English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/166276
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

A Bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images

AuthorsMoreno-Noguer, Francesc ; Porta, Josep M.
KeywordsPose estimation
Bayesian belief networks
SLAM
Deformable surfaces
Issue Date2016
PublisherElsevier
CitationImage and Vision Computing 52: 141-153 (2016)
AbstractIn this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of non-rigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated. An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current non-rigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.
Publisher version (URL)https://doi.org/10.1016/j.imavis.2016.05.012
URIhttp://hdl.handle.net/10261/166276
Identifiersdoi: 10.1016/j.imavis.2016.05.012
issn: 0262-8856
e-issn: 1872-8138
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
abayesianimage.pdf5,49 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.