IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i7p993-d1364671.html
   My bibliography  Save this article

Convex Quadratic Programming for Computing Geodesic Distances on Triangle Meshes

Author

Listed:
  • Shuangmin Chen

    (Qingdao University of Science and Technology, Qingdao 260061, China)

  • Nailei Hei

    (Fudan University, Shanghai 200437, China)

  • Shun Hu

    (Qingdao University of Science and Technology, Qingdao 260061, China)

  • Zijia Yue

    (Qingdao University of Science and Technology, Qingdao 260061, China)

  • Ying He

    (Nanyang Technologtical University, Singapore 639798, Singapore)

Abstract

Querying the geodesic distance field on a given smooth surface is a fundamental research pursuit in computer graphics. Both accuracy and smoothness serve as common indicators for evaluating geodesic algorithms. In this study, we argue that ensuring that the norm of the triangle-wise estimated gradients is not larger than 1 is preferable compared to the widely used eikonal condition. Inspired by this, we formulate the geodesic distance field problem as a Quadratically Constrained Linear Programming (QCLP) problem. This formulation can be further adapted into a Quadratically Constrained Quadratic Programming (QCQP) problem by incorporating considerations for smoothness requirements. Specifically, when enforcing a Hessian-energy-based smoothing term, our formulation, named QCQP-Hessian, effectively mitigates the cusps in the geodesic isolines within the near-ridge area while maintaining accuracy in the off-ridge area. We conducted extensive experiments to demonstrate the accuracy and smoothness advantages of QCQP-Hessian.

Suggested Citation

  • Shuangmin Chen & Nailei Hei & Shun Hu & Zijia Yue & Ying He, 2024. "Convex Quadratic Programming for Computing Geodesic Distances on Triangle Meshes," Mathematics, MDPI, vol. 12(7), pages 1-19, March.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:993-:d:1364671
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/7/993/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/7/993/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Mejia-Parra & Jairo R. Sánchez & Jorge Posada & Oscar Ruiz-Salguero & Carlos Cadavid, 2019. "Quasi-Isometric Mesh Parameterization Using Heat-Based Geodesics and Poisson Surface Fills," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:993-:d:1364671. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.