Largest Silhouette-Equivalent Volume for 3D Shapes Modeling without Ghost Object - Archive ouverte HAL Access content directly
Conference Papers Year : 2008

Largest Silhouette-Equivalent Volume for 3D Shapes Modeling without Ghost Object

(1) , (1) , (1) , (1)
1

Abstract

In this paper, we investigate a practical framework to compute a 3D shape estimation of multiple objects in real-time from silhouettes in multi-view environments. A popular method called Shape From Silhouette (SFS), computes a 3D shape estimation from binary silhouette masks. This method has several limitations: The acquisition space is limited to the intersection of the camera viewing frusta ; SFS methods reconstruct some ghost objects which do not contain real objects, especially when there are multiple real objects in the scene. In this paper we propose two contributions to overcome these limitations. First, using a new formulation of SFS approach, our system reconstructs objects with no constraints on camera placement and their visibility. Second, a new theoretical approach identifies and removes ghost objects. The reconstructed shapes are more accurate than current silhouette-based approaches. Reconstructed parts are guaranteed to contain real objects. Finally, we present a real-time system that captures multiple and complex objects moving through many camera frusta to demonstrate the application and robustness of our method.
Fichier principal
Vignette du fichier
1569139892.pdf (465.26 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00326774 , version 1 (05-10-2008)

Identifiers

  • HAL Id : inria-00326774 , version 1

Cite

Brice Michoud, Saïda Bouakaz, Erwan Guillou, Hector Briceño. Largest Silhouette-Equivalent Volume for 3D Shapes Modeling without Ghost Object. Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Andrea Cavallaro and Hamid Aghajan, Oct 2008, Marseille, France. ⟨inria-00326774⟩
192 View
115 Download

Share

Gmail Facebook Twitter LinkedIn More