Fusion of multiview silhouette cues using a space occupancy grid

In this paper, we investigate what can be inferred from several silhouette probability maps, in multiview silhouette cue fusion. To this aim, we propose a new framework for multiview silhouette cue fusion. This framework work uses a space occupancy grid as a probabilistic 3D representation of scene contents. Such a representation is of great interest for various computer vision applications in perception, or localization for instance. Our main contribution is to introduce the occupancy grid concept, popular in the robotics, for multicamera environments. The idea is to consider each camera pixel as a statistical occupancy sensor. All pixel observations are then used jointly to infer where, and how likely, matter is present in the scene. As our results illustrate, this sample model has various advantages. Most sources of uncertainty are explicitly modeled, and no premature decisions about pixel labeling occur, thus preserving pixel knowledge. Consequently, optimal scene object localization, and robust volume reconstruction, can achieved, with no constraint on camera placement and object visibility. In addition, this representation allows to improve silhouette extraction in images

[1]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  Sebastian Thrun,et al.  Learning to Locate an Object in 3D Space from a Sequence of Camera Images , 1998, ICML.

[3]  R. Cipolla,et al.  A probabilistic framework for space carving , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Stan Sclaroff,et al.  Stochastic refinement of the visual hull to satisfy photometric and silhouette consistency constraints , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[5]  R. Zabih,et al.  Exact voxel occupancy with graph cuts , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  A. Laurentini,et al.  The Visual Hull Concept for Silhouette-Based Image Understanding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Christophe Coué.  Modèle bayésien pour l'analyse multimodale d'environnements dynamiques et encombrés : application à l'assistance à la conduite en milieu urbain , 2003 .

[8]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[9]  Trevor Darrell,et al.  A Bayesian approach to image-based visual hull reconstruction , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Wojciech Matusik,et al.  Polyhedral Visual Hulls for Real-Time Rendering , 2001, Rendering Techniques.

[11]  Long Quan,et al.  Silhouette Extraction from Multiple Images of Unknown Background , 2004 .

[12]  Marcus A. Magnor,et al.  Joint 3D-reconstruction and background separation in multiple views using graph cuts , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[13]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.

[14]  Edmond Boyer,et al.  Exact polyhedral visual hulls , 2003, BMVC.

[15]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .

[16]  Ramesh Raskar,et al.  Image-based visual hulls , 2000, SIGGRAPH.

[17]  Richard Szeliski,et al.  Rapid octree construction from image sequences , 1993 .

[18]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  I. M. Anonymous,et al.  Joint 3 D-Reconstruction and Background Separation in Multiple Views using Graph Cuts Paper , .

[20]  Olivier D. Faugeras,et al.  Complete Dense Stereovision Using Level Set Methods , 1998, ECCV.

[21]  Paul A. Viola,et al.  Roxels: responsibility weighted 3D volume reconstruction , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  Takeo Kanade,et al.  A real time system for robust 3D voxel reconstruction of human motions , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).