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Camera Orientation Estimation in Natural Scenes Using Semantic Cues

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130783" target="_blank" >RIV/00216305:26230/18:PU130783 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11829" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11829</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/3DV.2018.00033" target="_blank" >10.1109/3DV.2018.00033</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Camera Orientation Estimation in Natural Scenes Using Semantic Cues

  • Original language description

    Camera orientation estimation in natural scenes has recently been approached by several methods, which rely mainly on matching a single modality - edges or horizon lines with 3D digital elevation models. In contrast to previous works, our new image to model matching scheme is based on a fusion of multiple modalities and is designed to be naturally extensible with different cues. In this paper, we use semantic segments and edges. To our knowledge, we are the first to consider using semantic segments jointly with edges for alignment with digital elevation model. We show that high-level features, such as semantic segments, complement the low-level edge information and together help to estimate the camera orientation more robustly compared to methods relying solely on edges or horizon lines. In a series of experiments, we show that segment boundaries tend to be imprecise and important information for matching is encoded in the segment area and a coarse shape. Intuitively, semantic segments encode low frequency information as opposed to edges, which encode high frequencies. Our experiments exhibit that semantic segments and edges are complementary, improving camera orientation estimation reliability when used together. We demonstrate that our method combining semantic and edge features is able to reach state-of-the-art performance on three datasets.

  • 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/TE01020415" target="_blank" >TE01020415: V3C - Visual Computing Competence Center</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2018 International Conference on 3D Vision

  • ISBN

    978-1-5386-2610-8

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    208-217

  • Publisher name

    IEEE Computer Society

  • Place of publication

    Verona

  • Event location

    Verona

  • Event date

    Sep 5, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000449774200022