Learning to approximate directional fields defined over 2D planes

Maria Taktasheva, Albert Matveev, Alexey Artemov, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions. A common approach to the construction of directional fields from data relies on complex optimization procedures, which are usually poorly formalizable, require a considerable computational effort, and do not transfer across applications. In this work, we propose a deep learning-based approach and study the expressive power and generalization ability.

    Original languageEnglish
    Title of host publicationAnalysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers
    EditorsWil M.P. van der Aalst, Vladimir Batagelj, Dmitry I. Ignatov, Valentina Kuskova, Sergei O. Kuznetsov, Irina A. Lomazova, Michael Khachay, Andrey Kutuzov, Natalia Loukachevitch, Amedeo Napoli, Panos M. Pardalos, Marcello Pelillo, Andrey V. Savchenko, Elena Tutubalina
    PublisherSpringer
    Pages367-374
    Number of pages8
    ISBN (Print)9783030373337
    DOIs
    Publication statusPublished - 2019
    Event8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019 - Kazan, Russian Federation
    Duration: 17 Jul 201919 Jul 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11832 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019
    Country/TerritoryRussian Federation
    CityKazan
    Period17/07/1919/07/19

    Keywords

    • Directional fields
    • Image vectorization
    • Neural networks

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