DocumentCode :
2711890
Title :
Progressive shape models
Author :
Letouzey, Antoine ; Boyer, Edmond
Author_Institution :
INRIA Grenoble Rhone-Alpes, St. Ismier, France
fYear :
2012
fDate :
16-21 June 2012
Firstpage :
190
Lastpage :
197
Abstract :
In this paper we address the problem of recovering both the topology and the geometry of a deformable shape using temporal mesh sequences. The interest arises in multi-camera applications when unknown natural dynamic scenes are captured. While several approaches allow recovery of shape models from static scenes, few consider dynamic scenes with evolving topology and without prior knowledge. In this nonetheless generic situation, a single time observation is not necessarily sufficient to infer the correct topology of the observed shape and evidences must be accumulated over time in order to learn the topology and to enable temporally consistent modelling. This appears to be a new problem for which no formal solution exists. We propose a principled approach based on the assumption that the observed objects have a fixed topology. Under this assumption, we can progressively learn the topology meanwhile capturing the deformation of the dynamic scene. The approach has been successfully experimented on several standard 4D datasets.
Keywords :
natural scenes; solid modelling; deformable shape geometry; deformable shape topology; dynamic scene deformation; multicamera applications; natural dynamic scenes; progressive shape models; shape model recovery; standard 4D datasets; static scenes; temporal mesh sequences; Computational modeling; Deformable models; Estimation; Geometry; Shape; Solid modeling; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location :
Providence, RI
ISSN :
1063-6919
Print_ISBN :
978-1-4673-1226-4
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2012.6247675
Filename :
6247675
Link To Document :
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