Volume42
Issue number5
DOIs
StatePublished - Aug 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

Funding

This project was initiated during the inaugural Summer Geometry Initiative held in Summer 2021, and we thank the organizers and participants for fostering a dynamic and supportive community. We also would like to acknowledge the following sources of support: Olga Guţan was supported by a Packard Fellowship; Shreya Hegde was supported by the Masason Foundation; Erick Jimenez Berumen was supported by a Questbridge National College Match Scholarship; Mikhail Bessmeltsev was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant No.: RGPIN‐2019‐05097 (“Creating Virtual Shapes via Intuitive Input”), and the NSERC ‐ Fonds de recherche du Québec ‐ Nature et technologies (FRQNT) NOVA Grant No. 314090.

FundersFunder number
Nurses Organization of Veterans Affairs314090
Masason Foundation
Natural Sciences and Engineering Research Council of CanadaRGPIN‐2019‐05097
Fonds de recherche du Québec – Nature et technologies

    Keywords

    • CCS Concepts
    • Shape analysis
    • • Computing methodologies
    • → Parametric curve and surface models

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