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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
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e-ISSN
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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