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License: CC BY NC ND
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License: CC BY NC ND
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Estimación monocular y eficiente de la pose usando modelos 3D complejos

Authors: Rubio Romano, Antonio; Villamizar Vergel, Michael Alejandro; Ferraz Colomina, Luis; Peñate Sánchez, Adrián; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc;

Estimación monocular y eficiente de la pose usando modelos 3D complejos

Abstract

El siguiente documento presenta un método robusto y eficiente para estimar la pose de una cámara. El método propuesto asume el conocimiento previo de un modelo 3D del entorno, y compara una nueva imagen de entrada únicamente con un conjunto pequeño de imágenes similares seleccionadas previamente por un algoritmo de >Bag of Visual Words>. De esta forma se evita el alto coste computacional de calcular la correspondencia de los puntos 2D de la imagen de entrada contra todos los puntos 3D de un modelo complejo, que en nuestro caso contiene más de 100,000 puntos. La estimación de la pose se lleva a cabo a partir de estas correspondencias 2D-3D utilizando un novedoso algoritmo de PnP que realiza la eliminación de valores atípicos (outliers) sin necesidad de utilizar RANSAC, y que es entre 10 y 100 veces más rápido que los métodos que lo utilizan.

Este trabajo ha estado financiado en parte por los proyectos RobTaskCoop DPI2010-17112, ERA-Net Chistera ViSen PCIN-2013-047, y por el proyecto EU ARCAS FP7-ICT-2011-287617.

Trabajo presentado a las XXXV Jornadas de Automática celebradas en Valencia del 3 al 5 de septiembre de 2014.-- Premio Infaimon a mejor artículo de visión.

Peer Reviewed

Country
Spain
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Keywords

:Informàtica::Automàtica i control [Àrees temàtiques de la UPC], deep learning, PnP, Classificació INSPEC::Control theory, computer vision, pattern classification, pattern matching, :Control theory [Classificació INSPEC], Àrees temàtiques de la UPC::Informàtica::Automàtica i control, complex 3D models, Robust efficient procrustes perspective-n-point, Estimación de la pose, Bag of visual words, efficient matching

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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