Progressive Shape Models

Antoine Letouzey 1, * Edmond Boyer 1
* Corresponding author
1 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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 enough to infer the correct topology of the observed shape and evidences must be accumulated over time in order to learn this 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 and we believe that it paves the way to more general multi-view scene capture and analysis.
Document type :
Conference papers
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download


https://hal.inria.fr/hal-00677506
Contributor : Antoine Letouzey <>
Submitted on : Friday, January 8, 2016 - 10:49:35 AM
Last modification on : Wednesday, April 11, 2018 - 1:59:47 AM
Long-term archiving on : Friday, April 15, 2016 - 5:40:24 PM

Files

Licence


Distributed under a Creative Commons Attribution - NonCommercial - ShareAlike 4.0 International License

Identifiers

Collections

Citation

Antoine Letouzey, Edmond Boyer. Progressive Shape Models. CVPR - Computer Vision and Patern Recognition - 2012, Jun 2012, Providence, United States. pp.190-197, ⟨10.1109/CVPR.2012.6247675⟩. ⟨hal-00677506⟩

Share

Metrics

Record views

570

Files downloads

882