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Uncertainty Based Camera Model Selection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345624" target="_blank" >RIV/68407700:21230/20:00345624 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/20:00345624

  • Result on the web

    <a href="https://doi.org/10.1109/CVPR42600.2020.00603" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.00603</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR42600.2020.00603" target="_blank" >10.1109/CVPR42600.2020.00603</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Uncertainty Based Camera Model Selection

  • Original language description

    The quality and speed of Structure from Motion (SfM) methods depend significantly on the camera model chosen for the reconstruction. In most of the SfM pipelines, the camera model is manually chosen by the user. In this paper, we present a new automatic method for camera model selection in large scale SfM that is based on efficient uncertainty evaluation. We first perform an extensive comparison of classical model selection based on known Information Criteria and show that they do not provide sufficiently accurate results when applied to camera model selection. Then we propose a new Accuracy-based Criterion, which evaluates an efficient approximation of the uncertainty of the estimated parameters in tested models. Using the new criterion, we design a camera model selection method and fine-tune it by machine learning. Our simulated and real experiments demonstrate a significant increase in reconstruction quality as well as a considerable speedup of the SfM process.

  • 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

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition

  • ISBN

    978-1-7281-7169-2

  • ISSN

    1063-6919

  • e-ISSN

    2575-7075

  • Number of pages

    10

  • Pages from-to

    5990-5999

  • Publisher name

    IEEE Computer Society

  • Place of publication

    USA

  • Event location

    Seattle

  • Event date

    Jun 13, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000620679506027