Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/117929
Título: Efficient monocular pose estimation for complex 3D models
Autores/as: Rubio, A.
Villamizar, M.
Ferraz, L.
Penate-Sanchez, Adrian 
Ramisa, A.
Simo-Serra, E.
Sanfeliu, A.
Moreno-Noguer, F.
Clasificación UNESCO: 1203 Ciencia de los ordenadores
1206 Análisis numérico
Palabras clave: Three-dimensional displays
Solid modeling
Estimation
Computational modeling
Cameras, et al.
Fecha de publicación: 2015
Publicación seriada: Proceedings - IEEE International Conference on Robotics and Automation 
Conferencia: 2015 IEEE International Conference on Robotics and Automation (ICRA)
Resumen: We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experiments dealing with very large and complex 3D models demonstrate the effectiveness of the approach.
URI: http://hdl.handle.net/10553/117929
ISBN: 978-1-4799-6923-4
ISSN: 1050-4729
DOI: 10.1109/ICRA.2015.7139372
Fuente: IEEE International Conference on Robotics and Automation (ICRA), 15285966, (02 July 2015)
Colección:Actas de congresos
Adobe PDF (3,94 MB)
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.