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Optimizing Elimination Templates by Greedy Parameter Search

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364414" target="_blank" >RIV/68407700:21730/22:00364414 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/CVPR52688.2022.01530" target="_blank" >https://doi.org/10.1109/CVPR52688.2022.01530</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR52688.2022.01530" target="_blank" >10.1109/CVPR52688.2022.01530</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Optimizing Elimination Templates by Greedy Parameter Search

  • Original language description

    We propose a new method for constructing elimination templates for efficient polynomial system solving of minimal problems in structure from motion, image matching, and camera tracking. We first construct a particular affine parameterization of the elimination templates for systems with a finite number of distinct solutions. Then, we use a heuristic greedy optimization strategy over the space of parameters to get a template with a small size. We test our method on 34 minimal problems in computer vision. For all of them, we found the templates either of the same or smaller size compared to the state-of-the-art. For some difficult examples, our templates are, e.g., 2.1, 2.5, 3.8, 6.6 times smaller. For the problem of refractive absolute pose estimation with unknown focal length, we have found a template that is 20 times smaller. Our experiments on synthetic data also show that the new solvers are fast and numerically accurate. We also present a fast and numerically accurate solver for the problem of relative pose estimation with unknown common focal length and radial distortion.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceeding 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  • ISBN

    978-1-6654-6946-3

  • ISSN

    1063-6919

  • e-ISSN

    2575-7075

  • Number of pages

    11

  • Pages from-to

    15733-15743

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    New Orleans, Louisiana

  • Event date

    Jun 19, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000870783001053